Optimization of the alpha image reconstruction. An iterative CT-image reconstruction with well-defined image quality metrics

Energy Technology Data Exchange (ETDEWEB)

Lebedev, Sergej; Sawall, Stefan; Knaup, Michael; Kachelriess, Marc [German Cancer Research Center, Heidelberg (Germany).

2017-10-01

Optimization of the AIR-algorithm for improved convergence and performance. TThe AIR method is an iterative algorithm for CT image reconstruction. As a result of its linearity with respect to the basis images, the AIR algorithm possesses well defined, regular image quality metrics, e.g. point spread function (PSF) or modulation transfer function (MTF), unlike other iterative reconstruction algorithms. The AIR algorithm computes weighting images α to blend between a set of basis images that preferably have mutually exclusive properties, e.g. high spatial resolution or low noise. The optimized algorithm uses an approach that alternates between the optimization of rawdata fidelity using an OSSART like update and regularization using gradient descent, as opposed to the initially proposed AIR using a straightforward gradient descent implementation. A regularization strength for a given task is chosen by formulating a requirement for the noise reduction and checking whether it is fulfilled for different regularization strengths, while monitoring the spatial resolution using the voxel-wise defined modulation transfer function for the AIR image. The optimized algorithm computes similar images in a shorter time compared to the initial gradient descent implementation of AIR. The result can be influenced by multiple parameters that can be narrowed down to a relatively simple framework to compute high quality images. The AIR images, for instance, can have at least a 50% lower noise level compared to the sharpest basis image, while the spatial resolution is mostly maintained. The optimization improves performance by a factor of 6, while maintaining image quality. Furthermore, it was demonstrated that the spatial resolution for AIR can be determined using regular image quality metrics, given smooth weighting images. This is not possible for other iterative reconstructions as a result of their non linearity. A simple set of parameters for the algorithm is discussed that provides

Optimization of the alpha image reconstruction. An iterative CT-image reconstruction with well-defined image quality metrics

International Nuclear Information System (INIS)

Lebedev, Sergej; Sawall, Stefan; Knaup, Michael; Kachelriess, Marc

2017-01-01

Optimization of the AIR-algorithm for improved convergence and performance. TThe AIR method is an iterative algorithm for CT image reconstruction. As a result of its linearity with respect to the basis images, the AIR algorithm possesses well defined, regular image quality metrics, e.g. point spread function (PSF) or modulation transfer function (MTF), unlike other iterative reconstruction algorithms. The AIR algorithm computes weighting images α to blend between a set of basis images that preferably have mutually exclusive properties, e.g. high spatial resolution or low noise. The optimized algorithm uses an approach that alternates between the optimization of rawdata fidelity using an OSSART like update and regularization using gradient descent, as opposed to the initially proposed AIR using a straightforward gradient descent implementation. A regularization strength for a given task is chosen by formulating a requirement for the noise reduction and checking whether it is fulfilled for different regularization strengths, while monitoring the spatial resolution using the voxel-wise defined modulation transfer function for the AIR image. The optimized algorithm computes similar images in a shorter time compared to the initial gradient descent implementation of AIR. The result can be influenced by multiple parameters that can be narrowed down to a relatively simple framework to compute high quality images. The AIR images, for instance, can have at least a 50% lower noise level compared to the sharpest basis image, while the spatial resolution is mostly maintained. The optimization improves performance by a factor of 6, while maintaining image quality. Furthermore, it was demonstrated that the spatial resolution for AIR can be determined using regular image quality metrics, given smooth weighting images. This is not possible for other iterative reconstructions as a result of their non linearity. A simple set of parameters for the algorithm is discussed that provides

Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction.

Science.gov (United States)

Fahimian, Benjamin P; Zhao, Yunzhe; Huang, Zhifeng; Fung, Russell; Mao, Yu; Zhu, Chun; Khatonabadi, Maryam; DeMarco, John J; Osher, Stanley J; McNitt-Gray, Michael F; Miao, Jianwei

2013-03-01

A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest scanner flux setting of 39 m

Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction

International Nuclear Information System (INIS)

Fahimian, Benjamin P.; Zhao Yunzhe; Huang Zhifeng; Fung, Russell; Zhu Chun; Miao Jianwei; Mao Yu; Khatonabadi, Maryam; DeMarco, John J.; McNitt-Gray, Michael F.; Osher, Stanley J.

2013-01-01

Purpose: A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. Methods: EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Results: Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest

An algebraic iterative reconstruction technique for differential X-ray phase-contrast computed tomography.

Science.gov (United States)

Fu, Jian; Schleede, Simone; Tan, Renbo; Chen, Liyuan; Bech, Martin; Achterhold, Klaus; Gifford, Martin; Loewen, Rod; Ruth, Ronald; Pfeiffer, Franz

2013-09-01

Iterative reconstruction has a wide spectrum of proven advantages in the field of conventional X-ray absorption-based computed tomography (CT). In this paper, we report on an algebraic iterative reconstruction technique for grating-based differential phase-contrast CT (DPC-CT). Due to the differential nature of DPC-CT projections, a differential operator and a smoothing operator are added to the iterative reconstruction, compared to the one commonly used for absorption-based CT data. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured at a two-grating interferometer setup. Since the algorithm is easy to implement and allows for the extension to various regularization possibilities, we expect a significant impact of the method for improving future medical and industrial DPC-CT applications. Copyright © 2012. Published by Elsevier GmbH.

Iterative regularization in intensity-modulated radiation therapy optimization

International Nuclear Information System (INIS)

Carlsson, Fredrik; Forsgren, Anders

2006-01-01

A common way to solve intensity-modulated radiation therapy (IMRT) optimization problems is to use a beamlet-based approach. The approach is usually employed in a three-step manner: first a beamlet-weight optimization problem is solved, then the fluence profiles are converted into step-and-shoot segments, and finally postoptimization of the segment weights is performed. A drawback of beamlet-based approaches is that beamlet-weight optimization problems are ill-conditioned and have to be regularized in order to produce smooth fluence profiles that are suitable for conversion. The purpose of this paper is twofold: first, to explain the suitability of solving beamlet-based IMRT problems by a BFGS quasi-Newton sequential quadratic programming method with diagonal initial Hessian estimate, and second, to empirically show that beamlet-weight optimization problems should be solved in relatively few iterations when using this optimization method. The explanation of the suitability is based on viewing the optimization method as an iterative regularization method. In iterative regularization, the optimization problem is solved approximately by iterating long enough to obtain a solution close to the optimal one, but terminating before too much noise occurs. Iterative regularization requires an optimization method that initially proceeds in smooth directions and makes rapid initial progress. Solving ten beamlet-based IMRT problems with dose-volume objectives and bounds on the beamlet-weights, we find that the considered optimization method fulfills the requirements for performing iterative regularization. After segment-weight optimization, the treatments obtained using 35 beamlet-weight iterations outperform the treatments obtained using 100 beamlet-weight iterations, both in terms of objective value and of target uniformity. We conclude that iterating too long may in fact deteriorate the quality of the deliverable plan

Backtracking-Based Iterative Regularization Method for Image Compressive Sensing Recovery

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Lingjun Liu

2017-01-01

Full Text Available This paper presents a variant of the iterative shrinkage-thresholding (IST algorithm, called backtracking-based adaptive IST (BAIST, for image compressive sensing (CS reconstruction. For increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity. To add back more details, the BAIST method backtracks to the previous noisy image using L2 norm minimization, i.e., minimizing the Euclidean distance between the current solution and the previous ones. Through this modification, the BAIST method achieves superior performance while maintaining the low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive regularizor to automatically detect the sparsity level of an image. Experimental results show that our algorithm outperforms the original IST method and several excellent CS techniques.

Iterative feature refinement for accurate undersampled MR image reconstruction

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Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong

2016-05-01

Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.

Iterative feature refinement for accurate undersampled MR image reconstruction

International Nuclear Information System (INIS)

Wang, Shanshan; Liu, Jianbo; Liu, Xin; Zheng, Hairong; Liang, Dong; Liu, Qiegen; Ying, Leslie

2016-01-01

Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches. (paper)

Image quality of iterative reconstruction in cranial CT imaging: comparison of model-based iterative reconstruction (MBIR) and adaptive statistical iterative reconstruction (ASiR).

Science.gov (United States)

Notohamiprodjo, S; Deak, Z; Meurer, F; Maertz, F; Mueck, F G; Geyer, L L; Wirth, S

2015-01-01

The purpose of this study was to compare cranial CT (CCT) image quality (IQ) of the MBIR algorithm with standard iterative reconstruction (ASiR). In this institutional review board (IRB)-approved study, raw data sets of 100 unenhanced CCT examinations (120 kV, 50-260 mAs, 20 mm collimation, 0.984 pitch) were reconstructed with both ASiR and MBIR. Signal-to-noise (SNR) and contrast-to-noise (CNR) were calculated from attenuation values measured in caudate nucleus, frontal white matter, anterior ventricle horn, fourth ventricle, and pons. Two radiologists, who were blinded to the reconstruction algorithms, evaluated anonymized multiplanar reformations of 2.5 mm with respect to depiction of different parenchymal structures and impact of artefacts on IQ with a five-point scale (0: unacceptable, 1: less than average, 2: average, 3: above average, 4: excellent). MBIR decreased artefacts more effectively than ASiR (p ASiR was 2 (p ASiR (p ASiR. As CCT is an examination that is frequently required, the use of MBIR may allow for substantial reduction of radiation exposure caused by medical diagnostics. • Model-Based iterative reconstruction (MBIR) effectively decreased artefacts in cranial CT. • MBIR reconstructed images were rated with significantly higher scores for image quality. • Model-Based iterative reconstruction may allow reduced-dose diagnostic examination protocols.

An iterative method for Tikhonov regularization with a general linear regularization operator

NARCIS (Netherlands)

Hochstenbach, M.E.; Reichel, L.

2010-01-01

Tikhonov regularization is one of the most popular approaches to solve discrete ill-posed problems with error-contaminated data. A regularization operator and a suitable value of a regularization parameter have to be chosen. This paper describes an iterative method, based on Golub-Kahan

SU-D-206-03: Segmentation Assisted Fast Iterative Reconstruction Method for Cone-Beam CT

International Nuclear Information System (INIS)

Wu, P; Mao, T; Gong, S; Wang, J; Niu, T; Sheng, K; Xie, Y

2016-01-01

Purpose: Total Variation (TV) based iterative reconstruction (IR) methods enable accurate CT image reconstruction from low-dose measurements with sparse projection acquisition, due to the sparsifiable feature of most CT images using gradient operator. However, conventional solutions require large amount of iterations to generate a decent reconstructed image. One major reason is that the expected piecewise constant property is not taken into consideration at the optimization starting point. In this work, we propose an iterative reconstruction method for cone-beam CT (CBCT) using image segmentation to guide the optimization path more efficiently on the regularization term at the beginning of the optimization trajectory. Methods: Our method applies general knowledge that one tissue component in the CT image contains relatively uniform distribution of CT number. This general knowledge is incorporated into the proposed reconstruction using image segmentation technique to generate the piecewise constant template on the first-pass low-quality CT image reconstructed using analytical algorithm. The template image is applied as an initial value into the optimization process. Results: The proposed method is evaluated on the Shepp-Logan phantom of low and high noise levels, and a head patient. The number of iterations is reduced by overall 40%. Moreover, our proposed method tends to generate a smoother reconstructed image with the same TV value. Conclusion: We propose a computationally efficient iterative reconstruction method for CBCT imaging. Our method achieves a better optimization trajectory and a faster convergence behavior. It does not rely on prior information and can be readily incorporated into existing iterative reconstruction framework. Our method is thus practical and attractive as a general solution to CBCT iterative reconstruction. This work is supported by the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR16F010001), National High-tech R

SU-D-206-03: Segmentation Assisted Fast Iterative Reconstruction Method for Cone-Beam CT

Energy Technology Data Exchange (ETDEWEB)

Wu, P; Mao, T; Gong, S; Wang, J; Niu, T [Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang (China); Sheng, K [Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA (United States); Xie, Y [Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong (China)

2016-06-15

Purpose: Total Variation (TV) based iterative reconstruction (IR) methods enable accurate CT image reconstruction from low-dose measurements with sparse projection acquisition, due to the sparsifiable feature of most CT images using gradient operator. However, conventional solutions require large amount of iterations to generate a decent reconstructed image. One major reason is that the expected piecewise constant property is not taken into consideration at the optimization starting point. In this work, we propose an iterative reconstruction method for cone-beam CT (CBCT) using image segmentation to guide the optimization path more efficiently on the regularization term at the beginning of the optimization trajectory. Methods: Our method applies general knowledge that one tissue component in the CT image contains relatively uniform distribution of CT number. This general knowledge is incorporated into the proposed reconstruction using image segmentation technique to generate the piecewise constant template on the first-pass low-quality CT image reconstructed using analytical algorithm. The template image is applied as an initial value into the optimization process. Results: The proposed method is evaluated on the Shepp-Logan phantom of low and high noise levels, and a head patient. The number of iterations is reduced by overall 40%. Moreover, our proposed method tends to generate a smoother reconstructed image with the same TV value. Conclusion: We propose a computationally efficient iterative reconstruction method for CBCT imaging. Our method achieves a better optimization trajectory and a faster convergence behavior. It does not rely on prior information and can be readily incorporated into existing iterative reconstruction framework. Our method is thus practical and attractive as a general solution to CBCT iterative reconstruction. This work is supported by the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR16F010001), National High-tech R

A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video

Directory of Open Access Journals (Sweden)

Zhang Liangpei

2007-01-01

Full Text Available Super-resolution (SR reconstruction technique is capable of producing a high-resolution image from a sequence of low-resolution images. In this paper, we study an efficient SR algorithm for digital video. To effectively deal with the intractable problems in SR video reconstruction, such as inevitable motion estimation errors, noise, blurring, missing regions, and compression artifacts, the total variation (TV regularization is employed in the reconstruction model. We use the fixed-point iteration method and preconditioning techniques to efficiently solve the associated nonlinear Euler-Lagrange equations of the corresponding variational problem in SR. The proposed algorithm has been tested in several cases of motion and degradation. It is also compared with the Laplacian regularization-based SR algorithm and other TV-based SR algorithms. Experimental results are presented to illustrate the effectiveness of the proposed algorithm.

Multicore Performance of Block Algebraic Iterative Reconstruction Methods

DEFF Research Database (Denmark)

Sørensen, Hans Henrik B.; Hansen, Per Christian

2014-01-01

Algebraic iterative methods are routinely used for solving the ill-posed sparse linear systems arising in tomographic image reconstruction. Here we consider the algebraic reconstruction technique (ART) and the simultaneous iterative reconstruction techniques (SIRT), both of which rely on semiconv......Algebraic iterative methods are routinely used for solving the ill-posed sparse linear systems arising in tomographic image reconstruction. Here we consider the algebraic reconstruction technique (ART) and the simultaneous iterative reconstruction techniques (SIRT), both of which rely...... on semiconvergence. Block versions of these methods, based on a partitioning of the linear system, are able to combine the fast semiconvergence of ART with the better multicore properties of SIRT. These block methods separate into two classes: those that, in each iteration, access the blocks in a sequential manner...... a fixed relaxation parameter in each method, namely, the one that leads to the fastest semiconvergence. Computational results show that for multicore computers, the sequential approach is preferable....

PET regularization by envelope guided conjugate gradients

International Nuclear Information System (INIS)

Kaufman, L.; Neumaier, A.

1996-01-01

The authors propose a new way to iteratively solve large scale ill-posed problems and in particular the image reconstruction problem in positron emission tomography by exploiting the relation between Tikhonov regularization and multiobjective optimization to obtain iteratively approximations to the Tikhonov L-curve and its corner. Monitoring the change of the approximate L-curves allows us to adjust the regularization parameter adaptively during a preconditioned conjugate gradient iteration, so that the desired solution can be reconstructed with a small number of iterations

Regularization iteration imaging algorithm for electrical capacitance tomography

Science.gov (United States)

Tong, Guowei; Liu, Shi; Chen, Hongyan; Wang, Xueyao

2018-03-01

The image reconstruction method plays a crucial role in real-world applications of the electrical capacitance tomography technique. In this study, a new cost function that simultaneously considers the sparsity and low-rank properties of the imaging targets is proposed to improve the quality of the reconstruction images, in which the image reconstruction task is converted into an optimization problem. Within the framework of the split Bregman algorithm, an iterative scheme that splits a complicated optimization problem into several simpler sub-tasks is developed to solve the proposed cost function efficiently, in which the fast-iterative shrinkage thresholding algorithm is introduced to accelerate the convergence. Numerical experiment results verify the effectiveness of the proposed algorithm in improving the reconstruction precision and robustness.

Iterative Regularization with Minimum-Residual Methods

DEFF Research Database (Denmark)

Jensen, Toke Koldborg; Hansen, Per Christian

2007-01-01

subspaces. We provide a combination of theory and numerical examples, and our analysis confirms the experience that MINRES and MR-II can work as general regularization methods. We also demonstrate theoretically and experimentally that the same is not true, in general, for GMRES and RRGMRES their success......We study the regularization properties of iterative minimum-residual methods applied to discrete ill-posed problems. In these methods, the projection onto the underlying Krylov subspace acts as a regularizer, and the emphasis of this work is on the role played by the basis vectors of these Krylov...... as regularization methods is highly problem dependent....

Iterative regularization with minimum-residual methods

DEFF Research Database (Denmark)

Jensen, Toke Koldborg; Hansen, Per Christian

2006-01-01

subspaces. We provide a combination of theory and numerical examples, and our analysis confirms the experience that MINRES and MR-II can work as general regularization methods. We also demonstrate theoretically and experimentally that the same is not true, in general, for GMRES and RRGMRES - their success......We study the regularization properties of iterative minimum-residual methods applied to discrete ill-posed problems. In these methods, the projection onto the underlying Krylov subspace acts as a regularizer, and the emphasis of this work is on the role played by the basis vectors of these Krylov...... as regularization methods is highly problem dependent....

Convergence of SART + OS + TV iterative reconstruction algorithm for optical CT imaging of gel dosimeters

International Nuclear Information System (INIS)

Du, Yi; Yu, Gongyi; Xiang, Xincheng; Wang, Xiangang; De Deene, Yves

2017-01-01

Computational simulations are used to investigate the convergence of a hybrid iterative algorithm for optical CT reconstruction, i.e. the simultaneous algebraic reconstruction technique (SART) integrated with ordered subsets (OS) iteration and total variation (TV) minimization regularization, or SART+OS+TV for short. The influence of parameter selection to reach convergence, spatial dose gradient integrity, MTF and convergent speed are discussed. It’s shown that the results of SART+OS+TV algorithm converge to the true values without significant bias, and MTF and convergent speed are affected by different parameter sets used for iterative calculation. In conclusion, the performance of the SART+OS+TV depends on parameter selection, which also implies that careful parameter tuning work is required and necessary for proper spatial performance and fast convergence. (paper)

Task-Driven Optimization of Fluence Field and Regularization for Model-Based Iterative Reconstruction in Computed Tomography.

Science.gov (United States)

Gang, Grace J; Siewerdsen, Jeffrey H; Stayman, J Webster

2017-12-01

This paper presents a joint optimization of dynamic fluence field modulation (FFM) and regularization in quadratic penalized-likelihood reconstruction that maximizes a task-based imaging performance metric. We adopted a task-driven imaging framework for prospective designs of the imaging parameters. A maxi-min objective function was adopted to maximize the minimum detectability index ( ) throughout the image. The optimization algorithm alternates between FFM (represented by low-dimensional basis functions) and local regularization (including the regularization strength and directional penalty weights). The task-driven approach was compared with three FFM strategies commonly proposed for FBP reconstruction (as well as a task-driven TCM strategy) for a discrimination task in an abdomen phantom. The task-driven FFM assigned more fluence to less attenuating anteroposterior views and yielded approximately constant fluence behind the object. The optimal regularization was almost uniform throughout image. Furthermore, the task-driven FFM strategy redistribute fluence across detector elements in order to prescribe more fluence to the more attenuating central region of the phantom. Compared with all strategies, the task-driven FFM strategy not only improved minimum by at least 17.8%, but yielded higher over a large area inside the object. The optimal FFM was highly dependent on the amount of regularization, indicating the importance of a joint optimization. Sample reconstructions of simulated data generally support the performance estimates based on computed . The improvements in detectability show the potential of the task-driven imaging framework to improve imaging performance at a fixed dose, or, equivalently, to provide a similar level of performance at reduced dose.

Adaptive Statistical Iterative Reconstruction-V Versus Adaptive Statistical Iterative Reconstruction: Impact on Dose Reduction and Image Quality in Body Computed Tomography.

Science.gov (United States)

Gatti, Marco; Marchisio, Filippo; Fronda, Marco; Rampado, Osvaldo; Faletti, Riccardo; Bergamasco, Laura; Ropolo, Roberto; Fonio, Paolo

The aim of this study was to evaluate the impact on dose reduction and image quality of the new iterative reconstruction technique: adaptive statistical iterative reconstruction (ASIR-V). Fifty consecutive oncologic patients acted as case controls undergoing during their follow-up a computed tomography scan both with ASIR and ASIR-V. Each study was analyzed in a double-blinded fashion by 2 radiologists. Both quantitative and qualitative analyses of image quality were conducted. Computed tomography scanner radiation output was 38% (29%-45%) lower (P ASIR-V examinations than for the ASIR ones. The quantitative image noise was significantly lower (P ASIR-V. Adaptive statistical iterative reconstruction-V had a higher performance for the subjective image noise (P = 0.01 for 5 mm and P = 0.009 for 1.25 mm), the other parameters (image sharpness, diagnostic acceptability, and overall image quality) being similar (P > 0.05). Adaptive statistical iterative reconstruction-V is a new iterative reconstruction technique that has the potential to provide image quality equal to or greater than ASIR, with a dose reduction around 40%.

NUFFT-Based Iterative Image Reconstruction via Alternating Direction Total Variation Minimization for Sparse-View CT

Directory of Open Access Journals (Sweden)

Bin Yan

2015-01-01

Full Text Available Sparse-view imaging is a promising scanning method which can reduce the radiation dose in X-ray computed tomography (CT. Reconstruction algorithm for sparse-view imaging system is of significant importance. The adoption of the spatial iterative algorithm for CT image reconstruction has a low operation efficiency and high computation requirement. A novel Fourier-based iterative reconstruction technique that utilizes nonuniform fast Fourier transform is presented in this study along with the advanced total variation (TV regularization for sparse-view CT. Combined with the alternating direction method, the proposed approach shows excellent efficiency and rapid convergence property. Numerical simulations and real data experiments are performed on a parallel beam CT. Experimental results validate that the proposed method has higher computational efficiency and better reconstruction quality than the conventional algorithms, such as simultaneous algebraic reconstruction technique using TV method and the alternating direction total variation minimization approach, with the same time duration. The proposed method appears to have extensive applications in X-ray CT imaging.

Right adrenal vein: comparison between adaptive statistical iterative reconstruction and model-based iterative reconstruction.

Science.gov (United States)

Noda, Y; Goshima, S; Nagata, S; Miyoshi, T; Kawada, H; Kawai, N; Tanahashi, Y; Matsuo, M

2018-06-01

To compare right adrenal vein (RAV) visualisation and contrast enhancement degree on adrenal venous phase images reconstructed using adaptive statistical iterative reconstruction (ASiR) and model-based iterative reconstruction (MBIR) techniques. This prospective study was approved by the institutional review board, and written informed consent was waived. Fifty-seven consecutive patients who underwent adrenal venous phase imaging were enrolled. The same raw data were reconstructed using ASiR 40% and MBIR. The expert and beginner independently reviewed computed tomography (CT) images. RAV visualisation rates, background noise, and CT attenuation of the RAV, right adrenal gland, inferior vena cava (IVC), hepatic vein, and bilateral renal veins were compared between the two reconstruction techniques. RAV visualisation rates were higher with MBIR than with ASiR (95% versus 88%, p=0.13 in expert and 93% versus 75%, p=0.002 in beginner, respectively). RAV visualisation confidence ratings with MBIR were significantly greater than with ASiR (pASiR (pASiR (p=0.0013 and 0.02). Reconstruction of adrenal venous phase images using MBIR significantly reduces background noise, leading to an improvement in the RAV visualisation compared with ASiR. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

A faster ordered-subset convex algorithm for iterative reconstruction in a rotation-free micro-CT system

International Nuclear Information System (INIS)

Quan, E; Lalush, D S

2009-01-01

We present a faster iterative reconstruction algorithm based on the ordered-subset convex (OSC) algorithm for transmission CT. The OSC algorithm was modified such that it calculates the normalization term before the iterative process in order to save computational cost. The modified version requires only one backprojection per iteration as compared to two required for the original OSC. We applied the modified OSC (MOSC) algorithm to a rotation-free micro-CT system that we proposed previously, observed its performance, and compared with the OSC algorithm for 3D cone-beam reconstruction. Measurements on the reconstructed images as well as the point spread functions show that MOSC is quite similar to OSC; in noise-resolution trade-off, MOSC is comparable with OSC in a regular-noise situation and it is slightly worse than OSC in an extremely high-noise situation. The timing record shows that MOSC saves 25-30% CPU time, depending on the number of iterations used. We conclude that the MOSC algorithm is more efficient than OSC and provides comparable images.

SPET reconstruction with a non-uniform attenuation coefficient using an analytical regularizing iterative method

International Nuclear Information System (INIS)

Soussaline, F.; LeCoq, C.; Raynaud, C.; Kellershohn, C.

1982-09-01

The aim of this study is to evaluate the potential of the RIM technique when used in brain studies. The analytical Regulatorizing Iterative Method (RIM) is designed to provide fast and accurate reconstruction of tomographic images when non-uniform attenuation is to be accounted for. As indicated by phantom studies, this method improves the contrast and the signal-to-noise ratio as compared to those obtained with FBP (Filtered Back Projection) technique. Preliminary results obtained in brain studies using AMPI-123 (isopropil-amphetamine I-123) are very encouraging in terms of quantitative regional cellular activity. However, the clinical usefulness of this mathematically accurate reconstruction procedure is going to be demonstrated in our Institution, in comparing quantitative data in heart or liver studies where control values can be obtained

Alpha image reconstruction (AIR): A new iterative CT image reconstruction approach using voxel-wise alpha blending

International Nuclear Information System (INIS)

Hofmann, Christian; Sawall, Stefan; Knaup, Michael; Kachelrieß, Marc

2014-01-01

Purpose: Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise (and thereby patient dose), to reduce artifacts, or to improve spatial resolution. Among vendors and researchers, however, there is no consensus of how to best achieve these aims. The general approach is to incorporatea priori knowledge into iterative image reconstruction, for example, by adding additional constraints to the cost function, which penalize variations between neighboring voxels. However, this approach to regularization in general poses a resolution noise trade-off because the stronger the regularization, and thus the noise reduction, the stronger the loss of spatial resolution and thus loss of anatomical detail. The authors propose a method which tries to improve this trade-off. The proposed reconstruction algorithm is called alpha image reconstruction (AIR). One starts with generating basis images, which emphasize certain desired image properties, like high resolution or low noise. The AIR algorithm reconstructs voxel-specific weighting coefficients that are applied to combine the basis images. By combining the desired properties of each basis image, one can generate an image with lower noise and maintained high contrast resolution thus improving the resolution noise trade-off. Methods: All simulations and reconstructions are performed in native fan-beam geometry. A water phantom with resolution bar patterns and low contrast disks is simulated. A filtered backprojection (FBP) reconstruction with a Ram-Lak kernel is used as a reference reconstruction. The results of AIR are compared against the FBP results and against a penalized weighted least squares reconstruction which uses total variation as regularization. The simulations are based on the geometry of the Siemens Somatom Definition Flash scanner. To quantitatively assess image quality, the authors analyze line profiles through resolution patterns to define a contrast

Iterative concurrent reconstruction algorithms for emission computed tomography

International Nuclear Information System (INIS)

Brown, J.K.; Hasegawa, B.H.; Lang, T.F.

1994-01-01

Direct reconstruction techniques, such as those based on filtered backprojection, are typically used for emission computed tomography (ECT), even though it has been argued that iterative reconstruction methods may produce better clinical images. The major disadvantage of iterative reconstruction algorithms, and a significant reason for their lack of clinical acceptance, is their computational burden. We outline a new class of ''concurrent'' iterative reconstruction techniques for ECT in which the reconstruction process is reorganized such that a significant fraction of the computational processing occurs concurrently with the acquisition of ECT projection data. These new algorithms use the 10-30 min required for acquisition of a typical SPECT scan to iteratively process the available projection data, significantly reducing the requirements for post-acquisition processing. These algorithms are tested on SPECT projection data from a Hoffman brain phantom acquired with a 2 x 10 5 counts in 64 views each having 64 projections. The SPECT images are reconstructed as 64 x 64 tomograms, starting with six angular views. Other angular views are added to the reconstruction process sequentially, in a manner that reflects their availability for a typical acquisition protocol. The results suggest that if T s of concurrent processing are used, the reconstruction processing time required after completion of the data acquisition can be reduced by at least 1/3 T s. (Author)

Higher order total variation regularization for EIT reconstruction.

Science.gov (United States)

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Zhang, Fan; Mueller-Lisse, Ullrich; Moeller, Knut

2018-01-08

Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images. Graphical abstract Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.

Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.

Science.gov (United States)

She, Huajun; Chen, Rong-Rong; Liang, Dong; DiBella, Edward V R; Ying, Leslie

2014-02-01

To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Copyright © 2013 Wiley Periodicals, Inc.

Impact of iterative reconstruction on CT coronary calcium quantification

DEFF Research Database (Denmark)

Kurata, Akira; Dharampal, Anoeshka; Dedic, Admir

2013-01-01

We evaluated the influence of sinogram-affirmed iterative reconstruction (SAFIRE) on the coronary artery calcium (CAC) score by computed tomography (CT).......We evaluated the influence of sinogram-affirmed iterative reconstruction (SAFIRE) on the coronary artery calcium (CAC) score by computed tomography (CT)....

Iterated Process Analysis over Lattice-Valued Regular Expressions

DEFF Research Database (Denmark)

Midtgaard, Jan; Nielson, Flemming; Nielson, Hanne Riis

2016-01-01

We present an iterated approach to statically analyze programs of two processes communicating by message passing. Our analysis operates over a domain of lattice-valued regular expressions, and computes increasingly better approximations of each process's communication behavior. Overall the work e...... extends traditional semantics-based program analysis techniques to automatically reason about message passing in a manner that can simultaneously analyze both values of variables as well as message order, message content, and their interdependencies.......We present an iterated approach to statically analyze programs of two processes communicating by message passing. Our analysis operates over a domain of lattice-valued regular expressions, and computes increasingly better approximations of each process's communication behavior. Overall the work...

Iterative reconstruction reduces abdominal CT dose

International Nuclear Information System (INIS)

Martinsen, Anne Catrine Trægde; Sæther, Hilde Kjernlie; Hol, Per Kristian; Olsen, Dag Rune; Skaane, Per

2012-01-01

Objective: In medical imaging, lowering radiation dose from computed tomography scanning, without reducing diagnostic performance is a desired achievement. Iterative image reconstruction may be one tool to achieve dose reduction. This study reports the diagnostic performance using a blending of 50% statistical iterative reconstruction (ASIR) and filtered back projection reconstruction (FBP) compared to standard FBP image reconstruction at different dose levels for liver phantom examinations. Methods: An anthropomorphic liver phantom was scanned at 250, 185, 155, 140, 120 and 100 mA s, on a 64-slice GE Lightspeed VCT scanner. All scans were reconstructed with ASIR and FBP. Four readers evaluated independently on a 5-point scale 21 images, each containing 32 test sectors. In total 672 areas were assessed. ROC analysis was used to evaluate the differences. Results: There was a difference in AUC between the 250 mA s FBP images and the 120 and 100 mA s FBP images. ASIR reconstruction gave a significantly higher diagnostic performance compared to standard reconstruction at 100 mA s. Conclusion: A blending of 50–90% ASIR and FBP may improve image quality of low dose CT examinations of the liver, and thus give a potential for reducing radiation dose.

Iterative CT reconstruction via minimizing adaptively reweighted total variation.

Science.gov (United States)

Zhu, Lei; Niu, Tianye; Petrongolo, Michael

2014-01-01

Iterative reconstruction via total variation (TV) minimization has demonstrated great successes in accurate CT imaging from under-sampled projections. When projections are further reduced, over-smoothing artifacts appear in the current reconstruction especially around the structure boundaries. We propose a practical algorithm to improve TV-minimization based CT reconstruction on very few projection data. Based on the theory of compressed sensing, the L-0 norm approach is more desirable to further reduce the projection views. To overcome the computational difficulty of the non-convex optimization of the L-0 norm, we implement an adaptive weighting scheme to approximate the solution via a series of TV minimizations for practical use in CT reconstruction. The weight on TV is initialized as uniform ones, and is automatically changed based on the gradient of the reconstructed image from the previous iteration. The iteration stops when a small difference between the weighted TV values is observed on two consecutive reconstructed images. We evaluate the proposed algorithm on both a digital phantom and a physical phantom. Using 20 equiangular projections, our method reduces reconstruction errors in the conventional TV minimization by a factor of more than 5, with improved spatial resolution. By adaptively reweighting TV in iterative CT reconstruction, we successfully further reduce the projection number for the same or better image quality.

Distributed 3-D iterative reconstruction for quantitative SPECT

International Nuclear Information System (INIS)

Ju, Z.W.; Frey, E.C.; Tsui, B.M.W.

1995-01-01

The authors describe a distributed three dimensional (3-D) iterative reconstruction library for quantitative single-photon emission computed tomography (SPECT). This library includes 3-D projector-backprojector pairs (PBPs) and distributed 3-D iterative reconstruction algorithms. The 3-D PBPs accurately and efficiently model various combinations of the image degrading factors including attenuation, detector response and scatter response. These PBPs were validated by comparing projection data computed using the projectors with that from direct Monte Carlo (MC) simulations. The distributed 3-D iterative algorithms spread the projection-backprojection operations for all the projection angles over a heterogeneous network of single or multi-processor computers to reduce the reconstruction time. Based on a master/slave paradigm, these distributed algorithms provide dynamic load balancing and fault tolerance. The distributed algorithms were verified by comparing images reconstructed using both the distributed and non-distributed algorithms. Computation times for distributed 3-D reconstructions running on up to 4 identical processors were reduced by a factor approximately 80--90% times the number of the processors participating, compared to those for non-distributed 3-D reconstructions running on a single processor. When combined with faster affordable computers, this library provides an efficient means for implementing accurate reconstruction and compensation methods to improve quality and quantitative accuracy in SPECT images

Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT.

Science.gov (United States)

Matenine, Dmitri; Mascolo-Fortin, Julia; Goussard, Yves; Després, Philippe

2015-11-01

The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numerical simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can potentially improve the rendering of

Single photon emission computed tomography using a regularizing iterative method for attenuation correction

International Nuclear Information System (INIS)

Soussaline, Francoise; Cao, A.; Lecoq, G.

1981-06-01

An analytically exact solution to the attenuated tomographic operator is proposed. Such a technique called Regularizing Iterative Method (RIM) belongs to the iterative class of procedures where a priori knowledge can be introduced on the evaluation of the size and shape of the activity domain to be reconstructed, and on the exact attenuation distribution. The relaxation factor used is so named because it leads to fast convergence and provides noise filtering for a small number of iteractions. The effectiveness of such a method was tested in the Single Photon Emission Computed Tomography (SPECT) reconstruction problem, with the goal of precise correction for attenuation before quantitative study. Its implementation involves the use of a rotating scintillation camera based SPECT detector connected to a mini computer system. Mathematical simulations of cylindical uniformly attenuated phantoms indicate that in the range of a priori calculated relaxation factor a fast converging solution can always be found with a (contrast) accuracy of the order of 0.2 to 4% given that numerical errors and noise are or not, taken into account. The sensitivity of the (RIM) algorithm to errors in the size of the reconstructed object and in the value of the attenuation coefficient μ was studied, using the same simulation data. Extreme variations of +- 15% in these parameters will lead to errors of the order of +- 20% in the quantitative results. Physical phantoms representing a variety of geometrical situations were also studied

Pediatric 320-row cardiac computed tomography using electrocardiogram-gated model-based full iterative reconstruction

Energy Technology Data Exchange (ETDEWEB)

Shirota, Go; Maeda, Eriko; Namiki, Yoko; Bari, Razibul; Abe, Osamu [The University of Tokyo, Department of Radiology, Graduate School of Medicine, Tokyo (Japan); Ino, Kenji [The University of Tokyo Hospital, Imaging Center, Tokyo (Japan); Torigoe, Rumiko [Toshiba Medical Systems, Tokyo (Japan)

2017-10-15

Full iterative reconstruction algorithm is available, but its diagnostic quality in pediatric cardiac CT is unknown. To compare the imaging quality of two algorithms, full and hybrid iterative reconstruction, in pediatric cardiac CT. We included 49 children with congenital cardiac anomalies who underwent cardiac CT. We compared quality of images reconstructed using the two algorithms (full and hybrid iterative reconstruction) based on a 3-point scale for the delineation of the following anatomical structures: atrial septum, ventricular septum, right atrium, right ventricle, left atrium, left ventricle, main pulmonary artery, ascending aorta, aortic arch including the patent ductus arteriosus, descending aorta, right coronary artery and left main trunk. We evaluated beam-hardening artifacts from contrast-enhancement material using a 3-point scale, and we evaluated the overall image quality using a 5-point scale. We also compared image noise, signal-to-noise ratio and contrast-to-noise ratio between the algorithms. The overall image quality was significantly higher with full iterative reconstruction than with hybrid iterative reconstruction (3.67±0.79 vs. 3.31±0.89, P=0.0072). The evaluation scores for most of the gross structures were higher with full iterative reconstruction than with hybrid iterative reconstruction. There was no significant difference between full and hybrid iterative reconstruction for the presence of beam-hardening artifacts. Image noise was significantly lower in full iterative reconstruction, while signal-to-noise ratio and contrast-to-noise ratio were significantly higher in full iterative reconstruction. The diagnostic quality was superior in images with cardiac CT reconstructed with electrocardiogram-gated full iterative reconstruction. (orig.)

Pediatric 320-row cardiac computed tomography using electrocardiogram-gated model-based full iterative reconstruction

International Nuclear Information System (INIS)

Shirota, Go; Maeda, Eriko; Namiki, Yoko; Bari, Razibul; Abe, Osamu; Ino, Kenji; Torigoe, Rumiko

2017-01-01

Full iterative reconstruction algorithm is available, but its diagnostic quality in pediatric cardiac CT is unknown. To compare the imaging quality of two algorithms, full and hybrid iterative reconstruction, in pediatric cardiac CT. We included 49 children with congenital cardiac anomalies who underwent cardiac CT. We compared quality of images reconstructed using the two algorithms (full and hybrid iterative reconstruction) based on a 3-point scale for the delineation of the following anatomical structures: atrial septum, ventricular septum, right atrium, right ventricle, left atrium, left ventricle, main pulmonary artery, ascending aorta, aortic arch including the patent ductus arteriosus, descending aorta, right coronary artery and left main trunk. We evaluated beam-hardening artifacts from contrast-enhancement material using a 3-point scale, and we evaluated the overall image quality using a 5-point scale. We also compared image noise, signal-to-noise ratio and contrast-to-noise ratio between the algorithms. The overall image quality was significantly higher with full iterative reconstruction than with hybrid iterative reconstruction (3.67±0.79 vs. 3.31±0.89, P=0.0072). The evaluation scores for most of the gross structures were higher with full iterative reconstruction than with hybrid iterative reconstruction. There was no significant difference between full and hybrid iterative reconstruction for the presence of beam-hardening artifacts. Image noise was significantly lower in full iterative reconstruction, while signal-to-noise ratio and contrast-to-noise ratio were significantly higher in full iterative reconstruction. The diagnostic quality was superior in images with cardiac CT reconstructed with electrocardiogram-gated full iterative reconstruction. (orig.)

Total variation regularization for a backward time-fractional diffusion problem

International Nuclear Information System (INIS)

Wang, Liyan; Liu, Jijun

2013-01-01

Consider a two-dimensional backward problem for a time-fractional diffusion process, which can be considered as image de-blurring where the blurring process is assumed to be slow diffusion. In order to avoid the over-smoothing effect for object image with edges and to construct a fast reconstruction scheme, the total variation regularizing term and the data residual error in the frequency domain are coupled to construct the cost functional. The well posedness of this optimization problem is studied. The minimizer is sought approximately using the iteration process for a series of optimization problems with Bregman distance as a penalty term. This iteration reconstruction scheme is essentially a new regularizing scheme with coupling parameter in the cost functional and the iteration stopping times as two regularizing parameters. We give the choice strategy for the regularizing parameters in terms of the noise level of measurement data, which yields the optimal error estimate on the iterative solution. The series optimization problems are solved by alternative iteration with explicit exact solution and therefore the amount of computation is much weakened. Numerical implementations are given to support our theoretical analysis on the convergence rate and to show the significant reconstruction improvements. (paper)

Inpainting for Fringe Projection Profilometry Based on Geometrically Guided Iterative Regularization.

Science.gov (United States)

Budianto; Lun, Daniel P K

2015-12-01

Conventional fringe projection profilometry methods often have difficulty in reconstructing the 3D model of objects when the fringe images have the so-called highlight regions due to strong illumination from nearby light sources. Within a highlight region, the fringe pattern is often overwhelmed by the strong reflected light. Thus, the 3D information of the object, which is originally embedded in the fringe pattern, can no longer be retrieved. In this paper, a novel inpainting algorithm is proposed to restore the fringe images in the presence of highlights. The proposed method first detects the highlight regions based on a Gaussian mixture model. Then, a geometric sketch of the missing fringes is made and used as the initial guess of an iterative regularization procedure for regenerating the missing fringes. The simulation and experimental results show that the proposed algorithm can accurately reconstruct the 3D model of objects even when their fringe images have large highlight regions. It significantly outperforms the traditional approaches in both quantitative and qualitative evaluations.

CT angiography after carotid artery stenting: assessment of the utility of adaptive statistical iterative reconstruction and model-based iterative reconstruction

Energy Technology Data Exchange (ETDEWEB)

Kuya, Keita; Shinohara, Yuki; Fujii, Shinya; Ogawa, Toshihide [Tottori University, Division of Radiology, Department of Pathophysiological Therapeutic Science, Faculty of Medicine, Yonago (Japan); Sakamoto, Makoto; Watanabe, Takashi [Tottori University, Division of Neurosurgery, Department of Brain and Neurosciences, Faculty of Medicine, Yonago (Japan); Iwata, Naoki; Kishimoto, Junichi [Tottori University, Division of Clinical Radiology Faculty of Medicine, Yonago (Japan); Kaminou, Toshio [Osaka Minami Medical Center, Department of Radiology, Osaka (Japan)

2014-11-15

Follow-up CT angiography (CTA) is routinely performed for post-procedure management after carotid artery stenting (CAS). However, the stent lumen tends to be underestimated because of stent artifacts on CTA reconstructed with the filtered back projection (FBP) technique. We assessed the utility of new iterative reconstruction techniques, such as adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR), for CTA after CAS in comparison with FBP. In a phantom study, we evaluated the differences among the three reconstruction techniques with regard to the relationship between the stent luminal diameter and the degree of underestimation of stent luminal diameter. In a clinical study, 34 patients who underwent follow-up CTA after CAS were included. We compared the stent luminal diameters among FBP, ASIR, and MBIR, and performed visual assessment of low attenuation area (LAA) in the stent lumen using a three-point scale. In the phantom study, stent luminal diameter was increasingly underestimated as luminal diameter became smaller in all CTA images. Stent luminal diameter was larger with MBIR than with the other reconstruction techniques. Similarly, in the clinical study, stent luminal diameter was larger with MBIR than with the other reconstruction techniques. LAA detectability scores of MBIR were greater than or equal to those of FBP and ASIR in all cases. MBIR improved the accuracy of assessment of stent luminal diameter and LAA detectability in the stent lumen when compared with FBP and ASIR. We conclude that MBIR is a useful reconstruction technique for CTA after CAS. (orig.)

CT angiography after carotid artery stenting: assessment of the utility of adaptive statistical iterative reconstruction and model-based iterative reconstruction

International Nuclear Information System (INIS)

Kuya, Keita; Shinohara, Yuki; Fujii, Shinya; Ogawa, Toshihide; Sakamoto, Makoto; Watanabe, Takashi; Iwata, Naoki; Kishimoto, Junichi; Kaminou, Toshio

2014-01-01

Follow-up CT angiography (CTA) is routinely performed for post-procedure management after carotid artery stenting (CAS). However, the stent lumen tends to be underestimated because of stent artifacts on CTA reconstructed with the filtered back projection (FBP) technique. We assessed the utility of new iterative reconstruction techniques, such as adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR), for CTA after CAS in comparison with FBP. In a phantom study, we evaluated the differences among the three reconstruction techniques with regard to the relationship between the stent luminal diameter and the degree of underestimation of stent luminal diameter. In a clinical study, 34 patients who underwent follow-up CTA after CAS were included. We compared the stent luminal diameters among FBP, ASIR, and MBIR, and performed visual assessment of low attenuation area (LAA) in the stent lumen using a three-point scale. In the phantom study, stent luminal diameter was increasingly underestimated as luminal diameter became smaller in all CTA images. Stent luminal diameter was larger with MBIR than with the other reconstruction techniques. Similarly, in the clinical study, stent luminal diameter was larger with MBIR than with the other reconstruction techniques. LAA detectability scores of MBIR were greater than or equal to those of FBP and ASIR in all cases. MBIR improved the accuracy of assessment of stent luminal diameter and LAA detectability in the stent lumen when compared with FBP and ASIR. We conclude that MBIR is a useful reconstruction technique for CTA after CAS. (orig.)

Dose reduction in pediatric abdominal CT: use of iterative reconstruction techniques across different CT platforms

International Nuclear Information System (INIS)

Khawaja, Ranish Deedar Ali; Singh, Sarabjeet; Otrakji, Alexi; Padole, Atul; Lim, Ruth; Nimkin, Katherine; Westra, Sjirk; Kalra, Mannudeep K.; Gee, Michael S.

2015-01-01

Dose reduction in children undergoing CT scanning is an important priority for the radiology community and public at large. Drawbacks of radiation reduction are increased image noise and artifacts, which can affect image interpretation. Iterative reconstruction techniques have been developed to reduce noise and artifacts from reduced-dose CT examinations, although reconstruction algorithm, magnitude of dose reduction and effects on image quality vary. We review the reconstruction principles, radiation dose potential and effects on image quality of several iterative reconstruction techniques commonly used in clinical settings, including 3-D adaptive iterative dose reduction (AIDR-3D), adaptive statistical iterative reconstruction (ASIR), iDose, sinogram-affirmed iterative reconstruction (SAFIRE) and model-based iterative reconstruction (MBIR). We also discuss clinical applications of iterative reconstruction techniques in pediatric abdominal CT. (orig.)

Dose reduction in pediatric abdominal CT: use of iterative reconstruction techniques across different CT platforms

Energy Technology Data Exchange (ETDEWEB)

Khawaja, Ranish Deedar Ali; Singh, Sarabjeet; Otrakji, Alexi; Padole, Atul; Lim, Ruth; Nimkin, Katherine; Westra, Sjirk; Kalra, Mannudeep K.; Gee, Michael S. [MGH Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA (United States)

2015-07-15

Dose reduction in children undergoing CT scanning is an important priority for the radiology community and public at large. Drawbacks of radiation reduction are increased image noise and artifacts, which can affect image interpretation. Iterative reconstruction techniques have been developed to reduce noise and artifacts from reduced-dose CT examinations, although reconstruction algorithm, magnitude of dose reduction and effects on image quality vary. We review the reconstruction principles, radiation dose potential and effects on image quality of several iterative reconstruction techniques commonly used in clinical settings, including 3-D adaptive iterative dose reduction (AIDR-3D), adaptive statistical iterative reconstruction (ASIR), iDose, sinogram-affirmed iterative reconstruction (SAFIRE) and model-based iterative reconstruction (MBIR). We also discuss clinical applications of iterative reconstruction techniques in pediatric abdominal CT. (orig.)

Model-based iterative reconstruction for reduction of radiation dose in abdominopelvic CT: comparison to adaptive statistical iterative reconstruction.

Science.gov (United States)

Yasaka, Koichiro; Katsura, Masaki; Akahane, Masaaki; Sato, Jiro; Matsuda, Izuru; Ohtomo, Kuni

2013-12-01

To evaluate dose reduction and image quality of abdominopelvic computed tomography (CT) reconstructed with model-based iterative reconstruction (MBIR) compared to adaptive statistical iterative reconstruction (ASIR). In this prospective study, 85 patients underwent referential-, low-, and ultralow-dose unenhanced abdominopelvic CT. Images were reconstructed with ASIR for low-dose (L-ASIR) and ultralow-dose CT (UL-ASIR), and with MBIR for ultralow-dose CT (UL-MBIR). Image noise was measured in the abdominal aorta and iliopsoas muscle. Subjective image analyses and a lesion detection study (adrenal nodules) were conducted by two blinded radiologists. A reference standard was established by a consensus panel of two different radiologists using referential-dose CT reconstructed with filtered back projection. Compared to low-dose CT, there was a 63% decrease in dose-length product with ultralow-dose CT. UL-MBIR had significantly lower image noise than L-ASIR and UL-ASIR (all pASIR and UL-ASIR (all pASIR in diagnostic acceptability (p>0.65), or diagnostic performance for adrenal nodules (p>0.87). MBIR significantly improves image noise and streak artifacts compared to ASIR, and can achieve radiation dose reduction without severely compromising image quality.

A new approach of equilibrium reconstruction for ITER

International Nuclear Information System (INIS)

Imazawa, R.; Kawano, Y.; Kusama, Y.

2011-01-01

We have proposed a new approach for equilibrium reconstruction that can be applied to ITER-like burning plasmas. In this study, we have focused on carrying out equilibrium reconstruction using polarimetry, which is feasible for ITER-like burning plasmas. Polarimetry in burning plasmas is different from that in the existing tokamaks in two regards: (1) increased importance of the relativistic effects and (2) significant coupling with the Faraday and Cotton–Mouton effects. We found that when polarimetric data (orientation angle, θ, and ellipticity angle, ε, of a polarization state) are used as the constraints in the equilibrium reconstruction, the optimum weighting factors for θ and ε depend on the magnetic surfaces through which the viewing chord of polarimetry passes. We applied our approach to the operation scenarios II (S2) and IV (S4) in ITER. In the case where the viewing chords are via both the equatorial and upper ports, the measurement requirements for the accuracy of the q-profile in ITER (±10%) were satisfied in S2 and S4 when the measuring errors of θ and ε were less than 0.5° and 3°, respectively.

Direct iterative reconstruction of computed tomography trajectories (DIRECTT)

International Nuclear Information System (INIS)

Lange, A.; Hentschel, M.P.; Schors, J.

2004-01-01

The direct reconstruction approach employs an iterative procedure by selection of and angular averaging over projected trajectory data of volume elements. This avoids the blur effects of the classical Fourier method due to the sampling theorem. But longer computing time is required. The reconstructed tomographic images reveal at least the spatial resolution of the radiation detector. Any set of projection angles may be selected for the measurements. Limited rotation of the object yields still good reconstruction of details. Projections of a partial region of the object can be reconstructed without additional artifacts thus reducing the overall radiation dose. Noisy signal data from low dose irradiation have low impact on spatial resolution. The image quality is monitored during all iteration steps and is pre-selected according to the specific requirements. DIRECTT can be applied independently from the measurement equipment in addition to conventional reconstruction or as a refinement filter. (author)

MRI reconstruction with joint global regularization and transform learning.

Science.gov (United States)

Tanc, A Korhan; Eksioglu, Ender M

2016-10-01

Sparsity based regularization has been a popular approach to remedy the measurement scarcity in image reconstruction. Recently, sparsifying transforms learned from image patches have been utilized as an effective regularizer for the Magnetic Resonance Imaging (MRI) reconstruction. Here, we infuse additional global regularization terms to the patch-based transform learning. We develop an algorithm to solve the resulting novel cost function, which includes both patchwise and global regularization terms. Extensive simulation results indicate that the introduced mixed approach has improved MRI reconstruction performance, when compared to the algorithms which use either of the patchwise transform learning or global regularization terms alone. Copyright © 2016 Elsevier Ltd. All rights reserved.

Regularized iterative integration combined with non-linear diffusion filtering for phase-contrast x-ray computed tomography.

Science.gov (United States)

Burger, Karin; Koehler, Thomas; Chabior, Michael; Allner, Sebastian; Marschner, Mathias; Fehringer, Andreas; Willner, Marian; Pfeiffer, Franz; Noël, Peter

2014-12-29

Phase-contrast x-ray computed tomography has a high potential to become clinically implemented because of its complementarity to conventional absorption-contrast.In this study, we investigate noise-reducing but resolution-preserving analytical reconstruction methods to improve differential phase-contrast imaging. We apply the non-linear Perona-Malik filter on phase-contrast data prior or post filtered backprojected reconstruction. Secondly, the Hilbert kernel is replaced by regularized iterative integration followed by ramp filtered backprojection as used for absorption-contrast imaging. Combining the Perona-Malik filter with this integration algorithm allows to successfully reveal relevant sample features, quantitatively confirmed by significantly increased structural similarity indices and contrast-to-noise ratios. With this concept, phase-contrast imaging can be performed at considerably lower dose.

Noise propagation in iterative reconstruction algorithms with line searches

International Nuclear Information System (INIS)

Qi, Jinyi

2003-01-01

In this paper we analyze the propagation of noise in iterative image reconstruction algorithms. We derive theoretical expressions for the general form of preconditioned gradient algorithms with line searches. The results are applicable to a wide range of iterative reconstruction problems, such as emission tomography, transmission tomography, and image restoration. A unique contribution of this paper comparing to our previous work [1] is that the line search is explicitly modeled and we do not use the approximation that the gradient of the objective function is zero. As a result, the error in the estimate of noise at early iterations is significantly reduced

Model-based iterative reconstruction and adaptive statistical iterative reconstruction: dose-reduced CT for detecting pancreatic calcification

International Nuclear Information System (INIS)

Yasaka, Koichiro; Katsura, Masaki; Akahane, Masaaki; Sato, Jiro; Matsuda, Izuru; Ohtomo, Kuni

2016-01-01

Iterative reconstruction methods have attracted attention for reducing radiation doses in computed tomography (CT). To investigate the detectability of pancreatic calcification using dose-reduced CT reconstructed with model-based iterative construction (MBIR) and adaptive statistical iterative reconstruction (ASIR). This prospective study approved by Institutional Review Board included 85 patients (57 men, 28 women; mean age, 69.9 years; mean body weight, 61.2 kg). Unenhanced CT was performed three times with different radiation doses (reference-dose CT [RDCT], low-dose CT [LDCT], ultralow-dose CT [ULDCT]). From RDCT, LDCT, and ULDCT, images were reconstructed with filtered-back projection (R-FBP, used for establishing reference standard), ASIR (L-ASIR), and MBIR and ASIR (UL-MBIR and UL-ASIR), respectively. A lesion (pancreatic calcification) detection test was performed by two blinded radiologists with a five-point certainty level scale. Dose-length products of RDCT, LDCT, and ULDCT were 410, 97, and 36 mGy-cm, respectively. Nine patients had pancreatic calcification. The sensitivity for detecting pancreatic calcification with UL-MBIR was high (0.67–0.89) compared to L-ASIR or UL-ASIR (0.11–0.44), and a significant difference was seen between UL-MBIR and UL-ASIR for one reader (P = 0.014). The area under the receiver-operating characteristic curve for UL-MBIR (0.818–0.860) was comparable to that for L-ASIR (0.696–0.844). The specificity was lower with UL-MBIR (0.79–0.92) than with L-ASIR or UL-ASIR (0.96–0.99), and a significant difference was seen for one reader (P < 0.01). In UL-MBIR, pancreatic calcification can be detected with high sensitivity, however, we should pay attention to the slightly lower specificity

Model-based iterative reconstruction and adaptive statistical iterative reconstruction: dose-reduced CT for detecting pancreatic calcification.

Science.gov (United States)

Yasaka, Koichiro; Katsura, Masaki; Akahane, Masaaki; Sato, Jiro; Matsuda, Izuru; Ohtomo, Kuni

2016-01-01

Iterative reconstruction methods have attracted attention for reducing radiation doses in computed tomography (CT). To investigate the detectability of pancreatic calcification using dose-reduced CT reconstructed with model-based iterative construction (MBIR) and adaptive statistical iterative reconstruction (ASIR). This prospective study approved by Institutional Review Board included 85 patients (57 men, 28 women; mean age, 69.9 years; mean body weight, 61.2 kg). Unenhanced CT was performed three times with different radiation doses (reference-dose CT [RDCT], low-dose CT [LDCT], ultralow-dose CT [ULDCT]). From RDCT, LDCT, and ULDCT, images were reconstructed with filtered-back projection (R-FBP, used for establishing reference standard), ASIR (L-ASIR), and MBIR and ASIR (UL-MBIR and UL-ASIR), respectively. A lesion (pancreatic calcification) detection test was performed by two blinded radiologists with a five-point certainty level scale. Dose-length products of RDCT, LDCT, and ULDCT were 410, 97, and 36 mGy-cm, respectively. Nine patients had pancreatic calcification. The sensitivity for detecting pancreatic calcification with UL-MBIR was high (0.67-0.89) compared to L-ASIR or UL-ASIR (0.11-0.44), and a significant difference was seen between UL-MBIR and UL-ASIR for one reader (P = 0.014). The area under the receiver-operating characteristic curve for UL-MBIR (0.818-0.860) was comparable to that for L-ASIR (0.696-0.844). The specificity was lower with UL-MBIR (0.79-0.92) than with L-ASIR or UL-ASIR (0.96-0.99), and a significant difference was seen for one reader (P < 0.01). In UL-MBIR, pancreatic calcification can be detected with high sensitivity, however, we should pay attention to the slightly lower specificity.

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

Science.gov (United States)

Elahi, Sana; kaleem, Muhammad; Omer, Hammad

2018-01-01

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Influence of iterative image reconstruction on CT-based calcium score measurements

NARCIS (Netherlands)

van Osch, Jochen A. C.; Mouden, Mohamed; van Dalen, Jorn A.; Timmer, Jorik R.; Reiffers, Stoffer; Knollema, Siert; Greuter, Marcel J. W.; Ottervanger, Jan Paul; Jager, Piet L.

Iterative reconstruction techniques for coronary CT angiography have been introduced as an alternative for traditional filter back projection (FBP) to reduce image noise, allowing improved image quality and a potential for dose reduction. However, the impact of iterative reconstruction on the

Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR).

Science.gov (United States)

Chen, Baiyu; Barnhart, Huiman; Richard, Samuel; Robins, Marthony; Colsher, James; Samei, Ehsan

2013-11-01

Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables. Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision. Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A. The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms

A heuristic statistical stopping rule for iterative reconstruction in emission tomography

International Nuclear Information System (INIS)

Ben Bouallegue, F.; Mariano-Goulart, D.; Crouzet, J.F.

2013-01-01

We propose a statistical stopping criterion for iterative reconstruction in emission tomography based on a heuristic statistical description of the reconstruction process. The method was assessed for maximum likelihood expectation maximization (MLEM) reconstruction. Based on Monte-Carlo numerical simulations and using a perfectly modeled system matrix, our method was compared with classical iterative reconstruction followed by low-pass filtering in terms of Euclidian distance to the exact object, noise, and resolution. The stopping criterion was then evaluated with realistic PET data of a Hoffman brain phantom produced using the Geant4 application in emission tomography (GATE) platform for different count levels. The numerical experiments showed that compared with the classical method, our technique yielded significant improvement of the noise-resolution tradeoff for a wide range of counting statistics compatible with routine clinical settings. When working with realistic data, the stopping rule allowed a qualitatively and quantitatively efficient determination of the optimal image. Our method appears to give a reliable estimation of the optimal stopping point for iterative reconstruction. It should thus be of practical interest as it produces images with similar or better quality than classical post-filtered iterative reconstruction with a mastered computation time. (author)

X-ray computed tomography using curvelet sparse regularization.

Science.gov (United States)

Wieczorek, Matthias; Frikel, Jürgen; Vogel, Jakob; Eggl, Elena; Kopp, Felix; Noël, Peter B; Pfeiffer, Franz; Demaret, Laurent; Lasser, Tobias

2015-04-01

Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

Tomographic reconstruction by using FPSIRT (Fast Particle System Iterative Reconstruction Technique)

Energy Technology Data Exchange (ETDEWEB)

Moreira, Icaro Valgueiro M.; Melo, Silvio de Barros; Dantas, Carlos; Lima, Emerson Alexandre; Silva, Ricardo Martins; Cardoso, Halisson Alberdan C., E-mail: ivmm@cin.ufpe.br, E-mail: sbm@cin.ufpe.br, E-mail: rmas@cin.ufpe.br, E-mail: hacc@cin.ufpe.br, E-mail: ccd@ufpe.br, E-mail: eal@cin.ufpe.br [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil)

2015-07-01

The PSIRT (Particle System Iterative Reconstruction Technique) is a method of tomographic image reconstruction primarily designed to work with configurations suitable for industrial applications. A particle system is an optimization technique inspired in real physical systems that associates to the reconstructing material a set of particles with certain physical features, subject to a force eld, which can produce movement. The system constantly updates the set of particles by repositioning them in such a way as to approach the equilibrium. The elastic potential along a trajectory is a function of the difference between the attenuation coefficient in the current configuration and the corresponding input data. PSIRT has been successfully used to reconstruct simulated and real objects subject to sets of parallel and fanbeam lines in different angles, representing typical gamma-ray tomographic arrangements. One of PSIRT's limitation was its performance, too slow for real time scenarios. In this work, it is presented a reformulation in PSIRT's computational model, which is able to grant the new algorithm, the FPSIRT - Fast System Iterative Reconstruction Technique, a performance up to 200-time faster than PSIRT's. In this work a comparison of their application to real and simulated data from the HSGT, High Speed Gamma Tomograph, is presented. (author)

Tomographic reconstruction by using FPSIRT (Fast Particle System Iterative Reconstruction Technique)

International Nuclear Information System (INIS)

Moreira, Icaro Valgueiro M.; Melo, Silvio de Barros; Dantas, Carlos; Lima, Emerson Alexandre; Silva, Ricardo Martins; Cardoso, Halisson Alberdan C.

2015-01-01

The PSIRT (Particle System Iterative Reconstruction Technique) is a method of tomographic image reconstruction primarily designed to work with configurations suitable for industrial applications. A particle system is an optimization technique inspired in real physical systems that associates to the reconstructing material a set of particles with certain physical features, subject to a force eld, which can produce movement. The system constantly updates the set of particles by repositioning them in such a way as to approach the equilibrium. The elastic potential along a trajectory is a function of the difference between the attenuation coefficient in the current configuration and the corresponding input data. PSIRT has been successfully used to reconstruct simulated and real objects subject to sets of parallel and fanbeam lines in different angles, representing typical gamma-ray tomographic arrangements. One of PSIRT's limitation was its performance, too slow for real time scenarios. In this work, it is presented a reformulation in PSIRT's computational model, which is able to grant the new algorithm, the FPSIRT - Fast System Iterative Reconstruction Technique, a performance up to 200-time faster than PSIRT's. In this work a comparison of their application to real and simulated data from the HSGT, High Speed Gamma Tomograph, is presented. (author)

Iterative CT reconstruction with small pixel size: distance-driven forward projector versus Joseph's

Science.gov (United States)

Hahn, K.; Rassner, U.; Davidson, H. C.; Schöndube, H.; Stierstorfer, K.; Hornegger, J.; Noo, F.

2015-03-01

Over the last few years, iterative reconstruction methods have become an important research topic in x-ray CT imaging. This effort is motivated by increasing evidence that such methods may enable significant savings in terms of dose imparted to the patient. Conceptually, iterative reconstruction methods involve two important ingredients: the statistical model, which includes the forward projector, and a priori information in the image domain, which is expressed using a regularizer. Most often, the image pixel size is chosen to be equal (or close) to the detector pixel size (at field-of-view center). However, there are applications for which a smaller pixel size is desired. In this investigation, we focus on reconstruction with a pixel size that is twice smaller than the detector pixel size. Using such a small pixel size implies a large increase in computational effort when using the distance-driven method for forward projection, which models the detector size. On the other hand, the more efficient method of Joseph will create imbalances in the reconstruction of each pixel, in the sense that there will be large differences in the way each projection contributes to the pixels. The purpose of this work is to evaluate the impact of these imbalances on image quality in comparison with utilization of the distance-driven method. The evaluation involves computational effort, bias and noise metrics, and LROC analysis using human observers. The results show that Joseph's method largely remains attractive.

Limited-angle three-dimensional reconstructions using Fourier transform iterations and Radon transform iterations

International Nuclear Information System (INIS)

Tam, K.C.; Perez-Mendez, V.

1981-01-01

The principles of limited-angle reconstruction of space-limited objects using the concepts of allowed cone and missing cone in Fourier space are discussed. The distortion of a point source resulting from setting the Fourier components in the missing cone to zero has been calculated mathematically, and its bearing on the convergence of an iteration scheme involving Fourier transforms has been analyzed in detail. it was found that the convergence rate is fairly insensitive to the position of the point source within the boundary of the object, apart from an edge effect which tends to enhance some parts of the boundary in reconstructing the object. Another iteration scheme involving Radon transforms was introduced and compared to the Fourier transform method in such areas as root mean square error, stability with respect to noise, and computer reconstruction time

Limited-angle 3-D reconstructions using Fourier transform iterations and Radon transform iterations

International Nuclear Information System (INIS)

Tam, K.C.; Perez-Mendez, V.

1979-12-01

The principles of limited-angle reconstruction of space-limited objects using the concepts of allowed cone and missing cone in Fourier space are discussed. The distortion of a point source resulting from setting the Fourier components in the missing cone to zero was calculated mathematically, and its bearing on the convergence of an iteration scheme involving Fourier transforms was analyzed in detail. It was found that the convergence rate is fairly insensitive to the position of the point source within the boundary of the object, apart from an edge effect that tends to enhance some parts of the boundary in reconstructing the object. Another iteration scheme involving Radon transforms was introduced and compared to the Fourier transform method in such areas as root mean square error, stability with respect to noise, and computer reconstruction time. 8 figures, 2 tables

Improving head and neck CTA with hybrid and model-based iterative reconstruction techniques

NARCIS (Netherlands)

Niesten, J. M.; van der Schaaf, I. C.; Vos, P. C.; Willemink, MJ; Velthuis, B. K.

2015-01-01

AIM: To compare image quality of head and neck computed tomography angiography (CTA) reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MIR) algorithms. MATERIALS AND METHODS: The raw data of 34 studies were

Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR)

Energy Technology Data Exchange (ETDEWEB)

Chen, Baiyu [Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, North Carolina 27705 (United States); Barnhart, Huiman [Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705 (United States); Richard, Samuel [Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, North Carolina 27705 and Department of Radiology, Duke University, Durham, North Carolina 27705 (United States); Robins, Marthony [Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 (United States); Colsher, James [Department of Radiology, Duke University, Durham, North Carolina 27705 (United States); Samei, Ehsan [Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 (United States); Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, North Carolina 27705 (United States); Department of Radiology, Duke University, Durham, North Carolina 27705 (United States); Department of Physics, Department of Biomedical Engineering, and Department of Electronic and Computer Engineering, Duke University, Durham, North Carolina 27705 (United States)

2013-11-15

Purpose: Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables.Methods: Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision.Results: Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A.Conclusions: The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of

Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR)

International Nuclear Information System (INIS)

Chen, Baiyu; Barnhart, Huiman; Richard, Samuel; Robins, Marthony; Colsher, James; Samei, Ehsan

2013-01-01

Purpose: Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables.Methods: Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision.Results: Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A.Conclusions: The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of

Limited angle CT reconstruction by simultaneous spatial and Radon domain regularization based on TV and data-driven tight frame

Science.gov (United States)

Zhang, Wenkun; Zhang, Hanming; Wang, Linyuan; Cai, Ailong; Li, Lei; Yan, Bin

2018-02-01

Limited angle computed tomography (CT) reconstruction is widely performed in medical diagnosis and industrial testing because of the size of objects, engine/armor inspection requirements, and limited scan flexibility. Limited angle reconstruction necessitates usage of optimization-based methods that utilize additional sparse priors. However, most of conventional methods solely exploit sparsity priors of spatial domains. When CT projection suffers from serious data deficiency or various noises, obtaining reconstruction images that meet the requirement of quality becomes difficult and challenging. To solve this problem, this paper developed an adaptive reconstruction method for limited angle CT problem. The proposed method simultaneously uses spatial and Radon domain regularization model based on total variation (TV) and data-driven tight frame. Data-driven tight frame being derived from wavelet transformation aims at exploiting sparsity priors of sinogram in Radon domain. Unlike existing works that utilize pre-constructed sparse transformation, the framelets of the data-driven regularization model can be adaptively learned from the latest projection data in the process of iterative reconstruction to provide optimal sparse approximations for given sinogram. At the same time, an effective alternating direction method is designed to solve the simultaneous spatial and Radon domain regularization model. The experiments for both simulation and real data demonstrate that the proposed algorithm shows better performance in artifacts depression and details preservation than the algorithms solely using regularization model of spatial domain. Quantitative evaluations for the results also indicate that the proposed algorithm applying learning strategy performs better than the dual domains algorithms without learning regularization model

An iterative reconstruction from truncated projection data

International Nuclear Information System (INIS)

Anon.

1985-01-01

Various methods have been proposed for tomographic reconstruction from truncated projection data. In this paper, a reconstructive method is discussed which consists of iterations of filtered back-projection, reprojection and some nonlinear processings. First, the method is so constructed that it converges to a fixed point. Then, to examine its effectiveness, comparisons are made by computer experiments with two existing reconstructive methods for truncated projection data, that is, the method of extrapolation based on the smooth assumption followed by filtered back-projection, and modified additive ART

A multiresolution approach to iterative reconstruction algorithms in X-ray computed tomography.

Science.gov (United States)

De Witte, Yoni; Vlassenbroeck, Jelle; Van Hoorebeke, Luc

2010-09-01

In computed tomography, the application of iterative reconstruction methods in practical situations is impeded by their high computational demands. Especially in high resolution X-ray computed tomography, where reconstruction volumes contain a high number of volume elements (several giga voxels), this computational burden prevents their actual breakthrough. Besides the large amount of calculations, iterative algorithms require the entire volume to be kept in memory during reconstruction, which quickly becomes cumbersome for large data sets. To overcome this obstacle, we present a novel multiresolution reconstruction, which greatly reduces the required amount of memory without significantly affecting the reconstructed image quality. It is shown that, combined with an efficient implementation on a graphical processing unit, the multiresolution approach enables the application of iterative algorithms in the reconstruction of large volumes at an acceptable speed using only limited resources.

Gamma regularization based reconstruction for low dose CT

International Nuclear Information System (INIS)

Zhang, Junfeng; Chen, Yang; Hu, Yining; Luo, Limin; Shu, Huazhong; Li, Bicao; Liu, Jin; Coatrieux, Jean-Louis

2015-01-01

Reducing the radiation in computerized tomography is today a major concern in radiology. Low dose computerized tomography (LDCT) offers a sound way to deal with this problem. However, more severe noise in the reconstructed CT images is observed under low dose scan protocols (e.g. lowered tube current or voltage values). In this paper we propose a Gamma regularization based algorithm for LDCT image reconstruction. This solution is flexible and provides a good balance between the regularizations based on l 0 -norm and l 1 -norm. We evaluate the proposed approach using the projection data from simulated phantoms and scanned Catphan phantoms. Qualitative and quantitative results show that the Gamma regularization based reconstruction can perform better in both edge-preserving and noise suppression when compared with other norms. (paper)

A reconstruction algorithm for electrical impedance tomography based on sparsity regularization

KAUST Repository

Jin, Bangti

2011-08-24

This paper develops a novel sparse reconstruction algorithm for the electrical impedance tomography problem of determining a conductivity parameter from boundary measurements. The sparsity of the \\'inhomogeneity\\' with respect to a certain basis is a priori assumed. The proposed approach is motivated by a Tikhonov functional incorporating a sparsity-promoting ℓ 1-penalty term, and it allows us to obtain quantitative results when the assumption is valid. A novel iterative algorithm of soft shrinkage type was proposed. Numerical results for several two-dimensional problems with both single and multiple convex and nonconvex inclusions were presented to illustrate the features of the proposed algorithm and were compared with one conventional approach based on smoothness regularization. © 2011 John Wiley & Sons, Ltd.

A very fast implementation of 2D iterative reconstruction algorithms

DEFF Research Database (Denmark)

Toft, Peter Aundal; Jensen, Peter James

1996-01-01

that iterative reconstruction algorithms can be implemented and run almost as fast as direct reconstruction algorithms. The method has been implemented in a software package that is available for free, providing reconstruction algorithms using ART, EM, and the Least Squares Conjugate Gradient Method...

EIT image reconstruction with four dimensional regularization.

Science.gov (United States)

Dai, Tao; Soleimani, Manuchehr; Adler, Andy

2008-09-01

Electrical impedance tomography (EIT) reconstructs internal impedance images of the body from electrical measurements on body surface. The temporal resolution of EIT data can be very high, although the spatial resolution of the images is relatively low. Most EIT reconstruction algorithms calculate images from data frames independently, although data are actually highly correlated especially in high speed EIT systems. This paper proposes a 4-D EIT image reconstruction for functional EIT. The new approach is developed to directly use prior models of the temporal correlations among images and 3-D spatial correlations among image elements. A fast algorithm is also developed to reconstruct the regularized images. Image reconstruction is posed in terms of an augmented image and measurement vector which are concatenated from a specific number of previous and future frames. The reconstruction is then based on an augmented regularization matrix which reflects the a priori constraints on temporal and 3-D spatial correlations of image elements. A temporal factor reflecting the relative strength of the image correlation is objectively calculated from measurement data. Results show that image reconstruction models which account for inter-element correlations, in both space and time, show improved resolution and noise performance, in comparison to simpler image models.

l0 regularization based on a prior image incorporated non-local means for limited-angle X-ray CT reconstruction.

Science.gov (United States)

Zhang, Lingli; Zeng, Li; Guo, Yumeng

2018-03-15

Restricted by the scanning environment in some CT imaging modalities, the acquired projection data are usually incomplete, which may lead to a limited-angle reconstruction problem. Thus, image quality usually suffers from the slope artifacts. The objective of this study is to first investigate the distorted domains of the reconstructed images which encounter the slope artifacts and then present a new iterative reconstruction method to address the limited-angle X-ray CT reconstruction problem. The presented framework of new method exploits the structural similarity between the prior image and the reconstructed image aiming to compensate the distorted edges. Specifically, the new method utilizes l0 regularization and wavelet tight framelets to suppress the slope artifacts and pursue the sparsity. New method includes following 4 steps to (1) address the data fidelity using SART; (2) compensate for the slope artifacts due to the missed projection data using the prior image and modified nonlocal means (PNLM); (3) utilize l0 regularization to suppress the slope artifacts and pursue the sparsity of wavelet coefficients of the transformed image by using iterative hard thresholding (l0W); and (4) apply an inverse wavelet transform to reconstruct image. In summary, this method is referred to as "l0W-PNLM". Numerical implementations showed that the presented l0W-PNLM was superior to suppress the slope artifacts while preserving the edges of some features as compared to the commercial and other popular investigative algorithms. When the image to be reconstructed is inconsistent with the prior image, the new method can avoid or minimize the distorted edges in the reconstructed images. Quantitative assessments also showed that applying the new method obtained the highest image quality comparing to the existing algorithms. This study demonstrated that the presented l0W-PNLM yielded higher image quality due to a number of unique characteristics, which include that (1) it utilizes

Iterative Reconstruction Methods for Hybrid Inverse Problems in Impedance Tomography

DEFF Research Database (Denmark)

Hoffmann, Kristoffer; Knudsen, Kim

2014-01-01

For a general formulation of hybrid inverse problems in impedance tomography the Picard and Newton iterative schemes are adapted and four iterative reconstruction algorithms are developed. The general problem formulation includes several existing hybrid imaging modalities such as current density...... impedance imaging, magnetic resonance electrical impedance tomography, and ultrasound modulated electrical impedance tomography, and the unified approach to the reconstruction problem encompasses several algorithms suggested in the literature. The four proposed algorithms are implemented numerically in two...

COMPARISON OF HOLOGRAPHIC AND ITERATIVE METHODS FOR AMPLITUDE OBJECT RECONSTRUCTION

Directory of Open Access Journals (Sweden)

I. A. Shevkunov

2015-01-01

Full Text Available Experimental comparison of four methods for the wavefront reconstruction is presented. We considered two iterative and two holographic methods with different mathematical models and algorithms for recovery. The first two of these methods do not use a reference wave recording scheme that reduces requirements for stability of the installation. A major role in phase information reconstruction by such methods is played by a set of spatial intensity distributions, which are recorded as the recording matrix is being moved along the optical axis. The obtained data are used consistently for wavefront reconstruction using an iterative procedure. In the course of this procedure numerical distribution of the wavefront between the planes is performed. Thus, phase information of the wavefront is stored in every plane and calculated amplitude distributions are replaced for the measured ones in these planes. In the first of the compared methods, a two-dimensional Fresnel transform and iterative calculation in the object plane are used as a mathematical model. In the second approach, an angular spectrum method is used for numerical wavefront propagation, and the iterative calculation is carried out only between closely located planes of data registration. Two digital holography methods, based on the usage of the reference wave in the recording scheme and differing from each other by numerical reconstruction algorithm of digital holograms, are compared with the first two methods. The comparison proved that the iterative method based on 2D Fresnel transform gives results comparable with the result of common holographic method with the Fourier-filtering. It is shown that holographic method for reconstructing of the object complex amplitude in the process of the object amplitude reduction is the best among considered ones.

Image quality in children with low-radiation chest CT using adaptive statistical iterative reconstruction and model-based iterative reconstruction.

Directory of Open Access Journals (Sweden)

Jihang Sun

Full Text Available OBJECTIVE: To evaluate noise reduction and image quality improvement in low-radiation dose chest CT images in children using adaptive statistical iterative reconstruction (ASIR and a full model-based iterative reconstruction (MBIR algorithm. METHODS: Forty-five children (age ranging from 28 days to 6 years, median of 1.8 years who received low-dose chest CT scans were included. Age-dependent noise index (NI was used for acquisition. Images were retrospectively reconstructed using three methods: MBIR, 60% of ASIR and 40% of conventional filtered back-projection (FBP, and FBP. The subjective quality of the images was independently evaluated by two radiologists. Objective noises in the left ventricle (LV, muscle, fat, descending aorta and lung field at the layer with the largest cross-section area of LV were measured, with the region of interest about one fourth to half of the area of descending aorta. Optimized signal-to-noise ratio (SNR was calculated. RESULT: In terms of subjective quality, MBIR images were significantly better than ASIR and FBP in image noise and visibility of tiny structures, but blurred edges were observed. In terms of objective noise, MBIR and ASIR reconstruction decreased the image noise by 55.2% and 31.8%, respectively, for LV compared with FBP. Similarly, MBIR and ASIR reconstruction increased the SNR by 124.0% and 46.2%, respectively, compared with FBP. CONCLUSION: Compared with FBP and ASIR, overall image quality and noise reduction were significantly improved by MBIR. MBIR image could reconstruct eligible chest CT images in children with lower radiation dose.

An iterative reconstruction of cosmological initial density fields

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Hada, Ryuichiro; Eisenstein, Daniel J.

2018-05-01

We present an iterative method to reconstruct the linear-theory initial conditions from the late-time cosmological matter density field, with the intent of improving the recovery of the cosmic distance scale from the baryon acoustic oscillations (BAOs). We present tests using the dark matter density field in both real and redshift space generated from an N-body simulation. In redshift space at z = 0.5, we find that the reconstructed displacement field using our iterative method are more than 80% correlated with the true displacement field of the dark matter particles on scales k < 0.10h Mpc-1. Furthermore, we show that the two-point correlation function of our reconstructed density field matches that of the initial density field substantially better, especially on small scales (<40h-1 Mpc). Our redshift-space results are improved if we use an anisotropic smoothing so as to account for the reduced small-scale information along the line of sight in redshift space.

Analysis of the iteratively regularized Gauss-Newton method under a heuristic rule

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Jin, Qinian; Wang, Wei

2018-03-01

The iteratively regularized Gauss-Newton method is one of the most prominent regularization methods for solving nonlinear ill-posed inverse problems when the data is corrupted by noise. In order to produce a useful approximate solution, this iterative method should be terminated properly. The existing a priori and a posteriori stopping rules require accurate information on the noise level, which may not be available or reliable in practical applications. In this paper we propose a heuristic selection rule for this regularization method, which requires no information on the noise level. By imposing certain conditions on the noise, we derive a posteriori error estimates on the approximate solutions under various source conditions. Furthermore, we establish a convergence result without using any source condition. Numerical results are presented to illustrate the performance of our heuristic selection rule.

Novel iterative reconstruction method with optimal dose usage for partially redundant CT-acquisition

International Nuclear Information System (INIS)

Bruder, H; Raupach, R; Sunnegardh, J; Allmendinger, T; Klotz, E; Stierstorfer, K; Flohr, T

2015-01-01

In CT imaging, a variety of applications exist which are strongly SNR limited. However, in some cases redundant data of the same body region provide additional quanta.Examples: in dual energy CT, the spatial resolution has to be compromised to provide good SNR for material decomposition. However, the respective spectral dataset of the same body region provides additional quanta which might be utilized to improve SNR of each spectral component. Perfusion CT is a high dose application, and dose reduction is highly desirable. However, a meaningful evaluation of perfusion parameters might be impaired by noisy time frames. On the other hand, the SNR of the average of all time frames is extremely high.In redundant CT acquisitions, multiple image datasets can be reconstructed and averaged to composite image data. These composite image data, however, might be compromised with respect to contrast resolution and/or spatial resolution and/or temporal resolution. These observations bring us to the idea of transferring high SNR of composite image data to low SNR ‘source’ image data, while maintaining their resolution.It has been shown that the noise characteristics of CT image data can be improved by iterative reconstruction (Popescu et al 2012 Book of Abstracts, 2nd CT Meeting (Salt Lake City, UT) p 148). In case of data dependent Gaussian noise it can be modelled with image-based iterative reconstruction at least in an approximate manner (Bruder et al 2011 Proc. SPIE 7961 79610J).We present a generalized update equation in image space, consisting of a linear combination of the previous update, a correction term which is constrained by the source image data, and a regularization prior, which is initialized by the composite image data. This iterative reconstruction approach we call bimodal reconstruction (BMR).Based on simulation data it is shown that BMR can improve low contrast detectability, substantially reduces the noise power and has the potential to recover spatial

Novel iterative reconstruction method with optimal dose usage for partially redundant CT-acquisition

Science.gov (United States)

Bruder, H.; Raupach, R.; Sunnegardh, J.; Allmendinger, T.; Klotz, E.; Stierstorfer, K.; Flohr, T.

2015-11-01

In CT imaging, a variety of applications exist which are strongly SNR limited. However, in some cases redundant data of the same body region provide additional quanta. Examples: in dual energy CT, the spatial resolution has to be compromised to provide good SNR for material decomposition. However, the respective spectral dataset of the same body region provides additional quanta which might be utilized to improve SNR of each spectral component. Perfusion CT is a high dose application, and dose reduction is highly desirable. However, a meaningful evaluation of perfusion parameters might be impaired by noisy time frames. On the other hand, the SNR of the average of all time frames is extremely high. In redundant CT acquisitions, multiple image datasets can be reconstructed and averaged to composite image data. These composite image data, however, might be compromised with respect to contrast resolution and/or spatial resolution and/or temporal resolution. These observations bring us to the idea of transferring high SNR of composite image data to low SNR ‘source’ image data, while maintaining their resolution. It has been shown that the noise characteristics of CT image data can be improved by iterative reconstruction (Popescu et al 2012 Book of Abstracts, 2nd CT Meeting (Salt Lake City, UT) p 148). In case of data dependent Gaussian noise it can be modelled with image-based iterative reconstruction at least in an approximate manner (Bruder et al 2011 Proc. SPIE 7961 79610J). We present a generalized update equation in image space, consisting of a linear combination of the previous update, a correction term which is constrained by the source image data, and a regularization prior, which is initialized by the composite image data. This iterative reconstruction approach we call bimodal reconstruction (BMR). Based on simulation data it is shown that BMR can improve low contrast detectability, substantially reduces the noise power and has the potential to recover

A general class of preconditioners for statistical iterative reconstruction of emission computed tomography

International Nuclear Information System (INIS)

Chinn, G.; Huang, S.C.

1997-01-01

A major drawback of statistical iterative image reconstruction for emission computed tomography is its high computational cost. The ill-posed nature of tomography leads to slow convergence for standard gradient-based iterative approaches such as the steepest descent or the conjugate gradient algorithm. In this paper new theory and methods for a class of preconditioners are developed for accelerating the convergence rate of iterative reconstruction. To demonstrate the potential of this class of preconditioners, a preconditioned conjugate gradient (PCG) iterative algorithm for weighted least squares reconstruction (WLS) was formulated for emission tomography. Using simulated positron emission tomography (PET) data of the Hoffman brain phantom, it was shown that the convergence rate of the PCG can reduce the number of iterations of the standard conjugate gradient algorithm by a factor of 2--8 times depending on the convergence criterion

Dynamic PET image reconstruction integrating temporal regularization associated with respiratory motion correction for applications in oncology

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Merlin, Thibaut; Visvikis, Dimitris; Fernandez, Philippe; Lamare, Frédéric

2018-02-01

Respiratory motion reduces both the qualitative and quantitative accuracy of PET images in oncology. This impact is more significant for quantitative applications based on kinetic modeling, where dynamic acquisitions are associated with limited statistics due to the necessity of enhanced temporal resolution. The aim of this study is to address these drawbacks, by combining a respiratory motion correction approach with temporal regularization in a unique reconstruction algorithm for dynamic PET imaging. Elastic transformation parameters for the motion correction are estimated from the non-attenuation-corrected PET images. The derived displacement matrices are subsequently used in a list-mode based OSEM reconstruction algorithm integrating a temporal regularization between the 3D dynamic PET frames, based on temporal basis functions. These functions are simultaneously estimated at each iteration, along with their relative coefficients for each image voxel. Quantitative evaluation has been performed using dynamic FDG PET/CT acquisitions of lung cancer patients acquired on a GE DRX system. The performance of the proposed method is compared with that of a standard multi-frame OSEM reconstruction algorithm. The proposed method achieved substantial improvements in terms of noise reduction while accounting for loss of contrast due to respiratory motion. Results on simulated data showed that the proposed 4D algorithms led to bias reduction values up to 40% in both tumor and blood regions for similar standard deviation levels, in comparison with a standard 3D reconstruction. Patlak parameter estimations on reconstructed images with the proposed reconstruction methods resulted in 30% and 40% bias reduction in the tumor and lung region respectively for the Patlak slope, and a 30% bias reduction for the intercept in the tumor region (a similar Patlak intercept was achieved in the lung area). Incorporation of the respiratory motion correction using an elastic model along with a

Evaluation of robustness of maximum likelihood cone-beam CT reconstruction with total variation regularization

International Nuclear Information System (INIS)

Stsepankou, D; Arns, A; Hesser, J; Ng, S K; Zygmanski, P

2012-01-01

The objective of this paper is to evaluate an iterative maximum likelihood (ML) cone–beam computed tomography (CBCT) reconstruction with total variation (TV) regularization with respect to the robustness of the algorithm due to data inconsistencies. Three different and (for clinical application) typical classes of errors are considered for simulated phantom and measured projection data: quantum noise, defect detector pixels and projection matrix errors. To quantify those errors we apply error measures like mean square error, signal-to-noise ratio, contrast-to-noise ratio and streak indicator. These measures are derived from linear signal theory and generalized and applied for nonlinear signal reconstruction. For quality check, we focus on resolution and CT-number linearity based on a Catphan phantom. All comparisons are made versus the clinical standard, the filtered backprojection algorithm (FBP). In our results, we confirm and substantially extend previous results on iterative reconstruction such as massive undersampling of the number of projections. Errors of projection matrix parameters of up to 1° projection angle deviations are still in the tolerance level. Single defect pixels exhibit ring artifacts for each method. However using defect pixel compensation, allows up to 40% of defect pixels for passing the standard clinical quality check. Further, the iterative algorithm is extraordinarily robust in the low photon regime (down to 0.05 mAs) when compared to FPB, allowing for extremely low-dose image acquisitions, a substantial issue when considering daily CBCT imaging for position correction in radiotherapy. We conclude that the ML method studied herein is robust under clinical quality assurance conditions. Consequently, low-dose regime imaging, especially for daily patient localization in radiation therapy is possible without change of the current hardware of the imaging system. (paper)

Iterative reconstruction: how it works, how to apply it

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Seibert, James Anthony [University of California Davis Medical Center, Department of Radiology, Sacramento, CA (United States)

2014-10-15

Computed tomography acquires X-ray projection data from multiple angles though an object to generate a tomographic rendition of its attenuation characteristics. Filtered back projection is a fast, closed analytical solution to the reconstruction process, whereby all projections are equally weighted, but is prone to deliver inadequate image quality when the dose levels are reduced. Iterative reconstruction is an algorithmic method that uses statistical and geometric models to variably weight the image data in a process that can be solved iteratively to independently reduce noise and preserve resolution and image quality. Applications of this technology in a clinical setting can result in lower dose on the order of 20-40% compared to a standard filtered back projection reconstruction for most exams. A carefully planned implementation strategy and methodological approach is necessary to achieve the goals of lower dose with uncompromised image quality. (orig.)

Quantitative evaluation of ASiR image quality: an adaptive statistical iterative reconstruction technique

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Van de Casteele, Elke; Parizel, Paul; Sijbers, Jan

2012-03-01

Adaptive statistical iterative reconstruction (ASiR) is a new reconstruction algorithm used in the field of medical X-ray imaging. This new reconstruction method combines the idealized system representation, as we know it from the standard Filtered Back Projection (FBP) algorithm, and the strength of iterative reconstruction by including a noise model in the reconstruction scheme. It studies how noise propagates through the reconstruction steps, feeds this model back into the loop and iteratively reduces noise in the reconstructed image without affecting spatial resolution. In this paper the effect of ASiR on the contrast to noise ratio is studied using the low contrast module of the Catphan phantom. The experiments were done on a GE LightSpeed VCT system at different voltages and currents. The results show reduced noise and increased contrast for the ASiR reconstructions compared to the standard FBP method. For the same contrast to noise ratio the images from ASiR can be obtained using 60% less current, leading to a reduction in dose of the same amount.

FIRST: Fast Iterative Reconstruction Software for (PET) tomography

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Herraiz, J L [Dpto. Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain); Espana, S [Dpto. Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain); Vaquero, J J [Unidad de Medicina y CirugIa Experimental, Hospital GU Gregorio Maranon, Madrid (Spain); Desco, M [Unidad de Medicina y CirugIa Experimental, Hospital GU Gregorio Maranon, Madrid (Spain); UdIas, J M [Dpto. Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain)

2006-09-21

Small animal PET scanners require high spatial resolution and good sensitivity. To reconstruct high-resolution images in 3D-PET, iterative methods, such as OSEM, are superior to analytical reconstruction algorithms, although their high computational cost is still a serious drawback. The higher performance of modern computers could make iterative image reconstruction fast enough to be viable, provided we are able to deal with the large number of probability coefficients for the system response matrix in high-resolution PET scanners, which is a difficult task that prevents the algorithms from reaching peak computing performance. Considering all possible axial and in-plane symmetries, as well as certain quasi-symmetries, we have been able to reduce the memory requirements to store the system response matrix (SRM) well below 1 GB, which allows us to keep the whole response matrix of the system inside RAM of ordinary industry-standard computers, so that the reconstruction algorithm can achieve near peak performance. The elements of the SRM are stored as cubic spline profiles and matched to voxel size during reconstruction. In this way, the advantages of 'on-the-fly' calculation and of fully stored SRM are combined. The on-the-fly part of the calculation (matching the profile functions to voxel size) of the SRM accounts for 10-30% of the reconstruction time, depending on the number of voxels chosen. We tested our approach with real data from a commercial small animal PET scanner. The results (image quality and reconstruction time) show that the proposed technique is a feasible solution.

FIRST: Fast Iterative Reconstruction Software for (PET) tomography

International Nuclear Information System (INIS)

Herraiz, J L; Espana, S; Vaquero, J J; Desco, M; UdIas, J M

2006-01-01

Small animal PET scanners require high spatial resolution and good sensitivity. To reconstruct high-resolution images in 3D-PET, iterative methods, such as OSEM, are superior to analytical reconstruction algorithms, although their high computational cost is still a serious drawback. The higher performance of modern computers could make iterative image reconstruction fast enough to be viable, provided we are able to deal with the large number of probability coefficients for the system response matrix in high-resolution PET scanners, which is a difficult task that prevents the algorithms from reaching peak computing performance. Considering all possible axial and in-plane symmetries, as well as certain quasi-symmetries, we have been able to reduce the memory requirements to store the system response matrix (SRM) well below 1 GB, which allows us to keep the whole response matrix of the system inside RAM of ordinary industry-standard computers, so that the reconstruction algorithm can achieve near peak performance. The elements of the SRM are stored as cubic spline profiles and matched to voxel size during reconstruction. In this way, the advantages of 'on-the-fly' calculation and of fully stored SRM are combined. The on-the-fly part of the calculation (matching the profile functions to voxel size) of the SRM accounts for 10-30% of the reconstruction time, depending on the number of voxels chosen. We tested our approach with real data from a commercial small animal PET scanner. The results (image quality and reconstruction time) show that the proposed technique is a feasible solution

DQS advisor: a visual interface and knowledge-based system to balance dose, quality, and reconstruction speed in iterative CT reconstruction with application to NLM-regularization

International Nuclear Information System (INIS)

Zheng, Z; Papenhausen, E; Mueller, K

2013-01-01

Motivated by growing concerns with regards to the x-ray dose delivered to the patient, low-dose computed tomography (CT) has gained substantial interest in recent years. However, achieving high-quality CT reconstructions from the limited projection data collected at reduced x-ray radiation is challenging, and iterative algorithms have been shown to perform much better than conventional analytical schemes in these cases. A problem with iterative methods in general is that they require users to set many parameters, and if set incorrectly high reconstruction time and/or low image quality are likely consequences. Since the interactions among parameters can be complex and thus effective settings can be difficult to identify for a given scanning scenario, these choices are often left to a highly-experienced human expert. To help alleviate this problem, we devise a computer-based assistant for this purpose, called dose, quality and speed (DQS)-advisor. It allows users to balance the three most important CT metrics–-DQS-–by ways of an intuitive visual interface. Using a known gold-standard, the system uses the ant-colony optimization algorithm to generate the most effective parameter settings for a comprehensive set of DQS configurations. A visual interface then presents the numerical outcome of this optimization, while a matrix display allows users to compare the corresponding images. The interface allows users to intuitively trade-off GPU-enabled reconstruction speed with quality and dose, while the system picks the associated parameter settings automatically. Further, once the knowledge has been generated, it can be used to correctly set the parameters for any new CT scan taken at similar scenarios. (paper)

A new iterative algorithm to reconstruct the refractive index.

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Liu, Y J; Zhu, P P; Chen, B; Wang, J Y; Yuan, Q X; Huang, W X; Shu, H; Li, E R; Liu, X S; Zhang, K; Ming, H; Wu, Z Y

2007-06-21

The latest developments in x-ray imaging are associated with techniques based on the phase contrast. However, the image reconstruction procedures demand significant improvements of the traditional methods, and/or new algorithms have to be introduced to take advantage of the high contrast and sensitivity of the new experimental techniques. In this letter, an improved iterative reconstruction algorithm based on the maximum likelihood expectation maximization technique is presented and discussed in order to reconstruct the distribution of the refractive index from data collected by an analyzer-based imaging setup. The technique considered probes the partial derivative of the refractive index with respect to an axis lying in the meridional plane and perpendicular to the propagation direction. Computer simulations confirm the reliability of the proposed algorithm. In addition, the comparison between an analytical reconstruction algorithm and the iterative method has been also discussed together with the convergent characteristic of this latter algorithm. Finally, we will show how the proposed algorithm may be applied to reconstruct the distribution of the refractive index of an epoxy cylinder containing small air bubbles of about 300 micro of diameter.

Effect of hybrid iterative reconstruction technique on quantitative and qualitative image analysis at 256-slice prospective gating cardiac CT

International Nuclear Information System (INIS)

Utsunomiya, Daisuke; Weigold, W. Guy; Weissman, Gaby; Taylor, Allen J.

2012-01-01

To evaluate the effect of hybrid iterative reconstruction on qualitative and quantitative parameters at 256-slice cardiac CT. Prospective cardiac CT images from 20 patients were analysed. Paired image sets were created using 3 reconstructions, i.e. filtered back projection (FBP) and moderate- and high-level iterative reconstructions. Quantitative parameters including CT-attenuation, noise, and contrast-to-noise ratio (CNR) were determined in both proximal- and distal coronary segments. Image quality was graded on a 4-point scale. Coronary CT attenuation values were similar for FBP, moderate- and high-level iterative reconstruction at 293 ± 74-, 290 ± 75-, and 283 ± 78 Hounsfield units (HU), respectively. CNR was significantly higher with moderate- and high-level iterative reconstructions (10.9 ± 3.5 and 18.4 ± 6.2, respectively) than FBP (8.2 ± 2.5) as was the visual grading of proximal vessels. Visualisation of distal vessels was better with high-level iterative reconstruction than FBP. The mean number of assessable segments among 289 segments was 245, 260, and 267 for FBP, moderate- and high-level iterative reconstruction, respectively; the difference between FBP and high-level iterative reconstruction was significant. Interobserver agreement was significantly higher for moderate- and high-level iterative reconstruction than FBP. Cardiac CT using hybrid iterative reconstruction yields higher CNR and better image quality than FBP. circle Cardiac CT helps clinicians to assess patients with coronary artery disease circle Hybrid iterative reconstruction provides improved cardiac CT image quality circle Hybrid iterative reconstruction improves the number of assessable coronary segments circle Hybrid iterative reconstruction improves interobserver agreement on cardiac CT. (orig.)

A combined reconstruction-classification method for diffuse optical tomography

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Hiltunen, P [Department of Biomedical Engineering and Computational Science, Helsinki University of Technology, PO Box 3310, FI-02015 TKK (Finland); Prince, S J D; Arridge, S [Department of Computer Science, University College London, Gower Street London, WC1E 6B (United Kingdom)], E-mail: petri.hiltunen@tkk.fi, E-mail: s.prince@cs.ucl.ac.uk, E-mail: s.arridge@cs.ucl.ac.uk

2009-11-07

We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.

Iterative CT reconstruction with correction for known rigid motion

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Nuyts, Johan [Katholieke Univ. Leuven (Belgium). Dept. of Nuclear Medicine; Kim, Jung-Ha; Fulton, Roger [Sydney Univ., NSW (Australia). School of Physics; Westmead Hospital, Sydney (Australia). Medical Physics

2011-07-01

In PET/CT brain imaging, correction for motion may be needed, in particular for children and psychiatric patients. Motion is more likely to occur in the lengthy PET measurement, but also during the short CT acquisition patient motion is possible. Rigid motion of the head can be measured independently from the PET/CT system with optical devices. In this paper, we propose a method and some preliminary simulation results for iterative CT reconstruction with correction for known rigid motion. We implemented an iterative algorithm for fully 3D reconstruction from helical CT scans. The motion of the head is incorporated in the system matrix as a view-dependent motion of the CT-system. The first simulation results indicate that some motion patterns may produce loss of essential data. This loss precludes exact reconstruction and results in artifacts in the reconstruction, even when motion is taken into account. However, by reducing the pitch during acquisition, the same motion pattern no longer caused artifacts in the motion corrected image. (orig.)

A comparison in the reconstruction of neutron spectrums using classical iterative techniques

International Nuclear Information System (INIS)

Ortiz R, J. M.; Martinez B, M. R.; Vega C, H. R.; Gallego, E.

2009-10-01

One of the key drawbacks to the use of BUNKI code is that the process begins the reconstruction of the spectrum based on a priori knowledge as close as possible to the solution that is sought. The user has to specify the initial spectrum or do it through a subroutine called MAXIET to calculate a Maxwellian and a 1/E spectrum as initial spectrum. Because the application of iterative procedures by to resolve the reconstruction of neutron spectrum needs an initial spectrum, it is necessary to have new proposals for the election of the same. Based on the experience gained with a widely used method of reconstruction, called BUNKI, has developed a new computational tools for neutron spectrometry and dosimetry, which was first introduced, which operates by means of an iterative algorithm for the reconstruction of neutron spectra. The main feature of this tool is that unlike the existing iterative codes, the choice of the initial spectrum is performed automatically by the program, through a neutron spectra catalog. To develop the code, the algorithm was selected as the routine iterative SPUNIT be used in computing tool and response matrix UTA4 for 31 energy groups. (author)

Iterative Reconstruction Techniques in Abdominopelvic CT: Technical Concepts and Clinical Implementation.

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Patino, Manuel; Fuentes, Jorge M; Singh, Sarabjeet; Hahn, Peter F; Sahani, Dushyant V

2015-07-01

This article discusses the clinical challenge of low-radiation-dose examinations, the commonly used approaches for dose optimization, and their effect on image quality. We emphasize practical aspects of the different iterative reconstruction techniques, along with their benefits, pitfalls, and clinical implementation. The widespread use of CT has raised concerns about potential radiation risks, motivating diverse strategies to reduce the radiation dose associated with CT. CT manufacturers have developed alternative reconstruction algorithms intended to improve image quality on dose-optimized CT studies, mainly through noise and artifact reduction. Iterative reconstruction techniques take unique approaches to noise reduction and provide distinct strength levels or settings.

Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique

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Katsura, Masaki; Matsuda, Izuru; Akahane, Masaaki; Sato, Jiro; Akai, Hiroyuki; Yasaka, Koichiro; Kunimatsu, Akira; Ohtomo, Kuni [University of Tokyo, Department of Radiology, Graduate School of Medicine, Bunkyo-ku, Tokyo (Japan)

2012-08-15

To prospectively evaluate dose reduction and image quality characteristics of chest CT reconstructed with model-based iterative reconstruction (MBIR) compared with adaptive statistical iterative reconstruction (ASIR). One hundred patients underwent reference-dose and low-dose unenhanced chest CT with 64-row multidetector CT. Images were reconstructed with 50 % ASIR-filtered back projection blending (ASIR50) for reference-dose CT, and with ASIR50 and MBIR for low-dose CT. Two radiologists assessed the images in a blinded manner for subjective image noise, artefacts and diagnostic acceptability. Objective image noise was measured in the lung parenchyma. Data were analysed using the sign test and pair-wise Student's t-test. Compared with reference-dose CT, there was a 79.0 % decrease in dose-length product with low-dose CT. Low-dose MBIR images had significantly lower objective image noise (16.93 {+-} 3.00) than low-dose ASIR (49.24 {+-} 9.11, P < 0.01) and reference-dose ASIR images (24.93 {+-} 4.65, P < 0.01). Low-dose MBIR images were all diagnostically acceptable. Unique features of low-dose MBIR images included motion artefacts and pixellated blotchy appearances, which did not adversely affect diagnostic acceptability. Diagnostically acceptable chest CT images acquired with nearly 80 % less radiation can be obtained using MBIR. MBIR shows greater potential than ASIR for providing diagnostically acceptable low-dose CT images without severely compromising image quality. (orig.)

Reducing the effects of acoustic heterogeneity with an iterative reconstruction method from experimental data in microwave induced thermoacoustic tomography

International Nuclear Information System (INIS)

Wang, Jinguo; Zhao, Zhiqin; Song, Jian; Chen, Guoping; Nie, Zaiping; Liu, Qing-Huo

2015-01-01

Purpose: An iterative reconstruction method has been previously reported by the authors of this paper. However, the iterative reconstruction method was demonstrated by solely using the numerical simulations. It is essential to apply the iterative reconstruction method to practice conditions. The objective of this work is to validate the capability of the iterative reconstruction method for reducing the effects of acoustic heterogeneity with the experimental data in microwave induced thermoacoustic tomography. Methods: Most existing reconstruction methods need to combine the ultrasonic measurement technology to quantitatively measure the velocity distribution of heterogeneity, which increases the system complexity. Different to existing reconstruction methods, the iterative reconstruction method combines time reversal mirror technique, fast marching method, and simultaneous algebraic reconstruction technique to iteratively estimate the velocity distribution of heterogeneous tissue by solely using the measured data. Then, the estimated velocity distribution is used subsequently to reconstruct the highly accurate image of microwave absorption distribution. Experiments that a target placed in an acoustic heterogeneous environment are performed to validate the iterative reconstruction method. Results: By using the estimated velocity distribution, the target in an acoustic heterogeneous environment can be reconstructed with better shape and higher image contrast than targets that are reconstructed with a homogeneous velocity distribution. Conclusions: The distortions caused by the acoustic heterogeneity can be efficiently corrected by utilizing the velocity distribution estimated by the iterative reconstruction method. The advantage of the iterative reconstruction method over the existing correction methods is that it is successful in improving the quality of the image of microwave absorption distribution without increasing the system complexity

A convergence analysis of the iteratively regularized Gauss–Newton method under the Lipschitz condition

International Nuclear Information System (INIS)

Jin Qinian

2008-01-01

In this paper we consider the iteratively regularized Gauss–Newton method for solving nonlinear ill-posed inverse problems. Under merely the Lipschitz condition, we prove that this method together with an a posteriori stopping rule defines an order optimal regularization method if the solution is regular in some suitable sense

Objective task-based assessment of low-contrast detectability in iterative reconstruction

International Nuclear Information System (INIS)

Racine, Damien; Ott, Julien G.; Ba, Alexandre; Ryckx, Nick; Bochud, Francois O.; Verdun, Francis R.

2016-01-01

Evaluating image quality by using receiver operating characteristic studies is time consuming and difficult to implement. This work assesses a new iterative algorithm using a channelised Hotelling observer (CHO). For this purpose, an anthropomorphic abdomen phantom with spheres of various sizes and contrasts was scanned at 3 volume computed tomography dose index (CTDI vol ) levels on a GE Revolution CT. Images were reconstructed using the iterative reconstruction method adaptive statistical iterative reconstruction-V (ASIR-V) at ASIR-V 0, 50 and 70 % and assessed by applying a CHO with dense difference of Gaussian and internal noise. Both CHO and human observers (HO) were compared based on a four-alternative forced-choice experiment, using the percentage correct as a figure of merit. The results showed accordance between CHO and HO. Moreover, an improvement in the low-contrast detection was observed when switching from ASIR-V 0 to 50 %. The results underpin the finding that ASIR-V allows dose reduction. (authors)

On convergence rates for iteratively regularized procedures with linear penalty terms

International Nuclear Information System (INIS)

Smirnova, Alexandra

2012-01-01

The impact of this paper is twofold. First, we study convergence rates of the iteratively regularized Gauss–Newton (IRGN) algorithm with a linear penalty term under a generalized source assumption and show how the regularizing properties of new iterations depend on the solution smoothness. Secondly, we introduce an adaptive IRGN procedure, which is investigated under a relaxed smoothness condition. The introduction and analysis of a more general penalty term are of great importance since, apart from bringing stability to the numerical scheme designed for solving a large class of applied inverse problems, it allows us to incorporate various types of a priori information available on the model. Both a priori and a posteriori stopping rules are investigated. For the a priori stopping rule, optimal convergence rates are derived. A numerical example illustrating convergence rates is considered. (paper)

Nonlocal Regularized Algebraic Reconstruction Techniques for MRI: An Experimental Study

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Xin Li

2013-01-01

Full Text Available We attempt to revitalize researchers' interest in algebraic reconstruction techniques (ART by expanding their capabilities and demonstrating their potential in speeding up the process of MRI acquisition. Using a continuous-to-discrete model, we experimentally study the application of ART into MRI reconstruction which unifies previous nonuniform-fast-Fourier-transform- (NUFFT- based and gridding-based approaches. Under the framework of ART, we advocate the use of nonlocal regularization techniques which are leveraged from our previous research on modeling photographic images. It is experimentally shown that nonlocal regularization ART (NR-ART can often outperform their local counterparts in terms of both subjective and objective qualities of reconstructed images. On one real-world k-space data set, we find that nonlocal regularization can achieve satisfactory reconstruction from as few as one-third of samples. We also address an issue related to image reconstruction from real-world k-space data but overlooked in the open literature: the consistency of reconstructed images across different resolutions. A resolution-consistent extension of NR-ART is developed and shown to effectively suppress the artifacts arising from frequency extrapolation. Both source codes and experimental results of this work are made fully reproducible.

CT colonography at low tube potential: using iterative reconstruction to decrease noise

International Nuclear Information System (INIS)

Chang, K.J.; Heisler, M.A.; Mahesh, M.; Baird, G.L.; Mayo-Smith, W.W.

2015-01-01

Aim: To determine the level of iterative reconstruction required to reduce increased image noise associated with low tube potential computed tomography (CT). Materials and methods: Fifty patients underwent CT colonography with a supine scan at 120 kVp and a prone scan at 100 kVp with other scan parameters unchanged. Both scans were reconstructed with filtered back projection (FBP) and increasing levels of adaptive statistical iterative reconstruction (ASiR) at 30%, 60%, and 90%. Mean noise, soft tissue and tagged fluid attenuation, contrast, and contrast-to-noise ratio (CNR) were collected from reconstructions at both 120 and 100 kVp and compared using a generalised linear mixed model. Results: Decreasing tube potential from 120 to 100 kVp significantly increased image noise by 30–34% and tagged fluid attenuation by 120 HU at all ASiR levels (p<0.0001, all measures). Increasing ASiR from 0% (FBP) to 30%, 60%, and 90% resulted in significant decreases in noise and increases in CNR at both tube potentials (p<0.001, all comparisons). Compared to 120 kVp FBP, ASiR greater than 30% at 100 kVp yielded similar or lower image noise. Conclusions: Iterative reconstruction adequately compensates for increased image noise associated with low tube potential imaging while improving CNR. An ASiR level of approximately 50% at 100 kVp yields similar noise to 120 kVp without ASiR. -- Highlights: •Peak kilovoltage (kVp) can be reduced to decrease radiation dose and increase contrast attenuation at a cost of increased image noise. •Utilizing iterative reconstruction can decrease image noise and increase contrast to noise ratio (CNR) independent of kVp. •Iterative reconstruction adequately compensates for increased image noise associated with low dose low kVp imaging while improving CNR. •An ASiR level of approximately 50% at 100 kVp yields similar noise to 120 kVp without ASiR

Clinical applications of iterative reconstruction

International Nuclear Information System (INIS)

Eberl, S.

1998-01-01

Expectation maximisation (EM) reconstruction largely eliminates the hot and cold streaking artifacts characteristic of filtered-back projection (FBP) reconstruction around localised hot areas, such as the bladder. It also substantially reduces the problem of decreased inferior wall counts in MIBI myocardial perfusion studies due to ''streaking'' from high liver uptake. Non-uniform attenuation and scatter correction, resolution recovery, anatomical information, e.g. from MRI or CT tracer kinetic modelling, can all be built into the EM reconstruction imaging model. The properties of ordered subset EM (OSEM) have also been used to correct for known patient motion as part of the reconstruction process. These uses of EM are elaborated more fully in some of the other abstracts of this meeting. Currently we use OSEM routinely for: (i) studies where streaking is a problem, including all MIBI myocardial perfusion studies, to avoid hot liver inferior wall artifact, (ii) all whole body FDG PET, all lung V/Q SPECT (which have a short acquisition time) and all gated 201 TI myocardial perfusion studies due to improved noise characteristics of OSEM in these studies; (iii) studies with measured, non-uniform attenuation correction. With the accelerated OSEM algorithm, iterative reconstruction is practical for routine clinical applications and we have found OSEM to provide clearly superior reconstructions for the areas listed above and are investigating its application to other studies. In clinical use, we have not found OSEM to introduce artifacts which would not also occur with FBP, e.g. uncorrected patient motion will cause artifacts with both OSEM and FBP

CT of the chest with model-based, fully iterative reconstruction: comparison with adaptive statistical iterative reconstruction.

Science.gov (United States)

Ichikawa, Yasutaka; Kitagawa, Kakuya; Nagasawa, Naoki; Murashima, Shuichi; Sakuma, Hajime

2013-08-09

The recently developed model-based iterative reconstruction (MBIR) enables significant reduction of image noise and artifacts, compared with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP). The purpose of this study was to evaluate lesion detectability of low-dose chest computed tomography (CT) with MBIR in comparison with ASIR and FBP. Chest CT was acquired with 64-slice CT (Discovery CT750HD) with standard-dose (5.7 ± 2.3 mSv) and low-dose (1.6 ± 0.8 mSv) conditions in 55 patients (aged 72 ± 7 years) who were suspected of lung disease on chest radiograms. Low-dose CT images were reconstructed with MBIR, ASIR 50% and FBP, and standard-dose CT images were reconstructed with FBP, using a reconstructed slice thickness of 0.625 mm. Two observers evaluated the image quality of abnormal lung and mediastinal structures on a 5-point scale (Score 5 = excellent and score 1 = non-diagnostic). The objective image noise was also measured as the standard deviation of CT intensity in the descending aorta. The image quality score of enlarged mediastinal lymph nodes on low-dose MBIR CT (4.7 ± 0.5) was significantly improved in comparison with low-dose FBP and ASIR CT (3.0 ± 0.5, p = 0.004; 4.0 ± 0.5, p = 0.02, respectively), and was nearly identical to the score of standard-dose FBP image (4.8 ± 0.4, p = 0.66). Concerning decreased lung attenuation (bulla, emphysema, or cyst), the image quality score on low-dose MBIR CT (4.9 ± 0.2) was slightly better compared to low-dose FBP and ASIR CT (4.5 ± 0.6, p = 0.01; 4.6 ± 0.5, p = 0.01, respectively). There were no significant differences in image quality scores of visualization of consolidation or mass, ground-glass attenuation, or reticular opacity among low- and standard-dose CT series. Image noise with low-dose MBIR CT (11.6 ± 1.0 Hounsfield units (HU)) were significantly lower than with low-dose ASIR (21.1 ± 2.6 HU, p standard-dose FBP CT (16.6 ± 2.3 HU, p 70%, MBIR can provide

Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique

International Nuclear Information System (INIS)

Katsura, Masaki; Matsuda, Izuru; Akahane, Masaaki; Sato, Jiro; Akai, Hiroyuki; Yasaka, Koichiro; Kunimatsu, Akira; Ohtomo, Kuni

2012-01-01

To prospectively evaluate dose reduction and image quality characteristics of chest CT reconstructed with model-based iterative reconstruction (MBIR) compared with adaptive statistical iterative reconstruction (ASIR). One hundred patients underwent reference-dose and low-dose unenhanced chest CT with 64-row multidetector CT. Images were reconstructed with 50 % ASIR-filtered back projection blending (ASIR50) for reference-dose CT, and with ASIR50 and MBIR for low-dose CT. Two radiologists assessed the images in a blinded manner for subjective image noise, artefacts and diagnostic acceptability. Objective image noise was measured in the lung parenchyma. Data were analysed using the sign test and pair-wise Student's t-test. Compared with reference-dose CT, there was a 79.0 % decrease in dose-length product with low-dose CT. Low-dose MBIR images had significantly lower objective image noise (16.93 ± 3.00) than low-dose ASIR (49.24 ± 9.11, P < 0.01) and reference-dose ASIR images (24.93 ± 4.65, P < 0.01). Low-dose MBIR images were all diagnostically acceptable. Unique features of low-dose MBIR images included motion artefacts and pixellated blotchy appearances, which did not adversely affect diagnostic acceptability. Diagnostically acceptable chest CT images acquired with nearly 80 % less radiation can be obtained using MBIR. MBIR shows greater potential than ASIR for providing diagnostically acceptable low-dose CT images without severely compromising image quality. (orig.)

Computed tomography depiction of small pediatric vessels with model-based iterative reconstruction

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Koc, Gonca; Courtier, Jesse L.; Phelps, Andrew; Marcovici, Peter A.; MacKenzie, John D. [UCSF Benioff Children' s Hospital, Department of Radiology and Biomedical Imaging, San Francisco, CA (United States)

2014-07-15

Computed tomography (CT) is extremely important in characterizing blood vessel anatomy and vascular lesions in children. Recent advances in CT reconstruction technology hold promise for improved image quality and also reductions in radiation dose. This report evaluates potential improvements in image quality for the depiction of small pediatric vessels with model-based iterative reconstruction (Veo trademark), a technique developed to improve image quality and reduce noise. To evaluate Veo trademark as an improved method when compared to adaptive statistical iterative reconstruction (ASIR trademark) for the depiction of small vessels on pediatric CT. Seventeen patients (mean age: 3.4 years, range: 2 days to 10.0 years; 6 girls, 11 boys) underwent contrast-enhanced CT examinations of the chest and abdomen in this HIPAA compliant and institutional review board approved study. Raw data were reconstructed into separate image datasets using Veo trademark and ASIR trademark algorithms (GE Medical Systems, Milwaukee, WI). Four blinded radiologists subjectively evaluated image quality. The pulmonary, hepatic, splenic and renal arteries were evaluated for the length and number of branches depicted. Datasets were compared with parametric and non-parametric statistical tests. Readers stated a preference for Veo trademark over ASIR trademark images when subjectively evaluating image quality criteria for vessel definition, image noise and resolution of small anatomical structures. The mean image noise in the aorta and fat was significantly less for Veo trademark vs. ASIR trademark reconstructed images. Quantitative measurements of mean vessel lengths and number of branches vessels delineated were significantly different for Veo trademark and ASIR trademark images. Veo trademark consistently showed more of the vessel anatomy: longer vessel length and more branching vessels. When compared to the more established adaptive statistical iterative reconstruction algorithm, model

Computed tomography depiction of small pediatric vessels with model-based iterative reconstruction

International Nuclear Information System (INIS)

Koc, Gonca; Courtier, Jesse L.; Phelps, Andrew; Marcovici, Peter A.; MacKenzie, John D.

2014-01-01

Computed tomography (CT) is extremely important in characterizing blood vessel anatomy and vascular lesions in children. Recent advances in CT reconstruction technology hold promise for improved image quality and also reductions in radiation dose. This report evaluates potential improvements in image quality for the depiction of small pediatric vessels with model-based iterative reconstruction (Veo trademark), a technique developed to improve image quality and reduce noise. To evaluate Veo trademark as an improved method when compared to adaptive statistical iterative reconstruction (ASIR trademark) for the depiction of small vessels on pediatric CT. Seventeen patients (mean age: 3.4 years, range: 2 days to 10.0 years; 6 girls, 11 boys) underwent contrast-enhanced CT examinations of the chest and abdomen in this HIPAA compliant and institutional review board approved study. Raw data were reconstructed into separate image datasets using Veo trademark and ASIR trademark algorithms (GE Medical Systems, Milwaukee, WI). Four blinded radiologists subjectively evaluated image quality. The pulmonary, hepatic, splenic and renal arteries were evaluated for the length and number of branches depicted. Datasets were compared with parametric and non-parametric statistical tests. Readers stated a preference for Veo trademark over ASIR trademark images when subjectively evaluating image quality criteria for vessel definition, image noise and resolution of small anatomical structures. The mean image noise in the aorta and fat was significantly less for Veo trademark vs. ASIR trademark reconstructed images. Quantitative measurements of mean vessel lengths and number of branches vessels delineated were significantly different for Veo trademark and ASIR trademark images. Veo trademark consistently showed more of the vessel anatomy: longer vessel length and more branching vessels. When compared to the more established adaptive statistical iterative reconstruction algorithm, model

Partial volume effect correction in PET using regularized iterative deconvolution with variance control based on local topology

International Nuclear Information System (INIS)

Kirov, A S; Schmidtlein, C R; Piao, J Z

2008-01-01

Correcting positron emission tomography (PET) images for the partial volume effect (PVE) due to the limited resolution of PET has been a long-standing challenge. Various approaches including incorporation of the system response function in the reconstruction have been previously tested. We present a post-reconstruction PVE correction based on iterative deconvolution using a 3D maximum likelihood expectation-maximization (MLEM) algorithm. To achieve convergence we used a one step late (OSL) regularization procedure based on the assumption of local monotonic behavior of the PET signal following Alenius et al. This technique was further modified to selectively control variance depending on the local topology of the PET image. No prior 'anatomic' information is needed in this approach. An estimate of the noise properties of the image is used instead. The procedure was tested for symmetric and isotropic deconvolution functions with Gaussian shape and full width at half-maximum (FWHM) ranging from 6.31 mm to infinity. The method was applied to simulated and experimental scans of the NEMA NU 2 image quality phantom with the GE Discovery LS PET/CT scanner. The phantom contained uniform activity spheres with diameters ranging from 1 cm to 3.7 cm within uniform background. The optimal sphere activity to variance ratio was obtained when the deconvolution function was replaced by a step function few voxels wide. In this case, the deconvolution method converged in ∼3-5 iterations for most points on both the simulated and experimental images. For the 1 cm diameter sphere, the contrast recovery improved from 12% to 36% in the simulated and from 21% to 55% in the experimental data. Recovery coefficients between 80% and 120% were obtained for all larger spheres, except for the 13 mm diameter sphere in the simulated scan (68%). No increase in variance was observed except for a few voxels neighboring strong activity gradients and inside the largest spheres. Testing the method for

Homotopic non-local regularized reconstruction from sparse positron emission tomography measurements

International Nuclear Information System (INIS)

Wong, Alexander; Liu, Chenyi; Wang, Xiao Yu; Fieguth, Paul; Bie, Hongxia

2015-01-01

Positron emission tomography scanners collect measurements of a patient’s in vivo radiotracer distribution. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule, and the tomograms must be reconstructed from projections. The reconstruction of tomograms from the acquired PET data is an inverse problem that requires regularization. The use of tightly packed discrete detector rings, although improves signal-to-noise ratio, are often associated with high costs of positron emission tomography systems. Thus a sparse reconstruction, which would be capable of overcoming the noise effect while allowing for a reduced number of detectors, would have a great deal to offer. In this study, we introduce and investigate the potential of a homotopic non-local regularization reconstruction framework for effectively reconstructing positron emission tomograms from such sparse measurements. Results obtained using the proposed approach are compared with traditional filtered back-projection as well as expectation maximization reconstruction with total variation regularization. A new reconstruction method was developed for the purpose of improving the quality of positron emission tomography reconstruction from sparse measurements. We illustrate that promising reconstruction performance can be achieved for the proposed approach even at low sampling fractions, which allows for the use of significantly fewer detectors and have the potential to reduce scanner costs

Iterative wave-front reconstruction in the Fourier domain.

Science.gov (United States)

Bond, Charlotte Z; Correia, Carlos M; Sauvage, Jean-François; Neichel, Benoit; Fusco, Thierry

2017-05-15

The use of Fourier methods in wave-front reconstruction can significantly reduce the computation time for large telescopes with a high number of degrees of freedom. However, Fourier algorithms for discrete data require a rectangular data set which conform to specific boundary requirements, whereas wave-front sensor data is typically defined over a circular domain (the telescope pupil). Here we present an iterative Gerchberg routine modified for the purposes of discrete wave-front reconstruction which adapts the measurement data (wave-front sensor slopes) for Fourier analysis, fulfilling the requirements of the fast Fourier transform (FFT) and providing accurate reconstruction. The routine is used in the adaptation step only and can be coupled to any other Wiener-like or least-squares method. We compare simulations using this method with previous Fourier methods and show an increase in performance in terms of Strehl ratio and a reduction in noise propagation for a 40×40 SPHERE-like adaptive optics system. For closed loop operation with minimal iterations the Gerchberg method provides an improvement in Strehl, from 95.4% to 96.9% in K-band. This corresponds to ~ 40 nm improvement in rms, and avoids the high spatial frequency errors present in other methods, providing an increase in contrast towards the edge of the correctable band.

Improvement of brain perfusion SPET using iterative reconstruction with scatter and non-uniform attenuation correction

Energy Technology Data Exchange (ETDEWEB)

Kauppinen, T.; Vanninen, E.; Kuikka, J.T. [Kuopio Central Hospital (Finland). Dept. of Clinical Physiology; Koskinen, M.O. [Dept. of Clinical Physiology and Nuclear Medicine, Tampere Univ. Hospital, Tampere (Finland); Alenius, S. [Signal Processing Lab., Tampere Univ. of Technology, Tampere (Finland)

2000-09-01

Filtered back-projection (FBP) is generally used as the reconstruction method for single-photon emission tomography although it produces noisy images with apparent streak artefacts. It is possible to improve the image quality by using an algorithm with iterative correction steps. The iterative reconstruction technique also has an additional benefit in that computation of attenuation correction can be included in the process. A commonly used iterative method, maximum-likelihood expectation maximisation (ML-EM), can be accelerated using ordered subsets (OS-EM). We have applied to the OS-EM algorithm a Bayesian one-step late correction method utilising median root prior (MRP). Methodological comparison was performed by means of measurements obtained with a brain perfusion phantom and using patient data. The aim of this work was to quantitate the accuracy of iterative reconstruction with scatter and non-uniform attenuation corrections and post-filtering in SPET brain perfusion imaging. SPET imaging was performed using a triple-head gamma camera with fan-beam collimators. Transmission and emission scans were acquired simultaneously. The brain phantom used was a high-resolution three-dimensional anthropomorphic JB003 phantom. Patient studies were performed in ten chronic pain syndrome patients. The images were reconstructed using conventional FBP and iterative OS-EM and MRP techniques including scatter and nonuniform attenuation corrections. Iterative reconstructions were individually post-filtered. The quantitative results obtained with the brain perfusion phantom were compared with the known actual contrast ratios. The calculated difference from the true values was largest with the FBP method; iteratively reconstructed images proved closer to the reality. Similar findings were obtained in the patient studies. The plain OS-EM method improved the contrast whereas in the case of the MRP technique the improvement in contrast was not so evident with post-filtering. (orig.)

Improvement of brain perfusion SPET using iterative reconstruction with scatter and non-uniform attenuation correction

International Nuclear Information System (INIS)

Kauppinen, T.; Vanninen, E.; Kuikka, J.T.; Alenius, S.

2000-01-01

Filtered back-projection (FBP) is generally used as the reconstruction method for single-photon emission tomography although it produces noisy images with apparent streak artefacts. It is possible to improve the image quality by using an algorithm with iterative correction steps. The iterative reconstruction technique also has an additional benefit in that computation of attenuation correction can be included in the process. A commonly used iterative method, maximum-likelihood expectation maximisation (ML-EM), can be accelerated using ordered subsets (OS-EM). We have applied to the OS-EM algorithm a Bayesian one-step late correction method utilising median root prior (MRP). Methodological comparison was performed by means of measurements obtained with a brain perfusion phantom and using patient data. The aim of this work was to quantitate the accuracy of iterative reconstruction with scatter and non-uniform attenuation corrections and post-filtering in SPET brain perfusion imaging. SPET imaging was performed using a triple-head gamma camera with fan-beam collimators. Transmission and emission scans were acquired simultaneously. The brain phantom used was a high-resolution three-dimensional anthropomorphic JB003 phantom. Patient studies were performed in ten chronic pain syndrome patients. The images were reconstructed using conventional FBP and iterative OS-EM and MRP techniques including scatter and nonuniform attenuation corrections. Iterative reconstructions were individually post-filtered. The quantitative results obtained with the brain perfusion phantom were compared with the known actual contrast ratios. The calculated difference from the true values was largest with the FBP method; iteratively reconstructed images proved closer to the reality. Similar findings were obtained in the patient studies. The plain OS-EM method improved the contrast whereas in the case of the MRP technique the improvement in contrast was not so evident with post-filtering. (orig.)

Image quality of iterative reconstruction in cranial CT imaging: comparison of model-based iterative reconstruction (MBIR) and adaptive statistical iterative reconstruction (ASiR)

Energy Technology Data Exchange (ETDEWEB)

Notohamiprodjo, S.; Deak, Z.; Meurer, F.; Maertz, F.; Mueck, F.G.; Geyer, L.L.; Wirth, S. [Ludwig-Maximilians University Hospital of Munich, Institute for Clinical Radiology, Munich (Germany)

2015-01-15

The purpose of this study was to compare cranial CT (CCT) image quality (IQ) of the MBIR algorithm with standard iterative reconstruction (ASiR). In this institutional review board (IRB)-approved study, raw data sets of 100 unenhanced CCT examinations (120 kV, 50-260 mAs, 20 mm collimation, 0.984 pitch) were reconstructed with both ASiR and MBIR. Signal-to-noise (SNR) and contrast-to-noise (CNR) were calculated from attenuation values measured in caudate nucleus, frontal white matter, anterior ventricle horn, fourth ventricle, and pons. Two radiologists, who were blinded to the reconstruction algorithms, evaluated anonymized multiplanar reformations of 2.5 mm with respect to depiction of different parenchymal structures and impact of artefacts on IQ with a five-point scale (0: unacceptable, 1: less than average, 2: average, 3: above average, 4: excellent). MBIR decreased artefacts more effectively than ASiR (p < 0.01). The median depiction score for MBIR was 3, whereas the median value for ASiR was 2 (p < 0.01). SNR and CNR were significantly higher in MBIR than ASiR (p < 0.01). MBIR showed significant improvement of IQ parameters compared to ASiR. As CCT is an examination that is frequently required, the use of MBIR may allow for substantial reduction of radiation exposure caused by medical diagnostics. (orig.)

Image quality of iterative reconstruction in cranial CT imaging: comparison of model-based iterative reconstruction (MBIR) and adaptive statistical iterative reconstruction (ASiR)

International Nuclear Information System (INIS)

Notohamiprodjo, S.; Deak, Z.; Meurer, F.; Maertz, F.; Mueck, F.G.; Geyer, L.L.; Wirth, S.

2015-01-01

The purpose of this study was to compare cranial CT (CCT) image quality (IQ) of the MBIR algorithm with standard iterative reconstruction (ASiR). In this institutional review board (IRB)-approved study, raw data sets of 100 unenhanced CCT examinations (120 kV, 50-260 mAs, 20 mm collimation, 0.984 pitch) were reconstructed with both ASiR and MBIR. Signal-to-noise (SNR) and contrast-to-noise (CNR) were calculated from attenuation values measured in caudate nucleus, frontal white matter, anterior ventricle horn, fourth ventricle, and pons. Two radiologists, who were blinded to the reconstruction algorithms, evaluated anonymized multiplanar reformations of 2.5 mm with respect to depiction of different parenchymal structures and impact of artefacts on IQ with a five-point scale (0: unacceptable, 1: less than average, 2: average, 3: above average, 4: excellent). MBIR decreased artefacts more effectively than ASiR (p < 0.01). The median depiction score for MBIR was 3, whereas the median value for ASiR was 2 (p < 0.01). SNR and CNR were significantly higher in MBIR than ASiR (p < 0.01). MBIR showed significant improvement of IQ parameters compared to ASiR. As CCT is an examination that is frequently required, the use of MBIR may allow for substantial reduction of radiation exposure caused by medical diagnostics. (orig.)

Use of regularized algebraic methods in tomographic reconstruction

International Nuclear Information System (INIS)

Koulibaly, P.M.; Darcourt, J.; Blanc-Ferraud, L.; Migneco, O.; Barlaud, M.

1997-01-01

The algebraic methods are used in emission tomography to facilitate the compensation of attenuation and of Compton scattering. We have tested on a phantom the use of a regularization (a priori introduction of information), as well as the taking into account of spatial resolution variation with the depth (SRVD). Hence, we have compared the performances of the two methods by back-projection filtering (BPF) and of the two algebraic methods (AM) in terms of FWHM (by means of a point source), of the reduction of background noise (σ/m) on the homogeneous part of Jaszczak's phantom and of reconstruction speed (time unit = BPF). The BPF methods make use of a grade filter (maximal resolution, no noise treatment), single or associated with a Hann's low-pass (f c = 0.4), as well as of an attenuation correction. The AM which embody attenuation and scattering corrections are, on one side, the OS EM (Ordered Subsets, partitioning and rearranging of the projection matrix; Expectation Maximization) without regularization or SRVD correction, and, on the other side, the OS MAP EM (Maximum a posteriori), regularized and embodying the SRVD correction. A table is given containing for each used method (grade, Hann, OS EM and OS MAP EM) the values of FWHM, σ/m and time, respectively. One can observe that the OS MAP EM algebraic method allows ameliorating both the resolution, by taking into account the SRVD in the reconstruction process and noise treatment by regularization. In addition, due to the OS technique the reconstruction times are acceptable

Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm.

Science.gov (United States)

Shi, Junwei; Liu, Fei; Zhang, Guanglei; Luo, Jianwen; Bai, Jing

2014-04-01

Owing to the high degree of scattering of light through tissues, the ill-posedness of fluorescence molecular tomography (FMT) inverse problem causes relatively low spatial resolution in the reconstruction results. Unlike L2 regularization, L1 regularization can preserve the details and reduce the noise effectively. Reconstruction is obtained through a restarted L1 regularization-based nonlinear conjugate gradient (re-L1-NCG) algorithm, which has been proven to be able to increase the computational speed with low memory consumption. The algorithm consists of inner and outer iterations. In the inner iteration, L1-NCG is used to obtain the L1-regularized results. In the outer iteration, the restarted strategy is used to increase the convergence speed of L1-NCG. To demonstrate the performance of re-L1-NCG in terms of spatial resolution, simulation and physical phantom studies with fluorescent targets located with different edge-to-edge distances were carried out. The reconstruction results show that the re-L1-NCG algorithm has the ability to resolve targets with an edge-to-edge distance of 0.1 cm at a depth of 1.5 cm, which is a significant improvement for FMT.

Motion tolerant iterative reconstruction algorithm for cone-beam helical CT imaging

Energy Technology Data Exchange (ETDEWEB)

Takahashi, Hisashi; Goto, Taiga; Hirokawa, Koichi; Miyazaki, Osamu [Hitachi Medical Corporation, Chiba-ken (Japan). CT System Div.

2011-07-01

We have developed a new advanced iterative reconstruction algorithm for cone-beam helical CT. The features of this algorithm are: (a) it uses separable paraboloidal surrogate (SPS) technique as a foundation for reconstruction to reduce noise and cone-beam artifact, (b) it uses a view weight in the back-projection process to reduce motion artifact. To confirm the improvement of our proposed algorithm over other existing algorithm, such as Feldkamp-Davis-Kress (FDK) or SPS algorithm, we compared the motion artifact reduction, image noise reduction (standard deviation of CT number), and cone-beam artifact reduction on simulated and clinical data set. Our results demonstrate that the proposed algorithm dramatically reduces motion artifacts compared with the SPS algorithm, and decreases image noise compared with the FDK algorithm. In addition, the proposed algorithm potentially improves time resolution of iterative reconstruction. (orig.)

Denoising multicriterion iterative reconstruction in emission spectral tomography

Science.gov (United States)

Wan, Xiong; Yin, Aihan

2007-03-01

In the study of optical testing, the computed tomogaphy technique has been widely adopted to reconstruct three-dimensional distributions of physical parameters of various kinds of fluid fields, such as flame, plasma, etc. In most cases, projection data are often stained by noise due to environmental disturbance, instrumental inaccuracy, and other random interruptions. To improve the reconstruction performance in noisy cases, an algorithm that combines a self-adaptive prefiltering denoising approach (SPDA) with a multicriterion iterative reconstruction (MCIR) is proposed and studied. First, the level of noise is approximately estimated with a frequency domain statistical method. Then the cutoff frequency of a Butterworth low-pass filter was established based on the evaluated noise energy. After the SPDA processing, the MCIR algorithm was adopted for limited-view optical computed tomography reconstruction. Simulated reconstruction of two test phantoms and a flame emission spectral tomography experiment were employed to evaluate the performance of SPDA-MCIR in noisy cases. Comparison with some traditional methods and experiment results showed that the SPDA-MCIR combination had obvious improvement in the case of noisy data reconstructions.

Exact iterative reconstruction for the interior problem

International Nuclear Information System (INIS)

Zeng, Gengsheng L; Gullberg, Grant T

2009-01-01

There is a trend in single photon emission computed tomography (SPECT) that small and dedicated imaging systems are becoming popular. For example, many companies are developing small dedicated cardiac SPECT systems with different designs. These dedicated systems have a smaller field of view (FOV) than a full-size clinical system. Thus data truncation has become the norm rather than the exception in these systems. Therefore, it is important to develop region of interest (ROI) reconstruction algorithms using truncated data. This paper is a stepping stone toward this direction. This paper shows that the common generic iterative image reconstruction algorithms are able to exactly reconstruct the ROI under the conditions that the convex ROI is fully sampled and the image value in a sub-region within the ROI is known. If the ROI includes a sub-region that is outside the patient body, then the conditions can be easily satisfied.

Comparison of the image qualities of filtered back-projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction for CT venography at 80 kVp

International Nuclear Information System (INIS)

Kim, Jin Hyeok; Choo, Ki Seok; Moon, Tae Yong; Lee, Jun Woo; Jeon, Ung Bae; Kim, Tae Un; Hwang, Jae Yeon; Yun, Myeong-Ja; Jeong, Dong Wook; Lim, Soo Jin

2016-01-01

To evaluate the subjective and objective qualities of computed tomography (CT) venography images at 80 kVp using model-based iterative reconstruction (MBIR) and to compare these with those of filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) using the same CT data sets. Forty-four patients (mean age: 56.1 ± 18.1) who underwent 80 kVp CT venography (CTV) for the evaluation of deep vein thrombosis (DVT) during 4 months were enrolled in this retrospective study. The same raw data were reconstructed using FBP, ASIR, and MBIR. Objective and subjective image analysis were performed at the inferior vena cava (IVC), femoral vein, and popliteal vein. The mean CNR of MBIR was significantly greater than those of FBP and ASIR and images reconstructed using MBIR had significantly lower objective image noise (p <.001). Subjective image quality and confidence of detecting DVT by MBIR group were significantly greater than those of FBP and ASIR (p <.005), and MBIR had the lowest score for subjective image noise (p <.001). CTV at 80 kVp with MBIR was superior to FBP and ASIR regarding subjective and objective image qualities. (orig.)

Iterative reconstruction techniques for computed tomography Part 1: Technical principles

International Nuclear Information System (INIS)

Willemink, Martin J.; Jong, Pim A. de; Leiner, Tim; Nievelstein, Rutger A.J.; Schilham, Arnold M.R.; Heer, Linda M. de; Budde, Ricardo P.J.

2013-01-01

To explain the technical principles of and differences between commercially available iterative reconstruction (IR) algorithms for computed tomography (CT) in non-mathematical terms for radiologists and clinicians. Technical details of the different proprietary IR techniques were distilled from available scientific articles and manufacturers' white papers and were verified by the manufacturers. Clinical results were obtained from a literature search spanning January 2006 to January 2012, including only original research papers concerning IR for CT. IR for CT iteratively reduces noise and artefacts in either image space or raw data, or both. Reported dose reductions ranged from 23 % to 76 % compared to locally used default filtered back-projection (FBP) settings, with similar noise, artefacts, subjective, and objective image quality. IR has the potential to allow reducing the radiation dose while preserving image quality. Disadvantages of IR include blotchy image appearance and longer computational time. Future studies need to address differences between IR algorithms for clinical low-dose CT. circle Iterative reconstruction technology for CT is presented in non-mathematical terms. (orig.)

X-ray dose reduction in abdominal computed tomography using advanced iterative reconstruction algorithms.

Directory of Open Access Journals (Sweden)

Peigang Ning

Full Text Available OBJECTIVE: This work aims to explore the effects of adaptive statistical iterative reconstruction (ASiR and model-based iterative reconstruction (MBIR algorithms in reducing computed tomography (CT radiation dosages in abdominal imaging. METHODS: CT scans on a standard male phantom were performed at different tube currents. Images at the different tube currents were reconstructed with the filtered back-projection (FBP, 50% ASiR and MBIR algorithms and compared. The CT value, image noise and contrast-to-noise ratios (CNRs of the reconstructed abdominal images were measured. Volumetric CT dose indexes (CTDIvol were recorded. RESULTS: At different tube currents, 50% ASiR and MBIR significantly reduced image noise and increased the CNR when compared with FBP. The minimal tube current values required by FBP, 50% ASiR, and MBIR to achieve acceptable image quality using this phantom were 200, 140, and 80 mA, respectively. At the identical image quality, 50% ASiR and MBIR reduced the radiation dose by 35.9% and 59.9% respectively when compared with FBP. CONCLUSIONS: Advanced iterative reconstruction techniques are able to reduce image noise and increase image CNRs. Compared with FBP, 50% ASiR and MBIR reduced radiation doses by 35.9% and 59.9%, respectively.

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA for L p -Regularization Using the Multiple Sub-Dictionary Representation

Directory of Open Access Journals (Sweden)

Yunyi Li

2017-12-01

Full Text Available Both L 1 / 2 and L 2 / 3 are two typical non-convex regularizations of L p ( 0 < p < 1 , which can be employed to obtain a sparser solution than the L 1 regularization. Recently, the multiple-state sparse transformation strategy has been developed to exploit the sparsity in L 1 regularization for sparse signal recovery, which combines the iterative reweighted algorithms. To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases p ∈ { 1 / 2 ,   2 / 3 } based on an iterative L p thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA. Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L 1 algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.

Total variation regularization in measurement and image space for PET reconstruction

KAUST Repository

Burger, M

2014-09-18

© 2014 IOP Publishing Ltd. The aim of this paper is to test and analyse a novel technique for image reconstruction in positron emission tomography, which is based on (total variation) regularization on both the image space and the projection space. We formulate our variational problem considering both total variation penalty terms on the image and on an idealized sinogram to be reconstructed from a given Poisson distributed noisy sinogram. We prove existence, uniqueness and stability results for the proposed model and provide some analytical insight into the structures favoured by joint regularization. For the numerical solution of the corresponding discretized problem we employ the split Bregman algorithm and extensively test the approach in comparison to standard total variation regularization on the image. The numerical results show that an additional penalty on the sinogram performs better on reconstructing images with thin structures.

Determination of quantitative tissue composition by iterative reconstruction on 3D DECT volumes

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Magnusson, Maria [Linkoeping Univ. (Sweden). Dept. of Electrical Engineering; Linkoeping Univ. (Sweden). Dept. of Medical and Health Sciences, Radiation Physics; Linkoeping Univ. (Sweden). Center for Medical Image Science and Visualization (CMIV); Malusek, Alexandr [Linkoeping Univ. (Sweden). Dept. of Medical and Health Sciences, Radiation Physics; Linkoeping Univ. (Sweden). Center for Medical Image Science and Visualization (CMIV); Nuclear Physics Institute AS CR, Prague (Czech Republic). Dept. of Radiation Dosimetry; Muhammad, Arif [Linkoeping Univ. (Sweden). Dept. of Medical and Health Sciences, Radiation Physics; Carlsson, Gudrun Alm [Linkoeping Univ. (Sweden). Dept. of Medical and Health Sciences, Radiation Physics; Linkoeping Univ. (Sweden). Center for Medical Image Science and Visualization (CMIV)

2011-07-01

Quantitative tissue classification using dual-energy CT has the potential to improve accuracy in radiation therapy dose planning as it provides more information about material composition of scanned objects than the currently used methods based on single-energy CT. One problem that hinders successful application of both single- and dual-energy CT is the presence of beam hardening and scatter artifacts in reconstructed data. Current pre- and post-correction methods used for image reconstruction often bias CT attenuation values and thus limit their applicability for quantitative tissue classification. Here we demonstrate simulation studies with a novel iterative algorithm that decomposes every soft tissue voxel into three base materials: water, protein, and adipose. The results demonstrate that beam hardening artifacts can effectively be removed and accurate estimation of mass fractions of each base material can be achieved. Our iterative algorithm starts with calculating parallel projections on two previously reconstructed DECT volumes reconstructed from fan-beam or helical projections with small conebeam angle. The parallel projections are then used in an iterative loop. Future developments include segmentation of soft and bone tissue and subsequent determination of bone composition. (orig.)

Intra-patient comparison of reduced-dose model-based iterative reconstruction with standard-dose adaptive statistical iterative reconstruction in the CT diagnosis and follow-up of urolithiasis

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Tenant, Sean; Pang, Chun Lap; Dissanayake, Prageeth [Peninsula Radiology Academy, Plymouth (United Kingdom); Vardhanabhuti, Varut [Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth (United Kingdom); University of Hong Kong, Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, Pokfulam (China); Stuckey, Colin; Gutteridge, Catherine [Plymouth Hospitals NHS Trust, Plymouth (United Kingdom); Hyde, Christopher [University of Exeter Medical School, St Luke' s Campus, Exeter (United Kingdom); Roobottom, Carl [Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth (United Kingdom); Plymouth Hospitals NHS Trust, Plymouth (United Kingdom)

2017-10-15

To evaluate the accuracy of reduced-dose CT scans reconstructed using a new generation of model-based iterative reconstruction (MBIR) in the imaging of urinary tract stone disease, compared with a standard-dose CT using 30% adaptive statistical iterative reconstruction. This single-institution prospective study recruited 125 patients presenting either with acute renal colic or for follow-up of known urinary tract stones. They underwent two immediately consecutive scans, one at standard dose settings and one at the lowest dose (highest noise index) the scanner would allow. The reduced-dose scans were reconstructed using both ASIR 30% and MBIR algorithms and reviewed independently by two radiologists. Objective and subjective image quality measures as well as diagnostic data were obtained. The reduced-dose MBIR scan was 100% concordant with the reference standard for the assessment of ureteric stones. It was extremely accurate at identifying calculi of 3 mm and above. The algorithm allowed a dose reduction of 58% without any loss of scan quality. A reduced-dose CT scan using MBIR is accurate in acute imaging for renal colic symptoms and for urolithiasis follow-up and allows a significant reduction in dose. (orig.)

MO-DE-207A-08: Four-Dimensional Cone-Beam CT Iterative Reconstruction with Time-Ordered Chain Graph Model for Non-Periodic Organ Motion and Deformation

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Nakano, M; Haga, A; Hanaoka, S; Nakagawa, K [The University of Tokyo Hospital, Bunkyo-ku, Tokyo (Japan); Kotoku, J [Teikyo University, Itabashi-ku, Tokyo (Japan); Magome, T [Komazawa University, Setagaya-ku, Tokyo (Japan); Masutani, Y [Hiroshima-City University, Hiroshima, Hiroshima (Japan)

2016-06-15

Purpose: The purpose of this study is to propose a new concept of four-dimensional (4D) cone-beam CT (CBCT) reconstruction for non-periodic organ motion using the Time-ordered Chain Graph Model (TCGM), and to compare the reconstructed results with the previously proposed methods, the total variation-based compressed sensing (TVCS) and prior-image constrained compressed sensing (PICCS). Methods: CBCT reconstruction method introduced in this study consisted of maximum a posteriori (MAP) iterative reconstruction combined with a regularization term derived from a concept of TCGM, which includes a constraint coming from the images of neighbouring time-phases. The time-ordered image series were concurrently reconstructed in the MAP iterative reconstruction framework. Angular range of projections for each time-phase was 90 degrees for TCGM and PICCS, and 200 degrees for TVCS. Two kinds of projection data, an elliptic-cylindrical digital phantom data and two clinical patients’ data, were used for reconstruction. The digital phantom contained an air sphere moving 3 cm along longitudinal axis, and temporal resolution of each method was evaluated by measuring the penumbral width of reconstructed moving air sphere. The clinical feasibility of non-periodic time-ordered 4D CBCT reconstruction was also examined using projection data of prostate cancer patients. Results: The results of reconstructed digital phantom shows that the penumbral widths of TCGM yielded the narrowest result; PICCS and TCGM were 10.6% and 17.4% narrower than that of TVCS, respectively. This suggests that the TCGM has the better temporal resolution than the others. Patients’ CBCT projection data were also reconstructed and all three reconstructed results showed motion of rectal gas and stool. The result of TCGM provided visually clearer and less blurring images. Conclusion: The present study demonstrates that the new concept for 4D CBCT reconstruction, TCGM, combined with MAP iterative reconstruction

Information-theoretic discrepancy based iterative reconstructions (IDIR) for polychromatic x-ray tomography

International Nuclear Information System (INIS)

Jang, Kwang Eun; Lee, Jongha; Sung, Younghun; Lee, SeongDeok

2013-01-01

Purpose: X-ray photons generated from a typical x-ray source for clinical applications exhibit a broad range of wavelengths, and the interactions between individual particles and biological substances depend on particles' energy levels. Most existing reconstruction methods for transmission tomography, however, neglect this polychromatic nature of measurements and rely on the monochromatic approximation. In this study, we developed a new family of iterative methods that incorporates the exact polychromatic model into tomographic image recovery, which improves the accuracy and quality of reconstruction.Methods: The generalized information-theoretic discrepancy (GID) was employed as a new metric for quantifying the distance between the measured and synthetic data. By using special features of the GID, the objective function for polychromatic reconstruction which contains a double integral over the wavelength and the trajectory of incident x-rays was simplified to a paraboloidal form without using the monochromatic approximation. More specifically, the original GID was replaced with a surrogate function with two auxiliary, energy-dependent variables. Subsequently, the alternating minimization technique was applied to solve the double minimization problem. Based on the optimization transfer principle, the objective function was further simplified to the paraboloidal equation, which leads to a closed-form update formula. Numerical experiments on the beam-hardening correction and material-selective reconstruction were conducted to compare and assess the performance of conventional methods and the proposed algorithms.Results: The authors found that the GID determines the distance between its two arguments in a flexible manner. In this study, three groups of GIDs with distinct data representations were considered. The authors demonstrated that one type of GIDs that comprises “raw” data can be viewed as an extension of existing statistical reconstructions; under a

The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique.

Science.gov (United States)

Kwon, Heejin; Cho, Jinhan; Oh, Jongyeong; Kim, Dongwon; Cho, Junghyun; Kim, Sanghyun; Lee, Sangyun; Lee, Jihyun

2015-10-01

To investigate whether reduced radiation dose abdominal CT images reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) compromise the depiction of clinically competent features when compared with the currently used routine radiation dose CT images reconstructed with ASIR. 27 consecutive patients (mean body mass index: 23.55 kg m(-2) underwent CT of the abdomen at two time points. At the first time point, abdominal CT was scanned at 21.45 noise index levels of automatic current modulation at 120 kV. Images were reconstructed with 40% ASIR, the routine protocol of Dong-A University Hospital. At the second time point, follow-up scans were performed at 30 noise index levels. Images were reconstructed with filtered back projection (FBP), 40% ASIR, 30% ASIR-V, 50% ASIR-V and 70% ASIR-V for the reduced radiation dose. Both quantitative and qualitative analyses of image quality were conducted. The CT dose index was also recorded. At the follow-up study, the mean dose reduction relative to the currently used common radiation dose was 35.37% (range: 19-49%). The overall subjective image quality and diagnostic acceptability of the 50% ASIR-V scores at the reduced radiation dose were nearly identical to those recorded when using the initial routine-dose CT with 40% ASIR. Subjective ratings of the qualitative analysis revealed that of all reduced radiation dose CT series reconstructed, 30% ASIR-V and 50% ASIR-V were associated with higher image quality with lower noise and artefacts as well as good sharpness when compared with 40% ASIR and FBP. However, the sharpness score at 70% ASIR-V was considered to be worse than that at 40% ASIR. Objective image noise for 50% ASIR-V was 34.24% and 46.34% which was lower than 40% ASIR and FBP. Abdominal CT images reconstructed with ASIR-V facilitate radiation dose reductions of to 35% when compared with the ASIR. This study represents the first clinical research experiment to use ASIR-V, the newest version of

Recent advances in iterative reconstruction for clinical SPECT/PET and CT.

Science.gov (United States)

Hutton, Brian F

2011-08-01

Statistical iterative reconstruction is now widely used in clinical practice and has contributed to significant improvement in image quality in recent years. Although primarily used for reconstruction in emission tomography (both single photon emission computed tomography (SPECT) and positron emission tomography (PET)) there is increasing interest in also applying similar algorithms to x-ray computed tomography (CT). There is increasing complexity in the factors that are included in the reconstruction, a demonstration of the versatility of the approach. Research continues with exploration of methods for further improving reconstruction quality with effective correction for various sources of artefact.

Sparse regularization for EIT reconstruction incorporating structural information derived from medical imaging.

Science.gov (United States)

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Mueller-Lisse, Ullrich; Moeller, Knut

2016-06-01

Electrical impedance tomography (EIT) reconstructs the conductivity distribution of a domain using electrical data on its boundary. This is an ill-posed inverse problem usually solved on a finite element mesh. For this article, a special regularization method incorporating structural information of the targeted domain is proposed and evaluated. Structural information was obtained either from computed tomography images or from preliminary EIT reconstructions by a modified k-means clustering. The proposed regularization method integrates this structural information into the reconstruction as a soft constraint preferring sparsity in group level. A first evaluation with Monte Carlo simulations indicated that the proposed solver is more robust to noise and the resulting images show fewer artifacts. This finding is supported by real data analysis. The structure based regularization has the potential to balance structural a priori information with data driven reconstruction. It is robust to noise, reduces artifacts and produces images that reflect anatomy and are thus easier to interpret for physicians.

SU-D-12A-05: Iterative Reconstruction Techniques to Enable Intrinsic Respiratory Gated CT in Mice

Energy Technology Data Exchange (ETDEWEB)

Sun, T; Sun, N; Tan, S [Huazhong University of Science and Technology, Wuhan, Hubei (China); Liu, Y; Mistry, N [University of Maryland School of Medicine, Baltimore, MD (United States)

2014-06-01

Purpose: Longitudinal studies of lung function in mice need the ability to image different phases of ventilation in free-breathing mice using retrospective gating. However, retrospective gating often produces under-sampled and uneven angular samples, resulting in severe reconstruction artifacts when using traditional FDK based reconstruction algorithms. We wanted to demonstrate the utility of iterative reconstruction method to enable intrinsic respiratory gating in small-animal CT. Methods: Free-breathing mice were imaged using a Siemens Inveon PET/micro-CT system. Evenly distributed projection images were acquired at 360 angles. Retrospective respiratory gating was performed using an intrinsic marker based on the average intensity in a region covering the diaphragm. Projections were classified into 4 and 6 phases (finer temporal resolution) resulting in 138 and 67 projections respectively. Reconstruction was carried out using 3 Methods: conventional FDK, iterative penalized least-square (PWLS) with total variation (TV), and PWLS with edge-preserving penalty. The performance of the methods was compared using contrast-to-noise (CNR) in a region of interest (ROI). Line profile through a specific region was plotted to evaluate the preserving of edges. Results: In both the cases with 4 and 6 phases, inadequate and non-uniform angular sampling results in artifacts using conventional FDK. However, such artifacts are minimized using both the iterative methods. Using both 4 and 6 phases, the iterative techniques outperformed FDK in terms of CNR and maintaining sharp edges. This is further evidenced especially with increased artifacts using FDK for 6 phases. Conclusion: This work indicates fewer artifacts and better image details can be achieved with iterative reconstruction methods in non-uniform under-sampled reconstruction. Using iterative methods can enable free-breathing intrinsic respiratory gating in small-animal CT. Further studies are needed to compare the

Knowledge-based iterative model reconstruction: comparative image quality and radiation dose with a pediatric computed tomography phantom

International Nuclear Information System (INIS)

Ryu, Young Jin; Choi, Young Hun; Cheon, Jung-Eun; Kim, Woo Sun; Kim, In-One; Ha, Seongmin

2016-01-01

CT of pediatric phantoms can provide useful guidance to the optimization of knowledge-based iterative reconstruction CT. To compare radiation dose and image quality of CT images obtained at different radiation doses reconstructed with knowledge-based iterative reconstruction, hybrid iterative reconstruction and filtered back-projection. We scanned a 5-year anthropomorphic phantom at seven levels of radiation. We then reconstructed CT data with knowledge-based iterative reconstruction (iterative model reconstruction [IMR] levels 1, 2 and 3; Philips Healthcare, Andover, MA), hybrid iterative reconstruction (iDose 4 , levels 3 and 7; Philips Healthcare, Andover, MA) and filtered back-projection. The noise, signal-to-noise ratio and contrast-to-noise ratio were calculated. We evaluated low-contrast resolutions and detectability by low-contrast targets and subjective and objective spatial resolutions by the line pairs and wire. With radiation at 100 peak kVp and 100 mAs (3.64 mSv), the relative doses ranged from 5% (0.19 mSv) to 150% (5.46 mSv). Lower noise and higher signal-to-noise, contrast-to-noise and objective spatial resolution were generally achieved in ascending order of filtered back-projection, iDose 4 levels 3 and 7, and IMR levels 1, 2 and 3, at all radiation dose levels. Compared with filtered back-projection at 100% dose, similar noise levels were obtained on IMR level 2 images at 24% dose and iDose 4 level 3 images at 50% dose, respectively. Regarding low-contrast resolution, low-contrast detectability and objective spatial resolution, IMR level 2 images at 24% dose showed comparable image quality with filtered back-projection at 100% dose. Subjective spatial resolution was not greatly affected by reconstruction algorithm. Reduced-dose IMR obtained at 0.92 mSv (24%) showed similar image quality to routine-dose filtered back-projection obtained at 3.64 mSv (100%), and half-dose iDose 4 obtained at 1.81 mSv. (orig.)

Knowledge-based iterative model reconstruction: comparative image quality and radiation dose with a pediatric computed tomography phantom.

Science.gov (United States)

Ryu, Young Jin; Choi, Young Hun; Cheon, Jung-Eun; Ha, Seongmin; Kim, Woo Sun; Kim, In-One

2016-03-01

CT of pediatric phantoms can provide useful guidance to the optimization of knowledge-based iterative reconstruction CT. To compare radiation dose and image quality of CT images obtained at different radiation doses reconstructed with knowledge-based iterative reconstruction, hybrid iterative reconstruction and filtered back-projection. We scanned a 5-year anthropomorphic phantom at seven levels of radiation. We then reconstructed CT data with knowledge-based iterative reconstruction (iterative model reconstruction [IMR] levels 1, 2 and 3; Philips Healthcare, Andover, MA), hybrid iterative reconstruction (iDose(4), levels 3 and 7; Philips Healthcare, Andover, MA) and filtered back-projection. The noise, signal-to-noise ratio and contrast-to-noise ratio were calculated. We evaluated low-contrast resolutions and detectability by low-contrast targets and subjective and objective spatial resolutions by the line pairs and wire. With radiation at 100 peak kVp and 100 mAs (3.64 mSv), the relative doses ranged from 5% (0.19 mSv) to 150% (5.46 mSv). Lower noise and higher signal-to-noise, contrast-to-noise and objective spatial resolution were generally achieved in ascending order of filtered back-projection, iDose(4) levels 3 and 7, and IMR levels 1, 2 and 3, at all radiation dose levels. Compared with filtered back-projection at 100% dose, similar noise levels were obtained on IMR level 2 images at 24% dose and iDose(4) level 3 images at 50% dose, respectively. Regarding low-contrast resolution, low-contrast detectability and objective spatial resolution, IMR level 2 images at 24% dose showed comparable image quality with filtered back-projection at 100% dose. Subjective spatial resolution was not greatly affected by reconstruction algorithm. Reduced-dose IMR obtained at 0.92 mSv (24%) showed similar image quality to routine-dose filtered back-projection obtained at 3.64 mSv (100%), and half-dose iDose(4) obtained at 1.81 mSv.

High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs

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Wan Xiaohua

2012-06-01

Full Text Available Abstract Background Three-dimensional (3D reconstruction in electron tomography (ET has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. Blob-based iterative methods are advantageous reconstruction methods for 3D reconstruction in ET, but demand huge computational costs. Multiple graphic processing units (multi-GPUs offer an affordable platform to meet these demands. However, a synchronous communication scheme between multi-GPUs leads to idle GPU time, and a weighted matrix involved in iterative methods cannot be loaded into GPUs especially for large images due to the limited available memory of GPUs. Results In this paper we propose a multilevel parallel strategy combined with an asynchronous communication scheme and a blob-ELLR data structure to efficiently perform blob-based iterative reconstructions on multi-GPUs. The asynchronous communication scheme is used to minimize the idle GPU time so as to asynchronously overlap communications with computations. The blob-ELLR data structure only needs nearly 1/16 of the storage space in comparison with ELLPACK-R (ELLR data structure and yields significant acceleration. Conclusions Experimental results indicate that the multilevel parallel scheme combined with the asynchronous communication scheme and the blob-ELLR data structure allows efficient implementations of 3D reconstruction in ET on multi-GPUs.

PRIFIRA: General regularization using prior-conditioning for fast radio interferometric imaging†

Science.gov (United States)

Naghibzadeh, Shahrzad; van der Veen, Alle-Jan

2018-06-01

Image formation in radio astronomy is a large-scale inverse problem that is inherently ill-posed. We present a general algorithmic framework based on a Bayesian-inspired regularized maximum likelihood formulation of the radio astronomical imaging problem with a focus on diffuse emission recovery from limited noisy correlation data. The algorithm is dubbed PRIor-conditioned Fast Iterative Radio Astronomy (PRIFIRA) and is based on a direct embodiment of the regularization operator into the system by right preconditioning. The resulting system is then solved using an iterative method based on projections onto Krylov subspaces. We motivate the use of a beamformed image (which includes the classical "dirty image") as an efficient prior-conditioner. Iterative reweighting schemes generalize the algorithmic framework and can account for different regularization operators that encourage sparsity of the solution. The performance of the proposed method is evaluated based on simulated one- and two-dimensional array arrangements as well as actual data from the core stations of the Low Frequency Array radio telescope antenna configuration, and compared to state-of-the-art imaging techniques. We show the generality of the proposed method in terms of regularization schemes while maintaining a competitive reconstruction quality with the current reconstruction techniques. Furthermore, we show that exploiting Krylov subspace methods together with the proper noise-based stopping criteria results in a great improvement in imaging efficiency.

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.

Science.gov (United States)

Li, Yunyi; Zhang, Jie; Fan, Shangang; Yang, Jie; Xiong, Jian; Cheng, Xiefeng; Sari, Hikmet; Adachi, Fumiyuki; Gui, Guan

2017-12-15

Both L 1/2 and L 2/3 are two typical non-convex regularizations of L p (0dictionary sparse transform strategies for the two typical cases p∈{1/2, 2/3} based on an iterative Lp thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA). Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L₁ algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.

Acceleration of iterative tomographic reconstruction using graphics processors

International Nuclear Information System (INIS)

Belzunce, M.A.; Osorio, A.; Verrastro, C.A.

2009-01-01

Using iterative algorithms for image reconstruction in 3 D Positron Emission Tomography has shown to produce images with better quality than analytical methods. How ever, these algorithms are computationally expensive. New Graphic Processor Units (GPU) provides high performance at low cost and also programming tools that make possible to execute parallel algorithms easily in scientific applications. In this work, we try to achieve an acceleration of image reconstruction algorithms in 3 D PET by using a GPU. A parallel implementation of the algorithm ML-EM 3 D was developed using Siddon algorithm as Projector and Back-projector. Results show that accelerations of more than one order of magnitude can be achieved, keeping similar image quality. (author)

Iterative reconstruction or filtered backprojection for semi-quantitative assessment of dopamine D2 receptor SPECT studies?

International Nuclear Information System (INIS)

Koch, Walter; Suessmair, Christine; Tatsch, Klaus; Poepperl, Gabriele

2011-01-01

In routine clinical practice striatal dopamine D 2 receptor binding is generally assessed using data reconstructed by filtered backprojection (FBP). The aim of this study was to investigate the use of an iterative reconstruction algorithm (ordered subset expectation maximization, OSEM) and to assess whether it may provide comparable or even better results than those obtained by standard FBP. In 56 patients with parkinsonian syndromes, single photon emission computed tomography (SPECT) scans were acquired 2 h after i.v. application of 185 MBq [ 123 I]iodobenzamide (IBZM) using a triple-head gamma camera (Siemens MS 3). The scans were reconstructed both by FBP and OSEM (3 iterations, 8 subsets) and filtered using a Butterworth filter. After attenuation correction the studies were automatically fitted to a mean template with a corresponding 3-D volume of interest (VOI) map covering striatum (S), caudate (C), putamen (P) and several reference VOIs using BRASS software. Visual assessment of the fitted studies suggests a better separation between C and P in studies reconstructed by OSEM than FBP. Unspecific background activity appears more homogeneous after iterative reconstruction. The correlation shows a good accordance of dopamine receptor binding using FBP and OSEM (intra-class correlation coefficients S: 0.87; C: 0.88; P: 0.84). Receiver-operating characteristic (ROC) analyses show comparable diagnostic power of OSEM and FBP in the differentiation between idiopathic parkinsonian syndrome (IPS) and non-IPS. Iterative reconstruction of IBZM SPECT studies for assessment of the D 2 receptors is feasible in routine clinical practice. Close correlations between FBP and OSEM data suggest that iteratively reconstructed IBZM studies allow reliable quantification of dopamine receptor binding even though a gain in diagnostic power could not be demonstrated. (orig.)

Iterative reconstruction technique with reduced volume CT dose index: diagnostic accuracy in pediatric acute appendicitis

International Nuclear Information System (INIS)

Didier, Ryne A.; Vajtai, Petra L.; Hopkins, Katharine L.

2015-01-01

Iterative reconstruction technique has been proposed as a means of reducing patient radiation dose in pediatric CT. Yet, the effect of such reductions on diagnostic accuracy has not been thoroughly evaluated. This study compares accuracy of diagnosing pediatric acute appendicitis using contrast-enhanced abdominopelvic CT scans performed with traditional pediatric weight-based protocols and filtered back projection reconstruction vs. a filtered back projection/iterative reconstruction technique blend with reduced volume CT dose index (CTDI vol ). Results of pediatric contrast-enhanced abdominopelvic CT scans done for pain and/or suspected appendicitis were reviewed in two groups: A, 192 scans performed with the hospital's established weight-based CT protocols and filtered back projection reconstruction; B, 194 scans performed with iterative reconstruction technique and reduced CTDI vol . Reduced CTDI vol was achieved primarily by reductions in effective tube current-time product (mAs eff ) and tube peak kilovoltage (kVp). CT interpretation was correlated with clinical follow-up and/or surgical pathology. CTDI vol , size-specific dose estimates (SSDE) and performance characteristics of the two CT techniques were then compared. Between groups A and B, mean CTDI vol was reduced by 45%, and mean SSDE was reduced by 46%. Sensitivity, specificity and diagnostic accuracy were 96%, 97% and 96% in group A vs. 100%, 99% and 99% in group B. Accuracy in diagnosing pediatric acute appendicitis was maintained in contrast-enhanced abdominopelvic CT scans that incorporated iterative reconstruction technique, despite reductions in mean CTDI vol and SSDE by nearly half as compared to the hospital's traditional weight-based protocols. (orig.)

TH-E-17A-02: High-Pitch and Sparse-View Helical 4D CT Via Iterative Image Reconstruction Method Based On Tensor Framelet

International Nuclear Information System (INIS)

Guo, M; Nam, H; Li, R; Xing, L; Gao, H

2014-01-01

Purpose: 4D CT is routinely performed during radiation therapy treatment planning of thoracic and abdominal cancers. Compared with the cine mode, the helical mode is advantageous in temporal resolution. However, a low pitch (∼0.1) for 4D CT imaging is often required instead of the standard pitch (∼1) for static imaging, since standard image reconstruction based on analytic method requires the low-pitch scanning in order to satisfy the data sufficient condition when reconstructing each temporal frame individually. In comparison, the flexible iterative method enables the reconstruction of all temporal frames simultaneously, so that the image similarity among frames can be utilized to possibly perform high-pitch and sparse-view helical 4D CT imaging. The purpose of this work is to investigate such an exciting possibility for faster imaging with lower dose. Methods: A key for highpitch and sparse-view helical 4D CT imaging is the simultaneous reconstruction of all temporal frames using the prior that temporal frames are continuous along the temporal direction. In this work, such a prior is regularized through the sparsity transform based on spatiotemporal tensor framelet (TF) as a multilevel and high-order extension of total variation transform. Moreover, GPU-based fast parallel computing of X-ray transform and its adjoint together with split Bregman method is utilized for solving the 4D image reconstruction problem efficiently and accurately. Results: The simulation studies based on 4D NCAT phantoms were performed with various pitches (i.e., 0.1, 0.2, 0.5, and 1) and sparse views (i.e., 400 views per rotation instead of standard >2000 views per rotation), using 3D iterative individual reconstruction method based on 3D TF and 4D iterative simultaneous reconstruction method based on 4D TF respectively. Conclusion: The proposed TF-based simultaneous 4D image reconstruction method enables high-pitch and sparse-view helical 4D CT with lower dose and faster speed

CT imaging of congenital lung lesions: effect of iterative reconstruction on diagnostic performance and radiation dose

International Nuclear Information System (INIS)

Haggerty, Jay E.; Smith, Ethan A.; Dillman, Jonathan R.; Kunisaki, Shaun M.

2015-01-01

Different iterative reconstruction techniques are available for use in pediatric computed tomography (CT), but these techniques have not been systematically evaluated in infants. To determine the effect of iterative reconstruction on diagnostic performance, image quality and radiation dose in infants undergoing CT evaluation for congenital lung lesions. A retrospective review of contrast-enhanced chest CT in infants (<1 year) with congenital lung lesions was performed. CT examinations were reviewed to document the type of lung lesion, vascular anatomy, image noise measurements and image reconstruction method. CTDI vol was used to calculate size-specific dose estimates (SSDE). CT findings were correlated with intraoperative and histopathological findings. Analysis of variance and the Student's t-test were used to compare image noise measurements and radiation dose estimates between groups. Fifteen CT examinations used filtered back projection (FBP; mean age: 84 days), 15 used adaptive statistical iterative reconstruction (ASiR; mean age: 93 days), and 11 used model-based iterative reconstruction (MBIR; mean age: 98 days). Compared to operative findings, 13/15 (87%), 14/15 (93%) and 11/11 (100%) lesions were correctly characterized using FBP, ASiR and MBIR, respectively. Arterial anatomy was correctly identified in 12/15 (80%) using FBP, 13/15 (87%) using ASiR and 11/11 (100%) using MBIR. Image noise was less for MBIR vs. ASiR (P < 0.0001). Mean SSDE was different among groups (P = 0.003; FBP = 7.35 mGy, ASiR = 1.89 mGy, MBIR = 1.49 mGy). Congenital lung lesions can be adequately characterized in infants using iterative CT reconstruction techniques while maintaining image quality and lowering radiation dose. (orig.)

CT imaging of congenital lung lesions: effect of iterative reconstruction on diagnostic performance and radiation dose

Energy Technology Data Exchange (ETDEWEB)

Haggerty, Jay E.; Smith, Ethan A.; Dillman, Jonathan R. [University of Michigan Health System, Section of Pediatric Radiology, Department of Radiology, C.S. Mott Children' s Hospital, Ann Arbor, MI (United States); Kunisaki, Shaun M. [University of Michigan Health System, Section of Pediatric Surgery, Department of Surgery, C.S. Mott Children' s Hospital, Ann Arbor, MI (United States)

2015-07-15

Different iterative reconstruction techniques are available for use in pediatric computed tomography (CT), but these techniques have not been systematically evaluated in infants. To determine the effect of iterative reconstruction on diagnostic performance, image quality and radiation dose in infants undergoing CT evaluation for congenital lung lesions. A retrospective review of contrast-enhanced chest CT in infants (<1 year) with congenital lung lesions was performed. CT examinations were reviewed to document the type of lung lesion, vascular anatomy, image noise measurements and image reconstruction method. CTDI{sub vol} was used to calculate size-specific dose estimates (SSDE). CT findings were correlated with intraoperative and histopathological findings. Analysis of variance and the Student's t-test were used to compare image noise measurements and radiation dose estimates between groups. Fifteen CT examinations used filtered back projection (FBP; mean age: 84 days), 15 used adaptive statistical iterative reconstruction (ASiR; mean age: 93 days), and 11 used model-based iterative reconstruction (MBIR; mean age: 98 days). Compared to operative findings, 13/15 (87%), 14/15 (93%) and 11/11 (100%) lesions were correctly characterized using FBP, ASiR and MBIR, respectively. Arterial anatomy was correctly identified in 12/15 (80%) using FBP, 13/15 (87%) using ASiR and 11/11 (100%) using MBIR. Image noise was less for MBIR vs. ASiR (P < 0.0001). Mean SSDE was different among groups (P = 0.003; FBP = 7.35 mGy, ASiR = 1.89 mGy, MBIR = 1.49 mGy). Congenital lung lesions can be adequately characterized in infants using iterative CT reconstruction techniques while maintaining image quality and lowering radiation dose. (orig.)

Coronary CT angiography: Comparison of a novel iterative reconstruction with filtered back projection for reconstruction of low-dose CT—Initial experience

International Nuclear Information System (INIS)

Takx, Richard A.P.; Schoepf, U. Joseph; Moscariello, Antonio; Das, Marco; Rowe, Garrett; Schoenberg, Stefan O.; Fink, Christian; Henzler, Thomas

2013-01-01

Objective: To prospectively compare subjective and objective image quality in 20% tube current coronary CT angiography (cCTA) datasets between an iterative reconstruction algorithm (SAFIRE) and traditional filtered back projection (FBP). Materials and methods: Twenty patients underwent a prospectively ECG-triggered dual-step cCTA protocol using 2nd generation dual-source CT (DSCT). CT raw data was reconstructed using standard FBP at full-dose (Group 1 a) and 80% tube current reduced low-dose (Group 1 b). The low-dose raw data was additionally reconstructed using iterative raw data reconstruction (Group 2 ). Attenuation and image noise were measured in three regions of interest and signal-to-noise-ratio (SNR) as well as contrast-to-noise-ratio (CNR) was calculated. Subjective diagnostic image quality was evaluated using a 4-point Likert scale. Results: Mean image noise of group 2 was lowered by 22% on average when compared to group 1 b (p 2 compared to group 1 b (p 2 (1.88 ± 0.63) was also rated significantly higher when compared to group 1 b (1.58 ± 0.63, p = 0.004). Conclusions: Image quality of 80% tube current reduced iteratively reconstructed cCTA raw data is significantly improved when compared to standard FBP and consequently may improve the diagnostic accuracy of cCTA

Born iterative reconstruction using perturbed-phase field estimates.

Science.gov (United States)

Astheimer, Jeffrey P; Waag, Robert C

2008-10-01

A method of image reconstruction from scattering measurements for use in ultrasonic imaging is presented. The method employs distorted-wave Born iteration but does not require using a forward-problem solver or solving large systems of equations. These calculations are avoided by limiting intermediate estimates of medium variations to smooth functions in which the propagated fields can be approximated by phase perturbations derived from variations in a geometric path along rays. The reconstruction itself is formed by a modification of the filtered-backpropagation formula that includes correction terms to account for propagation through an estimated background. Numerical studies that validate the method for parameter ranges of interest in medical applications are presented. The efficiency of this method offers the possibility of real-time imaging from scattering measurements.

Iterative reconstruction technique with reduced volume CT dose index: diagnostic accuracy in pediatric acute appendicitis

Energy Technology Data Exchange (ETDEWEB)

Didier, Ryne A. [Oregon Health and Science University, Department of Diagnostic Radiology, DC7R, Portland, OR (United States); Vajtai, Petra L. [Oregon Health and Science University, Department of Pediatrics, Portland, OR (United States); Oregon Health and Science University, Department of Diagnostic Radiology, DC7R, Portland, OR (United States); Hopkins, Katharine L. [Oregon Health and Science University, Department of Diagnostic Radiology, DC7R, Portland, OR (United States); Oregon Health and Science University, Department of Pediatrics, Portland, OR (United States)

2014-07-05

Iterative reconstruction technique has been proposed as a means of reducing patient radiation dose in pediatric CT. Yet, the effect of such reductions on diagnostic accuracy has not been thoroughly evaluated. This study compares accuracy of diagnosing pediatric acute appendicitis using contrast-enhanced abdominopelvic CT scans performed with traditional pediatric weight-based protocols and filtered back projection reconstruction vs. a filtered back projection/iterative reconstruction technique blend with reduced volume CT dose index (CTDI{sub vol}). Results of pediatric contrast-enhanced abdominopelvic CT scans done for pain and/or suspected appendicitis were reviewed in two groups: A, 192 scans performed with the hospital's established weight-based CT protocols and filtered back projection reconstruction; B, 194 scans performed with iterative reconstruction technique and reduced CTDI{sub vol}. Reduced CTDI{sub vol} was achieved primarily by reductions in effective tube current-time product (mAs{sub eff}) and tube peak kilovoltage (kVp). CT interpretation was correlated with clinical follow-up and/or surgical pathology. CTDI{sub vol}, size-specific dose estimates (SSDE) and performance characteristics of the two CT techniques were then compared. Between groups A and B, mean CTDI{sub vol} was reduced by 45%, and mean SSDE was reduced by 46%. Sensitivity, specificity and diagnostic accuracy were 96%, 97% and 96% in group A vs. 100%, 99% and 99% in group B. Accuracy in diagnosing pediatric acute appendicitis was maintained in contrast-enhanced abdominopelvic CT scans that incorporated iterative reconstruction technique, despite reductions in mean CTDI{sub vol} and SSDE by nearly half as compared to the hospital's traditional weight-based protocols. (orig.)

An iterative hyperelastic parameters reconstruction for breast cancer assessment

Science.gov (United States)

Mehrabian, Hatef; Samani, Abbas

2008-03-01

In breast elastography, breast tissues usually undergo large compressions resulting in significant geometric and structural changes, and consequently nonlinear mechanical behavior. In this study, an elastography technique is presented where parameters characterizing tissue nonlinear behavior is reconstructed. Such parameters can be used for tumor tissue classification. To model the nonlinear behavior, tissues are treated as hyperelastic materials. The proposed technique uses a constrained iterative inversion method to reconstruct the tissue hyperelastic parameters. The reconstruction technique uses a nonlinear finite element (FE) model for solving the forward problem. In this research, we applied Yeoh and Polynomial models to model the tissue hyperelasticity. To mimic the breast geometry, we used a computational phantom, which comprises of a hemisphere connected to a cylinder. This phantom consists of two types of soft tissue to mimic adipose and fibroglandular tissues and a tumor. Simulation results show the feasibility of the proposed method in reconstructing the hyperelastic parameters of the tumor tissue.

A study of reconstruction artifacts in cone beam tomography using filtered backprojection and iterative EM algorithms

International Nuclear Information System (INIS)

Zeng, G.L.; Gullberg, G.T.

1990-01-01

Reconstruction artifacts in cone beam tomography are studied for filtered backprojection (Feldkamp) and iterative EM algorithms. The filtered backprojection algorithm uses a voxel-driven, interpolated backprojection to reconstruct the cone beam data; whereas, the iterative EM algorithm performs ray-driven projection and backprojection operations for each iteration. Two weight in schemes for the projection and backprojection operations in the EM algorithm are studied. One weights each voxel by the length of the ray through the voxel and the other equates the value of a voxel to the functional value of the midpoint of the line intersecting the voxel, which is obtained by interpolating between eight neighboring voxels. Cone beam reconstruction artifacts such as rings, bright vertical extremities, and slice-to slice cross talk are not found with parallel beam and fan beam geometries

A new simple iterative reconstruction algorithm for SPECT transmission measurement

International Nuclear Information System (INIS)

Hwang, D.S.; Zeng, G.L.

2005-01-01

This paper proposes a new iterative reconstruction algorithm for transmission tomography and compares this algorithm with several other methods. The new algorithm is simple and resembles the emission ML-EM algorithm in form. Due to its simplicity, it is easy to implement and fast to compute a new update at each iteration. The algorithm also always guarantees non-negative solutions. Evaluations are performed using simulation studies and real phantom data. Comparisons with other algorithms such as convex, gradient, and logMLEM show that the proposed algorithm is as good as others and performs better in some cases

3D algebraic iterative reconstruction for cone-beam x-ray differential phase-contrast computed tomography.

Science.gov (United States)

Fu, Jian; Hu, Xinhua; Velroyen, Astrid; Bech, Martin; Jiang, Ming; Pfeiffer, Franz

2015-01-01

Due to the potential of compact imaging systems with magnified spatial resolution and contrast, cone-beam x-ray differential phase-contrast computed tomography (DPC-CT) has attracted significant interest. The current proposed FDK reconstruction algorithm with the Hilbert imaginary filter will induce severe cone-beam artifacts when the cone-beam angle becomes large. In this paper, we propose an algebraic iterative reconstruction (AIR) method for cone-beam DPC-CT and report its experiment results. This approach considers the reconstruction process as the optimization of a discrete representation of the object function to satisfy a system of equations that describes the cone-beam DPC-CT imaging modality. Unlike the conventional iterative algorithms for absorption-based CT, it involves the derivative operation to the forward projections of the reconstructed intermediate image to take into account the differential nature of the DPC projections. This method is based on the algebraic reconstruction technique, reconstructs the image ray by ray, and is expected to provide better derivative estimates in iterations. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a mini-focus x-ray tube source. It is shown that the proposed method can reduce the cone-beam artifacts and performs better than FDK under large cone-beam angles. This algorithm is of interest for future cone-beam DPC-CT applications.

Influence of iterative reconstruction on coronary calcium scores at multiple heart rates: a multivendor phantom study on state-of-the-art CT systems.

Science.gov (United States)

van der Werf, N R; Willemink, M J; Willems, T P; Greuter, M J W; Leiner, T

2017-12-28

The objective of this study was to evaluate the influence of iterative reconstruction on coronary calcium scores (CCS) at different heart rates for four state-of-the-art CT systems. Within an anthropomorphic chest phantom, artificial coronary arteries were translated in a water-filled compartment. The arteries contained three different calcifications with low (38 mg), medium (80 mg) and high (157 mg) mass. Linear velocities were applied, corresponding to heart rates of 0,  75 bpm. Data were acquired on four state-of-the-art CT systems (CT1-CT4) with routinely used CCS protocols. Filtered back projection (FBP) and three increasing levels of iterative reconstruction (L1-L3) were used for reconstruction. CCS were quantified as Agatston score and mass score. An iterative reconstruction susceptibility (IRS) index was used to assess susceptibility of Agatston score (IRS AS ) and mass score (IRS MS ) to iterative reconstruction. IRS values were compared between CT systems and between calcification masses. For each heart rate, differences in CCS of iterative reconstructed images were evaluated with CCS of FBP images as reference, and indicated as small ( 10%). Statistical analysis was performed with repeated measures ANOVA tests. While subtle differences were found for Agatston scores of low mass calcification, medium and high mass calcifications showed increased CCS up to 77% with increasing heart rates. IRS AS of CT1-T4 were 17, 41, 130 and 22% higher than IRS MS . Not only were IRS significantly different between all CT systems, but also between calcification masses. Up to a fourfold increase in IRS was found for the low mass calcification in comparison with the high mass calcification. With increasing iterative reconstruction strength, maximum decreases of 21 and 13% for Agatston and mass score were found. In total, 21 large differences between Agatston scores from FBP and iterative reconstruction were found, while only five large differences were found between

L{sub 1/2} regularization based numerical method for effective reconstruction of bioluminescence tomography

Energy Technology Data Exchange (ETDEWEB)

Chen, Xueli, E-mail: xlchen@xidian.edu.cn, E-mail: jimleung@mail.xidian.edu.cn; Yang, Defu; Zhang, Qitan; Liang, Jimin, E-mail: xlchen@xidian.edu.cn, E-mail: jimleung@mail.xidian.edu.cn [School of Life Science and Technology, Xidian University, Xi' an 710071 (China); Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education (China)

2014-05-14

Even though bioluminescence tomography (BLT) exhibits significant potential and wide applications in macroscopic imaging of small animals in vivo, the inverse reconstruction is still a tough problem that has plagued researchers in a related area. The ill-posedness of inverse reconstruction arises from insufficient measurements and modeling errors, so that the inverse reconstruction cannot be solved directly. In this study, an l{sub 1/2} regularization based numerical method was developed for effective reconstruction of BLT. In the method, the inverse reconstruction of BLT was constrained into an l{sub 1/2} regularization problem, and then the weighted interior-point algorithm (WIPA) was applied to solve the problem through transforming it into obtaining the solution of a series of l{sub 1} regularizers. The feasibility and effectiveness of the proposed method were demonstrated with numerical simulations on a digital mouse. Stability verification experiments further illustrated the robustness of the proposed method for different levels of Gaussian noise.

Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT.

Science.gov (United States)

Chen, Guang-Hong; Li, Yinsheng

2015-08-01

In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition. In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial-temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial-temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial-temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity of the reconstructed

A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging

International Nuclear Information System (INIS)

Chaari, L.; Pesquet, J.Ch.; Chaari, L.; Ciuciu, Ph.; Benazza-Benyahia, A.

2011-01-01

To reduce scanning time and/or improve spatial/temporal resolution in some Magnetic Resonance Imaging (MRI) applications, parallel MRI acquisition techniques with multiple coils acquisition have emerged since the early 1990's as powerful imaging methods that allow a faster acquisition process. In these techniques, the full FOV image has to be reconstructed from the resulting acquired under sampled k-space data. To this end, several reconstruction techniques have been proposed such as the widely-used Sensitivity Encoding (SENSE) method. However, the reconstructed image generally presents artifacts when perturbations occur in both the measured data and the estimated coil sensitivity profiles. In this paper, we aim at achieving accurate image reconstruction under degraded experimental conditions (low magnetic field and high reduction factor), in which neither the SENSE method nor the Tikhonov regularization in the image domain give convincing results. To this end, we present a novel method for SENSE-based reconstruction which proceeds with regularization in the complex wavelet domain by promoting sparsity. The proposed approach relies on a fast algorithm that enables the minimization of regularized non-differentiable criteria including more general penalties than a classical l 1 term. To further enhance the reconstructed image quality, local convex constraints are added to the regularization process. In vivo human brain experiments carried out on Gradient-Echo (GRE) anatomical and Echo Planar Imaging (EPI) functional MRI data at 1.5 T indicate that our algorithm provides reconstructed images with reduced artifacts for high reduction factors. (authors)

Projector and Backprojector for Iterative CT Reconstruction with Blobs using CUDA

NARCIS (Netherlands)

Bippus, R.D.; Koehler, T.; Bergner, F.; Brendel, B.; Hansis, E.; Proksa, R.

2011-01-01

Aiming at modeling the system’s geometry correctly accounting for the major effects influencing image quality within an iterative reconstruction framework we want to achieve this within reasonable processing times. This principle objective led us to using blobs for imagerepresentation and a

Experimental results and validation of a method to reconstruct forces on the ITER test blanket modules

International Nuclear Information System (INIS)

Zeile, Christian; Maione, Ivan A.

2015-01-01

Highlights: • An in operation force measurement system for the ITER EU HCPB TBM has been developed. • The force reconstruction methods are based on strain measurements on the attachment system. • An experimental setup and a corresponding mock-up have been built. • A set of test cases representing ITER relevant excitations has been used for validation. • The influence of modeling errors on the force reconstruction has been investigated. - Abstract: In order to reconstruct forces on the test blanket modules in ITER, two force reconstruction methods, the augmented Kalman filter and a model predictive controller, have been selected and developed to estimate the forces based on strain measurements on the attachment system. A dedicated experimental setup with a corresponding mock-up has been designed and built to validate these methods. A set of test cases has been defined to represent possible excitation of the system. It has been shown that the errors in the estimated forces mainly depend on the accuracy of the identified model used by the algorithms. Furthermore, it has been found that a minimum of 10 strain gauges is necessary to allow for a low error in the reconstructed forces.

Iterative reconstruction methods for Thermo-acoustic Tomography

International Nuclear Information System (INIS)

Marinesque, Sebastien

2012-01-01

We define, study and implement various iterative reconstruction methods for Thermo-acoustic Tomography (TAT): the Back and Forth Nudging (BFN), easy to implement and to use, a variational technique (VT) and the Back and Forth SEEK (BF-SEEK), more sophisticated, and a coupling method between Kalman filter (KF) and Time Reversal (TR). A unified formulation is explained for the sequential techniques aforementioned that defines a new class of inverse problem methods: the Back and Forth Filters (BFF). In addition to existence and uniqueness (particularly for backward solutions), we study many frameworks that ensure and characterize the convergence of the algorithms. Thus we give a general theoretical framework for which the BFN is a well-posed problem. Then, in application to TAT, existence and uniqueness of its solutions and geometrical convergence of the algorithm are proved, and an explicit convergence rate and a description of its numerical behaviour are given. Next, theoretical and numerical studies of more general and realistic framework are led, namely different objects, speeds (with or without trapping), various sensor configurations and samplings, attenuated equations or external sources. Then optimal control and best estimate tools are used to characterize the BFN convergence and converging feedbacks for BFF, under observability assumptions. Finally, we compare the most flexible and efficient current techniques (TR and an iterative variant) with our various BFF and the VT in several experiments. Thus, robust, with different possible complexities and flexible, the methods that we propose are very interesting reconstruction techniques, particularly in TAT and when observations are degraded. (author) [fr

A two-way regularization method for MEG source reconstruction

KAUST Repository

Tian, Tian Siva; Huang, Jianhua Z.; Shen, Haipeng; Li, Zhimin

2012-01-01

The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothness). The focality and smoothness of the reconstructed signals are ensured respectively by imposing a sparsity-inducing penalty and a roughness penalty in the data fitting criterion. A two-stage algorithm is developed for fast computation, where a raw estimate of the source time course is obtained in the first stage and then refined in the second stage by the two-way regularization. The proposed method is shown to be effective on both synthetic and real-world examples. © Institute of Mathematical Statistics, 2012.

A two-way regularization method for MEG source reconstruction

KAUST Repository

Tian, Tian Siva

2012-09-01

The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothness). The focality and smoothness of the reconstructed signals are ensured respectively by imposing a sparsity-inducing penalty and a roughness penalty in the data fitting criterion. A two-stage algorithm is developed for fast computation, where a raw estimate of the source time course is obtained in the first stage and then refined in the second stage by the two-way regularization. The proposed method is shown to be effective on both synthetic and real-world examples. © Institute of Mathematical Statistics, 2012.

The optimal monochromatic spectral computed tomographic imaging plus adaptive statistical iterative reconstruction algorithm can improve the superior mesenteric vessel image quality

Energy Technology Data Exchange (ETDEWEB)

Yin, Xiao-Ping; Zuo, Zi-Wei; Xu, Ying-Jin; Wang, Jia-Ning [CT/MRI room, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000 (China); Liu, Huai-Jun, E-mail: hebeiliu@outlook.com [Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000 (China); Liang, Guang-Lu [CT/MRI room, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000 (China); Gao, Bu-Lang, E-mail: browngao@163.com [Department of Medical Research, Shijiazhuang First Hospital, Shijiazhuang, Hebei, 050011 (China)

2017-04-15

Objective: To investigate the effect of the optimal monochromatic spectral computed tomography (CT) plus adaptive statistical iterative reconstruction on the improvement of the image quality of the superior mesenteric artery and vein. Materials and methods: The gemstone spectral CT angiographic data of 25 patients were reconstructed in the following three groups: 70 KeV, the optimal monochromatic imaging, and the optimal monochromatic plus 40%iterative reconstruction mode. The CT value, image noises (IN), background CT value and noises, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image scores of the vessels and surrounding tissues were analyzed. Results: In the 70 KeV, the optimal monochromatic and the optimal monochromatic images plus 40% iterative reconstruction group, the mean scores of image quality were 3.86, 4.24 and 4.25 for the superior mesenteric artery and 3.46, 3.78 and 3.81 for the superior mesenteric vein, respectively. The image quality scores for the optimal monochromatic and the optimal monochromatic plus 40% iterative reconstruction groups were significantly greater than for the 70 KeV group (P < 0.05). The vascular CT value, image noise, background noise, CNR and SNR were significantly (P < 0.001) greater in the optimal monochromatic and the optimal monochromatic images plus 40% iterative reconstruction group than in the 70 KeV group. The optimal monochromatic plus 40% iterative reconstruction group had significantly (P < 0.05) lower image and background noise but higher CNR and SNR than the other two groups. Conclusion: The optimal monochromatic imaging combined with 40% iterative reconstruction using low-contrast agent dosage and low injection rate can significantly improve the image quality of the superior mesenteric artery and vein.

PARALLEL ITERATIVE RECONSTRUCTION OF PHANTOM CATPHAN ON EXPERIMENTAL DATA

Directory of Open Access Journals (Sweden)

M. A. Mirzavand

2016-01-01

Full Text Available The principles of fast parallel iterative algorithms based on the use of graphics accelerators and OpenGL library are considered in the paper. The proposed approach provides simultaneous minimization of the residuals of the desired solution and total variation of the reconstructed three- dimensional image. The number of necessary input data, i. e. conical X-ray projections, can be reduced several times. It means in a corresponding number of times the possibility to reduce radiation exposure to the patient. At the same time maintain the necessary contrast and spatial resolution of threedimensional image of the patient. Heuristic iterative algorithm can be used as an alternative to the well-known three-dimensional Feldkamp algorithm.

Iterative Method of Regularization with Application of Advanced Technique for Detection of Contours

International Nuclear Information System (INIS)

Niedziela, T.; Stankiewicz, A.

2000-01-01

This paper proposes a novel iterative method of regularization with application of an advanced technique for detection of contours. To eliminate noises, the properties of convolution of functions are utilized. The method can be accomplished in a simple neural cellular network, which creates the possibility of extraction of contours by automatic image recognition equipment. (author)

Self-prior strategy for organ reconstruction in fluorescence molecular tomography.

Science.gov (United States)

Zhou, Yuan; Chen, Maomao; Su, Han; Luo, Jianwen

2017-10-01

The purpose of this study is to propose a strategy for organ reconstruction in fluorescence molecular tomography (FMT) without prior information from other imaging modalities, and to overcome the high cost and ionizing radiation caused by the traditional structural prior strategy. The proposed strategy is designed as an iterative architecture to solve the inverse problem of FMT. In each iteration, a short time Fourier transform (STFT) based algorithm is used to extract the self-prior information in the space-frequency energy spectrum with the assumption that the regions with higher fluorescence concentration have larger energy intensity, then the cost function of the inverse problem is modified by the self-prior information, and lastly an iterative Laplacian regularization algorithm is conducted to solve the updated inverse problem and obtains the reconstruction results. Simulations and in vivo experiments on liver reconstruction are carried out to test the performance of the self-prior strategy on organ reconstruction. The organ reconstruction results obtained by the proposed self-prior strategy are closer to the ground truth than those obtained by the iterative Tikhonov regularization (ITKR) method (traditional non-prior strategy). Significant improvements are shown in the evaluation indexes of relative locational error (RLE), relative error (RE) and contrast-to-noise ratio (CNR). The self-prior strategy improves the organ reconstruction results compared with the non-prior strategy and also overcomes the shortcomings of the traditional structural prior strategy. Various applications such as metabolic imaging and pharmacokinetic study can be aided by this strategy.

An ART iterative reconstruction algorithm for computed tomography of diffraction enhanced imaging

International Nuclear Information System (INIS)

Wang Zhentian; Zhang Li; Huang Zhifeng; Kang Kejun; Chen Zhiqiang; Fang Qiaoguang; Zhu Peiping

2009-01-01

X-ray diffraction enhanced imaging (DEI) has extremely high sensitivity for weakly absorbing low-Z samples in medical and biological fields. In this paper, we propose an Algebra Reconstruction Technique (ART) iterative reconstruction algorithm for computed tomography of diffraction enhanced imaging (DEI-CT). An Ordered Subsets (OS) technique is used to accelerate the ART reconstruction. Few-view reconstruction is also studied, and a partial differential equation (PDE) type filter which has the ability of edge-preserving and denoising is used to improve the image quality and eliminate the artifacts. The proposed algorithm is validated with both the numerical simulations and the experiment at the Beijing synchrotron radiation facility (BSRF). (authors)

An interior-point method for total variation regularized positron emission tomography image reconstruction

Science.gov (United States)

Bai, Bing

2012-03-01

There has been a lot of work on total variation (TV) regularized tomographic image reconstruction recently. Many of them use gradient-based optimization algorithms with a differentiable approximation of the TV functional. In this paper we apply TV regularization in Positron Emission Tomography (PET) image reconstruction. We reconstruct the PET image in a Bayesian framework, using Poisson noise model and TV prior functional. The original optimization problem is transformed to an equivalent problem with inequality constraints by adding auxiliary variables. Then we use an interior point method with logarithmic barrier functions to solve the constrained optimization problem. In this method, a series of points approaching the solution from inside the feasible region are found by solving a sequence of subproblems characterized by an increasing positive parameter. We use preconditioned conjugate gradient (PCG) algorithm to solve the subproblems directly. The nonnegativity constraint is enforced by bend line search. The exact expression of the TV functional is used in our calculations. Simulation results show that the algorithm converges fast and the convergence is insensitive to the values of the regularization and reconstruction parameters.

A fast method to emulate an iterative POCS image reconstruction algorithm.

Science.gov (United States)

Zeng, Gengsheng L

2017-10-01

Iterative image reconstruction algorithms are commonly used to optimize an objective function, especially when the objective function is nonquadratic. Generally speaking, the iterative algorithms are computationally inefficient. This paper presents a fast algorithm that has one backprojection and no forward projection. This paper derives a new method to solve an optimization problem. The nonquadratic constraint, for example, an edge-preserving denoising constraint is implemented as a nonlinear filter. The algorithm is derived based on the POCS (projections onto projections onto convex sets) approach. A windowed FBP (filtered backprojection) algorithm enforces the data fidelity. An iterative procedure, divided into segments, enforces edge-enhancement denoising. Each segment performs nonlinear filtering. The derived iterative algorithm is computationally efficient. It contains only one backprojection and no forward projection. Low-dose CT data are used for algorithm feasibility studies. The nonlinearity is implemented as an edge-enhancing noise-smoothing filter. The patient studies results demonstrate its effectiveness in processing low-dose x ray CT data. This fast algorithm can be used to replace many iterative algorithms. © 2017 American Association of Physicists in Medicine.

Comparison of adaptive statistical iterative reconstruction (ASiRTM) and model-based iterative reconstruction (VeoTM) for paediatric abdominal CT examinations: an observer performance study of diagnostic image quality

International Nuclear Information System (INIS)

Hultenmo, Maria; Caisander, Haakan; Mack, Karsten; Thilander-Klang, Anne

2016-01-01

The diagnostic image quality of 75 paediatric abdominal computed tomography (CT) examinations reconstructed with two different iterative reconstruction (IR) algorithms-adaptive statistical IR (ASiR TM ) and model-based IR (Veo TM )-was compared. Axial and coronal images were reconstructed with 70 % ASiR with the Soft TM convolution kernel and with the Veo algorithm. The thickness of the reconstructed images was 2.5 or 5 mm depending on the scanning protocol used. Four radiologists graded the delineation of six abdominal structures and the diagnostic usefulness of the image quality. The Veo reconstruction significantly improved the visibility of most of the structures compared with ASiR in all subgroups of images. For coronal images, the Veo reconstruction resulted in significantly improved ratings of the diagnostic use of the image quality compared with the ASiR reconstruction. This was not seen for the axial images. The greatest improvement using Veo reconstruction was observed for the 2.5 mm coronal slices. (authors)

Application of iterative reconstruction in dynamic studies

International Nuclear Information System (INIS)

Meikle, S.R.

1998-01-01

Full text: The conventional approach to analysing dynamic tomographic data (SPECT or PET) is to reconstruct projections corresponding to each time interval separately and then fit a suitable tracer kinetic model to the dynamic sequence (method 1 ) . This approach assumes that the tracer distribution remains static during any given time interval and, for practical reasons, filtered back-projection (FBP) is the preferred reconstruction algorithm. However, alternative approaches exist which lend themselves to iterative algorithms, such as EM. One approach is to fit the model directly to the projection data, followed by EM reconstruction of the parameter estimates (method 2). This requires that the tracer model can be expressed as a linear function of the unknown model parameters. A third alternative is to incorporate the tracer model into the reconstruction algorithm (method 3). Such an extension was described during the early development of the EM algorithm, referred to as the EM parametric image reconstruction algorithm (EM-PIRA). We have investigated these various strategies for analysing dynamic data and their relative pros and cons. Tracer modelling was performed using a general model, referred to as spectral analysis, which makes no restriction on the number of physiological compartments and satisfies the linearity requirement of method 2. A kinetic software phantom was created and used to test the convergence and noise properties of the different approaches. In summary, method 2 is the most practical as it reduces the number of reconstructions by at least an order of magnitude and provides improved signal-to-noise ratios compared with method 1. EM-PIRA allows greater flexibility in the choice of parametric images and appears to have a regularising effect on convergence. Methods 2 and 3 are also better suited to dynamic scanning with a rotating camera, as they can potentially account for changes in tracer distribution between projections

Quantitative analysis of emphysema and airway measurements according to iterative reconstruction algorithms: comparison of filtered back projection, adaptive statistical iterative reconstruction and model-based iterative reconstruction

International Nuclear Information System (INIS)

Choo, Ji Yung; Goo, Jin Mo; Park, Chang Min; Park, Sang Joon; Lee, Chang Hyun; Shim, Mi-Suk

2014-01-01

To evaluate filtered back projection (FBP) and two iterative reconstruction (IR) algorithms and their effects on the quantitative analysis of lung parenchyma and airway measurements on computed tomography (CT) images. Low-dose chest CT obtained in 281 adult patients were reconstructed using three algorithms: FBP, adaptive statistical IR (ASIR) and model-based IR (MBIR). Measurements of each dataset were compared: total lung volume, emphysema index (EI), airway measurements of the lumen and wall area as well as average wall thickness. Accuracy of airway measurements of each algorithm was also evaluated using an airway phantom. EI using a threshold of -950 HU was significantly different among the three algorithms in decreasing order of FBP (2.30 %), ASIR (1.49 %) and MBIR (1.20 %) (P < 0.01). Wall thickness was also significantly different among the three algorithms with FBP (2.09 mm) demonstrating thicker walls than ASIR (2.00 mm) and MBIR (1.88 mm) (P < 0.01). Airway phantom analysis revealed that MBIR showed the most accurate value for airway measurements. The three algorithms presented different EIs and wall thicknesses, decreasing in the order of FBP, ASIR and MBIR. Thus, care should be taken in selecting the appropriate IR algorithm on quantitative analysis of the lung. (orig.)

Quantitative analysis of emphysema and airway measurements according to iterative reconstruction algorithms: comparison of filtered back projection, adaptive statistical iterative reconstruction and model-based iterative reconstruction

Energy Technology Data Exchange (ETDEWEB)

Choo, Ji Yung [Seoul National University Medical Research Center, Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul (Korea, Republic of); Korea University Ansan Hospital, Ansan-si, Department of Radiology, Gyeonggi-do (Korea, Republic of); Goo, Jin Mo; Park, Chang Min; Park, Sang Joon [Seoul National University Medical Research Center, Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul (Korea, Republic of); Seoul National University, Cancer Research Institute, Seoul (Korea, Republic of); Lee, Chang Hyun; Shim, Mi-Suk [Seoul National University Medical Research Center, Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul (Korea, Republic of)

2014-04-15

To evaluate filtered back projection (FBP) and two iterative reconstruction (IR) algorithms and their effects on the quantitative analysis of lung parenchyma and airway measurements on computed tomography (CT) images. Low-dose chest CT obtained in 281 adult patients were reconstructed using three algorithms: FBP, adaptive statistical IR (ASIR) and model-based IR (MBIR). Measurements of each dataset were compared: total lung volume, emphysema index (EI), airway measurements of the lumen and wall area as well as average wall thickness. Accuracy of airway measurements of each algorithm was also evaluated using an airway phantom. EI using a threshold of -950 HU was significantly different among the three algorithms in decreasing order of FBP (2.30 %), ASIR (1.49 %) and MBIR (1.20 %) (P < 0.01). Wall thickness was also significantly different among the three algorithms with FBP (2.09 mm) demonstrating thicker walls than ASIR (2.00 mm) and MBIR (1.88 mm) (P < 0.01). Airway phantom analysis revealed that MBIR showed the most accurate value for airway measurements. The three algorithms presented different EIs and wall thicknesses, decreasing in the order of FBP, ASIR and MBIR. Thus, care should be taken in selecting the appropriate IR algorithm on quantitative analysis of the lung. (orig.)

Cardiovascular CT angiography in neonates and children : Image quality and potential for radiation dose reduction with iterative image reconstruction techniques

NARCIS (Netherlands)

Tricarico, Francesco; Hlavacek, Anthony M.; Schoepf, U. Joseph; Ebersberger, Ullrich; Nance, John W.; Vliegenthart, Rozemarijn; Cho, Young Jun; Spears, J. Reid; Secchi, Francesco; Savino, Giancarlo; Marano, Riccardo; Schoenberg, Stefan O.; Bonomo, Lorenzo; Apfaltrer, Paul

To evaluate image quality (IQ) of low-radiation-dose paediatric cardiovascular CT angiography (CTA), comparing iterative reconstruction in image space (IRIS) and sinogram-affirmed iterative reconstruction (SAFIRE) with filtered back-projection (FBP) and estimate the potential for further dose

Application of a dual-resolution voxelization scheme to compressed-sensing (CS)-based iterative reconstruction in digital tomosynthesis (DTS)

Science.gov (United States)

Park, S. Y.; Kim, G. A.; Cho, H. S.; Park, C. K.; Lee, D. Y.; Lim, H. W.; Lee, H. W.; Kim, K. S.; Kang, S. Y.; Park, J. E.; Kim, W. S.; Jeon, D. H.; Je, U. K.; Woo, T. H.; Oh, J. E.

2018-02-01

In recent digital tomosynthesis (DTS), iterative reconstruction methods are often used owing to the potential to provide multiplanar images of superior image quality to conventional filtered-backprojection (FBP)-based methods. However, they require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, we propose a new DTS reconstruction method incorporated with a dual-resolution voxelization scheme in attempt to overcome these difficulties, in which the voxels outside a small region-of-interest (ROI) containing target diagnosis are binned by 2 × 2 × 2 while the voxels inside the ROI remain unbinned. We considered a compressed-sensing (CS)-based iterative algorithm with a dual-constraint strategy for more accurate DTS reconstruction. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative DTS reconstruction, keeping the image quality inside the ROI not much degraded. A binning size of 2 × 2 × 2 required only about 31.9% computational memory and about 2.6% reconstruction time, compared to those for no binning case. The reconstruction quality was evaluated in terms of the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal-quality index (UQI).

l1- and l2-Norm Joint Regularization Based Sparse Signal Reconstruction Scheme

Directory of Open Access Journals (Sweden)

Chanzi Liu

2016-01-01

Full Text Available Many problems in signal processing and statistical inference involve finding sparse solution to some underdetermined linear system of equations. This is also the application condition of compressive sensing (CS which can find the sparse solution from the measurements far less than the original signal. In this paper, we propose l1- and l2-norm joint regularization based reconstruction framework to approach the original l0-norm based sparseness-inducing constrained sparse signal reconstruction problem. Firstly, it is shown that, by employing the simple conjugate gradient algorithm, the new formulation provides an effective framework to deduce the solution as the original sparse signal reconstruction problem with l0-norm regularization item. Secondly, the upper reconstruction error limit is presented for the proposed sparse signal reconstruction framework, and it is unveiled that a smaller reconstruction error than l1-norm relaxation approaches can be realized by using the proposed scheme in most cases. Finally, simulation results are presented to validate the proposed sparse signal reconstruction approach.

Computer-assisted solid lung nodule 3D volumetry on CT. Influence of scan mode and iterative reconstruction. A CT phantom study

International Nuclear Information System (INIS)

Coenen, Adriaan; Honda, Osamu; Tomiyama, Noriyuki; Jagt, Eric J. van der

2013-01-01

The objective of this study was to evaluate the effect of high-resolution scan mode and iterative reconstruction on lung nodule 3D volumetry. Solid nodules with various sizes (5, 8, 10 and 12 mm) were placed inside a chest phantom. CT images were obtained with various tube currents, scan modes (conventional mode, high-resolution mode) and iterative reconstructions [0, 50 and 100% blending of adaptive statistical iterative reconstruction (ASiR) and filtered back projection]. The nodule volumes were calculated using semiautomatic software and compared with the assumed volume from the nodules. The mean absolute and relative percentage error improved when using iterative reconstruction especially when using the conventional scan mode; however, this effect was not significant. Significant reduction in volume overestimation was observed when using high-resolution scan mode (P=0.011). The high-resolution mode significantly reduces the volume overestimation of 3D volumetry. Iterative reconstruction shows a reduction in volume overestimation and error margin especially with the conventional scan mode; however, this effect was not significant. (author)

Low-dose 4D cone-beam CT via joint spatiotemporal regularization of tensor framelet and nonlocal total variation

Science.gov (United States)

Han, Hao; Gao, Hao; Xing, Lei

2017-08-01

Excessive radiation exposure is still a major concern in 4D cone-beam computed tomography (4D-CBCT) due to its prolonged scanning duration. Radiation dose can be effectively reduced by either under-sampling the x-ray projections or reducing the x-ray flux. However, 4D-CBCT reconstruction under such low-dose protocols is prone to image artifacts and noise. In this work, we propose a novel joint regularization-based iterative reconstruction method for low-dose 4D-CBCT. To tackle the under-sampling problem, we employ spatiotemporal tensor framelet (STF) regularization to take advantage of the spatiotemporal coherence of the patient anatomy in 4D images. To simultaneously suppress the image noise caused by photon starvation, we also incorporate spatiotemporal nonlocal total variation (SNTV) regularization to make use of the nonlocal self-recursiveness of anatomical structures in the spatial and temporal domains. Under the joint STF-SNTV regularization, the proposed iterative reconstruction approach is evaluated first using two digital phantoms and then using physical experiment data in the low-dose context of both under-sampled and noisy projections. Compared with existing approaches via either STF or SNTV regularization alone, the presented hybrid approach achieves improved image quality, and is particularly effective for the reconstruction of low-dose 4D-CBCT data that are not only sparse but noisy.

X-ray phase laminography with a grating interferometer using iterative reconstruction

International Nuclear Information System (INIS)

Harasse, Sébastien; Yashiro, Wataru; Momose, Atsushi

2012-01-01

X-ray phase computed laminography is performed using a Talbot interferometer and synchrotron radiation. An iterative reconstruction algorithm which includes prior information about limited support, range of values and sparsity of the imaged object has been developped. It allows the reconstruction of objects with an improved resolution of the unsampled frequencies, compared to the classical filtered backprojection. The imaging method, demonstrated for a nylon mesh sample and a leaf sample, shows promising results for the imaging of flat, laterally extended objects made of low absorbing elements.

Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient.

Science.gov (United States)

Shi, Junwei; Zhang, Bin; Liu, Fei; Luo, Jianwen; Bai, Jing

2013-09-15

For the ill-posed fluorescent molecular tomography (FMT) inverse problem, the L1 regularization can protect the high-frequency information like edges while effectively reduce the image noise. However, the state-of-the-art L1 regularization-based algorithms for FMT reconstruction are expensive in memory, especially for large-scale problems. An efficient L1 regularization-based reconstruction algorithm based on nonlinear conjugate gradient with restarted strategy is proposed to increase the computational speed with low memory consumption. The reconstruction results from phantom experiments demonstrate that the proposed algorithm can obtain high spatial resolution and high signal-to-noise ratio, as well as high localization accuracy for fluorescence targets.

Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction.

Science.gov (United States)

Barca, Patrizio; Giannelli, Marco; Fantacci, Maria Evelina; Caramella, Davide

2018-06-01

Computed tomography (CT) is a useful and widely employed imaging technique, which represents the largest source of population exposure to ionizing radiation in industrialized countries. Adaptive Statistical Iterative Reconstruction (ASIR) is an iterative reconstruction algorithm with the potential to allow reduction of radiation exposure while preserving diagnostic information. The aim of this phantom study was to assess the performance of ASIR, in terms of a number of image quality indices, when different reconstruction blending levels are employed. CT images of the Catphan-504 phantom were reconstructed using conventional filtered back-projection (FBP) and ASIR with reconstruction blending levels of 20, 40, 60, 80, and 100%. Noise, noise power spectrum (NPS), contrast-to-noise ratio (CNR) and modulation transfer function (MTF) were estimated for different scanning parameters and contrast objects. Noise decreased and CNR increased non-linearly up to 50 and 100%, respectively, with increasing blending level of reconstruction. Also, ASIR has proven to modify the NPS curve shape. The MTF of ASIR reconstructed images depended on tube load/contrast and decreased with increasing blending level of reconstruction. In particular, for low radiation exposure and low contrast acquisitions, ASIR showed lower performance than FBP, in terms of spatial resolution for all blending levels of reconstruction. CT image quality varies substantially with the blending level of reconstruction. ASIR has the potential to reduce noise whilst maintaining diagnostic information in low radiation exposure CT imaging. Given the opposite variation of CNR and spatial resolution with the blending level of reconstruction, it is recommended to use an optimal value of this parameter for each specific clinical application.

Image quality of CT angiography in young children with congenital heart disease: a comparison between the sinogram-affirmed iterative reconstruction (SAFIRE) and advanced modelled iterative reconstruction (ADMIRE) algorithms.

Science.gov (United States)

Nam, S B; Jeong, D W; Choo, K S; Nam, K J; Hwang, J-Y; Lee, J W; Kim, J Y; Lim, S J

2017-12-01

To compare the image quality of computed tomography angiography (CTA) reconstructed by sinogram-affirmed iterative reconstruction (SAFIRE) with that of advanced modelled iterative reconstruction (ADMIRE) in children with congenital heart disease (CHD). Thirty-one children (8.23±13.92 months) with CHD who underwent CTA were enrolled. Images were reconstructed using SAFIRE (strength 5) and ADMIRE (strength 5). Objective image qualities (attenuation, noise) were measured in the great vessels and heart chambers. Two radiologists independently calculated the contrast-to-noise ratio (CNR) by measuring the intensity and noise of the myocardial walls. Subjective noise, diagnostic confidence, and sharpness at the level prior to the first branch of the main pulmonary artery were also graded by the two radiologists independently. The objective image noise of ADMIRE was significantly lower than that of SAFIRE in the right atrium, right ventricle, and myocardial wall (p0.05). The mean CNR values were 21.56±10.80 for ADMIRE and 18.21±6.98 for SAFIRE, which were significantly different (p0.05). CTA using ADMIRE was superior to SAFIRE when comparing the objective and subjective image quality in children with CHD. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Iterative Nonlinear Tikhonov Algorithm with Constraints for Electromagnetic Tomography

Science.gov (United States)

Xu, Feng; Deshpande, Manohar

2012-01-01

Low frequency electromagnetic tomography such as the capacitance tomography (ECT) has been proposed for monitoring and mass-gauging of gas-liquid two-phase system under microgravity condition in NASA's future long-term space missions. Due to the ill-posed inverse problem of ECT, images reconstructed using conventional linear algorithms often suffer from limitations such as low resolution and blurred edges. Hence, new efficient high resolution nonlinear imaging algorithms are needed for accurate two-phase imaging. The proposed Iterative Nonlinear Tikhonov Regularized Algorithm with Constraints (INTAC) is based on an efficient finite element method (FEM) forward model of quasi-static electromagnetic problem. It iteratively minimizes the discrepancy between FEM simulated and actual measured capacitances by adjusting the reconstructed image using the Tikhonov regularized method. More importantly, it enforces the known permittivity of two phases to the unknown pixels which exceed the reasonable range of permittivity in each iteration. This strategy does not only stabilize the converging process, but also produces sharper images. Simulations show that resolution improvement of over 2 times can be achieved by INTAC with respect to conventional approaches. Strategies to further improve spatial imaging resolution are suggested, as well as techniques to accelerate nonlinear forward model and thus increase the temporal resolution.

Reconstruction of a cone-beam CT image via forward iterative projection matching

International Nuclear Information System (INIS)

Brock, R. Scott; Docef, Alen; Murphy, Martin J.

2010-01-01

Purpose: To demonstrate the feasibility of reconstructing a cone-beam CT (CBCT) image by deformably altering a prior fan-beam CT (FBCT) image such that it matches the anatomy portrayed in the CBCT projection data set. Methods: A prior FBCT image of the patient is assumed to be available as a source image. A CBCT projection data set is obtained and used as a target image set. A parametrized deformation model is applied to the source FBCT image, digitally reconstructed radiographs (DRRs) that emulate the CBCT projection image geometry are calculated and compared to the target CBCT projection data, and the deformation model parameters are adjusted iteratively until the DRRs optimally match the CBCT projection data set. The resulting deformed FBCT image is hypothesized to be an accurate representation of the patient's anatomy imaged by the CBCT system. The process is demonstrated via numerical simulation. A known deformation is applied to a prior FBCT image and used to create a synthetic set of CBCT target projections. The iterative projection matching process is then applied to reconstruct the deformation represented in the synthetic target projections; the reconstructed deformation is then compared to the known deformation. The sensitivity of the process to the number of projections and the DRR/CBCT projection mismatch is explored by systematically adding noise to and perturbing the contrast of the target projections relative to the iterated source DRRs and by reducing the number of projections. Results: When there is no noise or contrast mismatch in the CBCT projection images, a set of 64 projections allows the known deformed CT image to be reconstructed to within a nRMS error of 1% and the known deformation to within a nRMS error of 7%. A CT image nRMS error of less than 4% is maintained at noise levels up to 3% of the mean projection intensity, at which the deformation error is 13%. At 1% noise level, the number of projections can be reduced to 8 while maintaining

High resolution reconstruction of PET images using the iterative OSEM algorithm

International Nuclear Information System (INIS)

Doll, J.; Bublitz, O.; Werling, A.; Haberkorn, U.; Semmler, W.; Adam, L.E.; Pennsylvania Univ., Philadelphia, PA; Brix, G.

2004-01-01

Aim: Improvement of the spatial resolution in positron emission tomography (PET) by incorporation of the image-forming characteristics of the scanner into the process of iterative image reconstruction. Methods: All measurements were performed at the whole-body PET system ECAT EXACT HR + in 3D mode. The acquired 3D sinograms were sorted into 2D sinograms by means of the Fourier rebinning (FORE) algorithm, which allows the usage of 2D algorithms for image reconstruction. The scanner characteristics were described by a spatially variant line-spread function (LSF), which was determined from activated copper-64 line sources. This information was used to model the physical degradation processes in PET measurements during the course of 2D image reconstruction with the iterative OSEM algorithm. To assess the performance of the high-resolution OSEM algorithm, phantom measurements performed at a cylinder phantom, the hotspot Jaszczack phantom, and the 3D Hoffmann brain phantom as well as different patient examinations were analyzed. Results: Scanner characteristics could be described by a Gaussian-shaped LSF with a full-width at half-maximum increasing from 4.8 mm at the center to 5.5 mm at a radial distance of 10.5 cm. Incorporation of the LSF into the iteration formula resulted in a markedly improved resolution of 3.0 and 3.5 mm, respectively. The evaluation of phantom and patient studies showed that the high-resolution OSEM algorithm not only lead to a better contrast resolution in the reconstructed activity distributions but also to an improved accuracy in the quantification of activity concentrations in small structures without leading to an amplification of image noise or even the occurrence of image artifacts. Conclusion: The spatial and contrast resolution of PET scans can markedly be improved by the presented image restauration algorithm, which is of special interest for the examination of both patients with brain disorders and small animals. (orig.)

An attenuated projector-backprojector for iterative SPECT reconstruction

International Nuclear Information System (INIS)

Gullberg, G.T.; Pelc, N.J.; Huesman, R.H.; Budinger, T.F.; Malko, J.A.

1985-01-01

A new ray-driven projector-backprojector which can easily be adapted for hardware implementation is described and simulated in software. The projector-backprojector discretely models the attenuated Radon transform of a source distributed within an attenuating medium as line integrals of discrete pixels, obtained using the standard sampling technique of averaging the emission source or attenuation distribution over small square regions. Attenuation factors are calculated for each pixel during the projection and backprojection operations instead of using precalculated values. The calculation of the factors requires a specification of the attenuation distribution, estimated either from an assumed constant distribution and an approximate body outline or from transmission measurements. The distribution of attenuation coefficients is stored in memory for efficient access during the projection and backprojection operations. The reconstruction of the source distribution is obtained by using a conjugate gradient or SIRT type iterative algorithm which requires one projection and one backprojection operation for each iteration. (author)

A linear iterative unfolding method

International Nuclear Information System (INIS)

László, András

2012-01-01

A frequently faced task in experimental physics is to measure the probability distribution of some quantity. Often this quantity to be measured is smeared by a non-ideal detector response or by some physical process. The procedure of removing this smearing effect from the measured distribution is called unfolding, and is a delicate problem in signal processing, due to the well-known numerical ill behavior of this task. Various methods were invented which, given some assumptions on the initial probability distribution, try to regularize the unfolding problem. Most of these methods definitely introduce bias into the estimate of the initial probability distribution. We propose a linear iterative method (motivated by the Neumann series / Landweber iteration known in functional analysis), which has the advantage that no assumptions on the initial probability distribution is needed, and the only regularization parameter is the stopping order of the iteration, which can be used to choose the best compromise between the introduced bias and the propagated statistical and systematic errors. The method is consistent: 'binwise' convergence to the initial probability distribution is proved in absence of measurement errors under a quite general condition on the response function. This condition holds for practical applications such as convolutions, calorimeter response functions, momentum reconstruction response functions based on tracking in magnetic field etc. In presence of measurement errors, explicit formulae for the propagation of the three important error terms is provided: bias error (distance from the unknown to-be-reconstructed initial distribution at a finite iteration order), statistical error, and systematic error. A trade-off between these three error terms can be used to define an optimal iteration stopping criterion, and the errors can be estimated there. We provide a numerical C library for the implementation of the method, which incorporates automatic

Coronary artery plaques: Cardiac CT with model-based and adaptive-statistical iterative reconstruction technique

International Nuclear Information System (INIS)

Scheffel, Hans; Stolzmann, Paul; Schlett, Christopher L.; Engel, Leif-Christopher; Major, Gyöngi Petra; Károlyi, Mihály; Do, Synho; Maurovich-Horvat, Pál; Hoffmann, Udo

2012-01-01

Objectives: To compare image quality of coronary artery plaque visualization at CT angiography with images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model based iterative reconstruction (MBIR) techniques. Methods: The coronary arteries of three ex vivo human hearts were imaged by CT and reconstructed with FBP, ASIR and MBIR. Coronary cross-sectional images were co-registered between the different reconstruction techniques and assessed for qualitative and quantitative image quality parameters. Readers were blinded to the reconstruction algorithm. Results: A total of 375 triplets of coronary cross-sectional images were co-registered. Using MBIR, 26% of the images were rated as having excellent overall image quality, which was significantly better as compared to ASIR and FBP (4% and 13%, respectively, all p < 0.001). Qualitative assessment of image noise demonstrated a noise reduction by using ASIR as compared to FBP (p < 0.01) and further noise reduction by using MBIR (p < 0.001). The contrast-to-noise-ratio (CNR) using MBIR was better as compared to ASIR and FBP (44 ± 19, 29 ± 15, 26 ± 9, respectively; all p < 0.001). Conclusions: Using MBIR improved image quality, reduced image noise and increased CNR as compared to the other available reconstruction techniques. This may further improve the visualization of coronary artery plaque and allow radiation reduction.

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

International Nuclear Information System (INIS)

Xu, Shiyu; Chen, Ying; Lu, Jianping; Zhou, Otto

2015-01-01

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair based prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

Energy Technology Data Exchange (ETDEWEB)

Xu, Shiyu, E-mail: shiyu.xu@gmail.com; Chen, Ying, E-mail: adachen@siu.edu [Department of Electrical and Computer Engineering, Southern Illinois University Carbondale, Carbondale, Illinois 62901 (United States); Lu, Jianping; Zhou, Otto [Department of Physics and Astronomy and Curriculum in Applied Sciences and Engineering, University of North Carolina Chapel Hill, Chapel Hill, North Carolina 27599 (United States)

2015-09-15

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair based prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.

Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR Technique in EIT: A Simulation Study

Directory of Open Access Journals (Sweden)

Tushar Kanti Bera

2011-06-01

Full Text Available A Block Matrix based Multiple Regularization (BMMR technique is proposed for improving conductivity image quality in EIT. The response matrix (JTJ has been partitioned into several sub-block matrices and the highest eigenvalue of each sub-block matrices has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domain with circular inhomogeneity and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR and modified Levenberg-Marquardt Regularization (LMR methods. It is observed that the BMMR technique reduces the projection error and solution error and improves the conductivity reconstruction in EIT. Result show that the BMMR method also improves the image contrast and inhomogeneity conductivity profile and hence the reconstructed image quality is enhanced. ;doi:10.5617/jeb.170 J Electr Bioimp, vol. 2, pp. 33-47, 2011

Influence of Ultra-Low-Dose and Iterative Reconstructions on the Visualization of Orbital Soft Tissues on Maxillofacial CT.

Science.gov (United States)

Widmann, G; Juranek, D; Waldenberger, F; Schullian, P; Dennhardt, A; Hoermann, R; Steurer, M; Gassner, E-M; Puelacher, W

2017-08-01

Dose reduction on CT scans for surgical planning and postoperative evaluation of midface and orbital fractures is an important concern. The purpose of this study was to evaluate the variability of various low-dose and iterative reconstruction techniques on the visualization of orbital soft tissues. Contrast-to-noise ratios of the optic nerve and inferior rectus muscle and subjective scores of a human cadaver were calculated from CT with a reference dose protocol (CT dose index volume = 36.69 mGy) and a subsequent series of low-dose protocols (LDPs I-4: CT dose index volume = 4.18, 2.64, 0.99, and 0.53 mGy) with filtered back-projection (FBP) and adaptive statistical iterative reconstruction (ASIR)-50, ASIR-100, and model-based iterative reconstruction. The Dunn Multiple Comparison Test was used to compare each combination of protocols (α = .05). Compared with the reference dose protocol with FBP, the following statistically significant differences in contrast-to-noise ratios were shown (all, P ≤ .012) for the following: 1) optic nerve: LDP-I with FBP; LDP-II with FBP and ASIR-50; LDP-III with FBP, ASIR-50, and ASIR-100; and LDP-IV with FBP, ASIR-50, and ASIR-100; and 2) inferior rectus muscle: LDP-II with FBP, LDP-III with FBP and ASIR-50, and LDP-IV with FBP, ASIR-50, and ASIR-100. Model-based iterative reconstruction showed the best contrast-to-noise ratio in all images and provided similar subjective scores for LDP-II. ASIR-50 had no remarkable effect, and ASIR-100, a small effect on subjective scores. Compared with a reference dose protocol with FBP, model-based iterative reconstruction may show similar diagnostic visibility of orbital soft tissues at a CT dose index volume of 2.64 mGy. Low-dose technology and iterative reconstruction technology may redefine current reference dose levels in maxillofacial CT. © 2017 by American Journal of Neuroradiology.

Fisher's method of scoring in statistical image reconstruction: comparison of Jacobi and Gauss-Seidel iterative schemes.

Science.gov (United States)

Hudson, H M; Ma, J; Green, P

1994-01-01

Many algorithms for medical image reconstruction adopt versions of the expectation-maximization (EM) algorithm. In this approach, parameter estimates are obtained which maximize a complete data likelihood or penalized likelihood, in each iteration. Implicitly (and sometimes explicitly) penalized algorithms require smoothing of the current reconstruction in the image domain as part of their iteration scheme. In this paper, we discuss alternatives to EM which adapt Fisher's method of scoring (FS) and other methods for direct maximization of the incomplete data likelihood. Jacobi and Gauss-Seidel methods for non-linear optimization provide efficient algorithms applying FS in tomography. One approach uses smoothed projection data in its iterations. We investigate the convergence of Jacobi and Gauss-Seidel algorithms with clinical tomographic projection data.

Dose reduction with adaptive statistical iterative reconstruction for paediatric CT: phantom study and clinical experience on chest and abdomen CT.

Science.gov (United States)

Gay, F; Pavia, Y; Pierrat, N; Lasalle, S; Neuenschwander, S; Brisse, H J

2014-01-01

To assess the benefit and limits of iterative reconstruction of paediatric chest and abdominal computed tomography (CT). The study compared adaptive statistical iterative reconstruction (ASIR) with filtered back projection (FBP) on 64-channel MDCT. A phantom study was first performed using variable tube potential, tube current and ASIR settings. The assessed image quality indices were the signal-to-noise ratio (SNR), the noise power spectrum, low contrast detectability (LCD) and spatial resolution. A clinical retrospective study of 26 children (M:F = 14/12, mean age: 4 years, range: 1-9 years) was secondarily performed allowing comparison of 18 chest and 14 abdominal CT pairs, one with a routine CT dose and FBP reconstruction, and the other with 30 % lower dose and 40 % ASIR reconstruction. Two radiologists independently compared the images for overall image quality, noise, sharpness and artefacts, and measured image noise. The phantom study demonstrated a significant increase in SNR without impairment of the LCD or spatial resolution, except for tube current values below 30-50 mA. On clinical images, no significant difference was observed between FBP and reduced dose ASIR images. Iterative reconstruction allows at least 30 % dose reduction in paediatric chest and abdominal CT, without impairment of image quality. • Iterative reconstruction helps lower radiation exposure levels in children undergoing CT. • Adaptive statistical iterative reconstruction (ASIR) significantly increases SNR without impairing spatial resolution. • For abdomen and chest CT, ASIR allows at least a 30 % dose reduction.

Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms

International Nuclear Information System (INIS)

Tang Jie; Nett, Brian E; Chen Guanghong

2009-01-01

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

Anisotropic modeling and joint-MAP stitching for improved ultrasound model-based iterative reconstruction of large and thick specimens

Energy Technology Data Exchange (ETDEWEB)

Almansouri, Hani [Purdue University; Venkatakrishnan, Singanallur V. [ORNL; Clayton, Dwight A. [ORNL; Polsky, Yarom [ORNL; Bouman, Charles [Purdue University; Santos-Villalobos, Hector J. [ORNL

2018-04-01

One-sided non-destructive evaluation (NDE) is widely used to inspect materials, such as concrete structures in nuclear power plants (NPP). A widely used method for one-sided NDE is the synthetic aperture focusing technique (SAFT). The SAFT algorithm produces reasonable results when inspecting simple structures. However, for complex structures, such as heavily reinforced thick concrete structures, SAFT results in artifacts and hence there is a need for a more sophisticated inversion technique. Model-based iterative reconstruction (MBIR) algorithms, which are typically equivalent to regularized inversion techniques, offer a powerful framework to incorporate complex models for the physics, detector miscalibrations and the materials being imaged to obtain high quality reconstructions. Previously, we have proposed an ultrasonic MBIR method that signifcantly improves reconstruction quality compared to SAFT. However, the method made some simplifying assumptions on the propagation model and did not disucss ways to handle data that is obtained by raster scanning a system over a surface to inspect large regions. In this paper, we propose a novel MBIR algorithm that incorporates an anisotropic forward model and allows for the joint processing of data obtained from a system that raster scans a large surface. We demonstrate that the new MBIR method can produce dramatic improvements in reconstruction quality compared to SAFT and suppresses articfacts compared to the perviously presented MBIR approach.

Limiting CT radiation dose in children with craniosynostosis: phantom study using model-based iterative reconstruction

Energy Technology Data Exchange (ETDEWEB)

Kaasalainen, Touko; Lampinen, Anniina [University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Center, Radiology, POB 340, Helsinki (Finland); University of Helsinki, Department of Physics, Helsinki (Finland); Palmu, Kirsi [University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Center, Radiology, POB 340, Helsinki (Finland); School of Science, Aalto University, Department of Biomedical Engineering and Computational Science, Helsinki (Finland); Reijonen, Vappu; Kortesniemi, Mika [University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Center, Radiology, POB 340, Helsinki (Finland); Leikola, Junnu [University of Helsinki and Helsinki University Hospital, Department of Plastic Surgery, Helsinki (Finland); Kivisaari, Riku [University of Helsinki and Helsinki University Hospital, Department of Neurosurgery, Helsinki (Finland)

2015-09-15

Medical professionals need to exercise particular caution when developing CT scanning protocols for children who require multiple CT studies, such as those with craniosynostosis. To evaluate the utility of ultra-low-dose CT protocols with model-based iterative reconstruction techniques for craniosynostosis imaging. We scanned two pediatric anthropomorphic phantoms with a 64-slice CT scanner using different low-dose protocols for craniosynostosis. We measured organ doses in the head region with metal-oxide-semiconductor field-effect transistor (MOSFET) dosimeters. Numerical simulations served to estimate organ and effective doses. We objectively and subjectively evaluated the quality of images produced by adaptive statistical iterative reconstruction (ASiR) 30%, ASiR 50% and Veo (all by GE Healthcare, Waukesha, WI). Image noise and contrast were determined for different tissues. Mean organ dose with the newborn phantom was decreased up to 83% compared to the routine protocol when using ultra-low-dose scanning settings. Similarly, for the 5-year phantom the greatest radiation dose reduction was 88%. The numerical simulations supported the findings with MOSFET measurements. The image quality remained adequate with Veo reconstruction, even at the lowest dose level. Craniosynostosis CT with model-based iterative reconstruction could be performed with a 20-μSv effective dose, corresponding to the radiation exposure of plain skull radiography, without compromising required image quality. (orig.)

Constrained least squares regularization in PET

International Nuclear Information System (INIS)

Choudhury, K.R.; O'Sullivan, F.O.

1996-01-01

Standard reconstruction methods used in tomography produce images with undesirable negative artifacts in background and in areas of high local contrast. While sophisticated statistical reconstruction methods can be devised to correct for these artifacts, their computational implementation is excessive for routine operational use. This work describes a technique for rapid computation of approximate constrained least squares regularization estimates. The unique feature of the approach is that it involves no iterative projection or backprojection steps. This contrasts with the familiar computationally intensive algorithms based on algebraic reconstruction (ART) or expectation-maximization (EM) methods. Experimentation with the new approach for deconvolution and mixture analysis shows that the root mean square error quality of estimators based on the proposed algorithm matches and usually dominates that of more elaborate maximum likelihood, at a fraction of the computational effort

Image quality of ct angiography using model-based iterative reconstruction in infants with congenital heart disease: Comparison with filtered back projection and hybrid iterative reconstruction

Energy Technology Data Exchange (ETDEWEB)

Jia, Qianjun, E-mail: jiaqianjun@126.com [Southern Medical University, Guangzhou, Guangdong (China); Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong (China); Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong (China); Zhuang, Jian, E-mail: zhuangjian5413@tom.com [Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong (China); Jiang, Jun, E-mail: 81711587@qq.com [Department of Radiology, Shenzhen Second People’s Hospital, Shenzhen, Guangdong (China); Li, Jiahua, E-mail: 970872804@qq.com [Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong (China); Huang, Meiping, E-mail: huangmeiping_vip@163.com [Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong (China); Southern Medical University, Guangzhou, Guangdong (China); Liang, Changhong, E-mail: cjr.lchh@vip.163.com [Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong (China); Southern Medical University, Guangzhou, Guangdong (China)

2017-01-15

Purpose: To compare the image quality, rate of coronary artery visualization and diagnostic accuracy of 256-slice multi-detector computed tomography angiography (CTA) with prospective electrocardiographic (ECG) triggering at a tube voltage of 80 kVp between 3 reconstruction algorithms (filtered back projection (FBP), hybrid iterative reconstruction (iDose{sup 4}) and iterative model reconstruction (IMR)) in infants with congenital heart disease (CHD). Methods: Fifty-one infants with CHD who underwent cardiac CTA in our institution between December 2014 and March 2015 were included. The effective radiation doses were calculated. Imaging data were reconstructed using the FBP, iDose{sup 4} and IMR algorithms. Parameters of objective image quality (noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR)); subjective image quality (overall image quality, image noise and margin sharpness); coronary artery visibility; and diagnostic accuracy for the three algorithms were measured and compared. Results: The mean effective radiation dose was 0.61 ± 0.32 mSv. Compared to FBP and iDose{sup 4}, IMR yielded significantly lower noise (P < 0.01), higher SNR and CNR values (P < 0.01), and a greater subjective image quality score (P < 0.01). The total number of coronary segments visualized was significantly higher for both iDose{sup 4} and IMR than for FBP (P = 0.002 and P = 0.025, respectively), but there was no significant difference in this parameter between iDose{sup 4} and IMR (P = 0.397). There was no significant difference in the diagnostic accuracy between the FBP, iDose{sup 4} and IMR algorithms (χ{sup 2} = 0.343, P = 0.842). Conclusions: For infants with CHD undergoing cardiac CTA, the IMR reconstruction algorithm provided significantly increased objective and subjective image quality compared with the FBP and iDose{sup 4} algorithms. However, IMR did not improve the diagnostic accuracy or coronary artery visualization compared with iDose{sup 4}.

EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression

KAUST Repository

Tian, Tian Siva; Huang, Jianhua Z.; Shen, Haipeng; Li, Zhimin

2013-01-01

In this work, we propose a spatial-temporal two-way regularized regression method for reconstructing neural source signals from EEG/MEG time course measurements. The proposed method estimates the dipole locations and amplitudes simultaneously

Image super-resolution reconstruction based on regularization technique and guided filter

Science.gov (United States)

Huang, De-tian; Huang, Wei-qin; Gu, Pei-ting; Liu, Pei-zhong; Luo, Yan-min

2017-06-01

In order to improve the accuracy of sparse representation coefficients and the quality of reconstructed images, an improved image super-resolution algorithm based on sparse representation is presented. In the sparse coding stage, the autoregressive (AR) regularization and the non-local (NL) similarity regularization are introduced to improve the sparse coding objective function. A group of AR models which describe the image local structures are pre-learned from the training samples, and one or several suitable AR models can be adaptively selected for each image patch to regularize the solution space. Then, the image non-local redundancy is obtained by the NL similarity regularization to preserve edges. In the process of computing the sparse representation coefficients, the feature-sign search algorithm is utilized instead of the conventional orthogonal matching pursuit algorithm to improve the accuracy of the sparse coefficients. To restore image details further, a global error compensation model based on weighted guided filter is proposed to realize error compensation for the reconstructed images. Experimental results demonstrate that compared with Bicubic, L1SR, SISR, GR, ANR, NE + LS, NE + NNLS, NE + LLE and A + (16 atoms) methods, the proposed approach has remarkable improvement in peak signal-to-noise ratio, structural similarity and subjective visual perception.

Sparse reconstruction by means of the standard Tikhonov regularization

International Nuclear Information System (INIS)

Lu Shuai; Pereverzev, Sergei V

2008-01-01

It is a common belief that Tikhonov scheme with || · ||L 2 -penalty fails in sparse reconstruction. We are going to show, however, that this standard regularization can help if the stability measured in L 1 -norm will be properly taken into account in the choice of the regularization parameter. The crucial point is that now a stability bound may depend on the bases with respect to which the solution of the problem is assumed to be sparse. We discuss how such a stability can be estimated numerically and present the results of computational experiments giving the evidence of the reliability of our approach.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

Science.gov (United States)

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.

WE-AB-207A-08: BEST IN PHYSICS (IMAGING): Advanced Scatter Correction and Iterative Reconstruction for Improved Cone-Beam CT Imaging On the TrueBeam Radiotherapy Machine

Energy Technology Data Exchange (ETDEWEB)

Wang, A; Paysan, P; Brehm, M; Maslowski, A; Lehmann, M; Messmer, P; Munro, P; Yoon, S; Star-Lack, J; Seghers, D [Varian Medical Systems, Palo Alto, CA (United States)

2016-06-15

Purpose: To improve CBCT image quality for image-guided radiotherapy by applying advanced reconstruction algorithms to overcome scatter, noise, and artifact limitations Methods: CBCT is used extensively for patient setup in radiotherapy. However, image quality generally falls short of diagnostic CT, limiting soft-tissue based positioning and potential applications such as adaptive radiotherapy. The conventional TrueBeam CBCT reconstructor uses a basic scatter correction and FDK reconstruction, resulting in residual scatter artifacts, suboptimal image noise characteristics, and other artifacts like cone-beam artifacts. We have developed an advanced scatter correction that uses a finite-element solver (AcurosCTS) to model the behavior of photons as they pass (and scatter) through the object. Furthermore, iterative reconstruction is applied to the scatter-corrected projections, enforcing data consistency with statistical weighting and applying an edge-preserving image regularizer to reduce image noise. The combined algorithms have been implemented on a GPU. CBCT projections from clinically operating TrueBeam systems have been used to compare image quality between the conventional and improved reconstruction methods. Planning CT images of the same patients have also been compared. Results: The advanced scatter correction removes shading and inhomogeneity artifacts, reducing the scatter artifact from 99.5 HU to 13.7 HU in a typical pelvis case. Iterative reconstruction provides further benefit by reducing image noise and eliminating streak artifacts, thereby improving soft-tissue visualization. In a clinical head and pelvis CBCT, the noise was reduced by 43% and 48%, respectively, with no change in spatial resolution (assessed visually). Additional benefits include reduction of cone-beam artifacts and reduction of metal artifacts due to intrinsic downweighting of corrupted rays. Conclusion: The combination of an advanced scatter correction with iterative reconstruction

An Iterative Regularization Method for Identifying the Source Term in a Second Order Differential Equation

Directory of Open Access Journals (Sweden)

Fairouz Zouyed

2015-01-01

Full Text Available This paper discusses the inverse problem of determining an unknown source in a second order differential equation from measured final data. This problem is ill-posed; that is, the solution (if it exists does not depend continuously on the data. In order to solve the considered problem, an iterative method is proposed. Using this method a regularized solution is constructed and an a priori error estimate between the exact solution and its regularized approximation is obtained. Moreover, numerical results are presented to illustrate the accuracy and efficiency of this method.

An investigation of temporal regularization techniques for dynamic PET reconstructions using temporal splines

International Nuclear Information System (INIS)

Verhaeghe, Jeroen; D'Asseler, Yves; Vandenberghe, Stefaan; Staelens, Steven; Lemahieu, Ignace

2007-01-01

The use of a temporal B-spline basis for the reconstruction of dynamic positron emission tomography data was investigated. Maximum likelihood (ML) reconstructions using an expectation maximization framework and maximum A-posteriori (MAP) reconstructions using the generalized expectation maximization framework were evaluated. Different parameters of the B-spline basis of such as order, number of basis functions and knot placing were investigated in a reconstruction task using simulated dynamic list-mode data. We found that a higher order basis reduced both the bias and variance. Using a higher number of basis functions in the modeling of the time activity curves (TACs) allowed the algorithm to model faster changes of the TACs, however, the TACs became noisier. We have compared ML, Gaussian postsmoothed ML and MAP reconstructions. The noise level in the ML reconstructions was controlled by varying the number of basis functions. The MAP algorithm penalized the integrated squared curvature of the reconstructed TAC. The postsmoothed ML was always outperformed in terms of bias and variance properties by the MAP and ML reconstructions. A simple adaptive knot placing strategy was also developed and evaluated. It is based on an arc length redistribution scheme during the reconstruction. The free knot reconstruction allowed a more accurate reconstruction while reducing the noise level especially for fast changing TACs such as blood input functions. Limiting the number of temporal basis functions combined with the adaptive knot placing strategy is in this case advantageous for regularization purposes when compared to the other regularization techniques

Information operator approach and iterative regularization methods for atmospheric remote sensing

International Nuclear Information System (INIS)

Doicu, A.; Hilgers, S.; Bargen, A. von; Rozanov, A.; Eichmann, K.-U.; Savigny, C. von; Burrows, J.P.

2007-01-01

In this study, we present the main features of the information operator approach for solving linear inverse problems arising in atmospheric remote sensing. This method is superior to the stochastic version of the Tikhonov regularization (or the optimal estimation method) due to its capability to filter out the noise-dominated components of the solution generated by an inappropriate choice of the regularization parameter. We extend this approach to iterative methods for nonlinear ill-posed problems and derive the truncated versions of the Gauss-Newton and Levenberg-Marquardt methods. Although the paper mostly focuses on discussing the mathematical details of the inverse method, retrieval results have been provided, which exemplify the performances of the methods. These results correspond to the NO 2 retrieval from SCIAMACHY limb scatter measurements and have been obtained by using the retrieval processors developed at the German Aerospace Center Oberpfaffenhofen and Institute of Environmental Physics of the University of Bremen

Quantitative SPECT reconstruction for brain distribution with a non-uniform attenuation using a regularizing method

International Nuclear Information System (INIS)

Soussaline, F.; Bidaut, L.; Raynaud, C.; Le Coq, G.

1983-06-01

An analytical solution to the SPECT reconstruction problem, where the actual attenuation effect can be included, was developped using a regularizing iterative method (RIM). The potential of this approach in quantitative brain studies when using a tracer for cerebrovascular disorders is now under evaluation. Mathematical simulations for a distributed activity in the brain surrounded by the skull and physical phantom studies were performed, using a rotating camera based SPECT system, allowing the calibration of the system and the evaluation of the adapted method to be used. On the simulation studies, the contrast obtained along a profile, was less than 5%, the standard deviation 8% and the quantitative accuracy 13%, for a uniform emission distribution of mean = 100 per pixel and a double attenuation coefficient of μ = 0.115 cm -1 and 0.5 cm -1 . Clinical data obtained after injection of 123 I (AMPI) were reconstructed using the RIM without and with cerebrovascular diseases or lesion defects. Contour finding techniques were used for the delineation of the brain and the skull, and measured attenuation coefficients were assumed within these two regions. Using volumes of interest, selected on homogeneous regions on an hemisphere and reported symetrically, the statistical uncertainty for 300 K events in the tomogram was found to be 12%, the index of symetry was of 4% for normal distribution. These results suggest that quantitative SPECT reconstruction for brain distribution is feasible, and that combined with an adapted tracer and an adequate model physiopathological parameters could be extracted

Iterative model reconstruction reduces calcified plaque volume in coronary CT angiography

Energy Technology Data Exchange (ETDEWEB)

Károlyi, Mihály, E-mail: mihaly.karolyi@cirg.hu [MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st, 1122, Budapest (Hungary); Szilveszter, Bálint, E-mail: szilveszter.balint@gmail.com [MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st, 1122, Budapest (Hungary); Kolossváry, Márton, E-mail: martonandko@gmail.com [MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st, 1122, Budapest (Hungary); Takx, Richard A.P, E-mail: richard.takx@gmail.com [Department of Radiology, University Medical Center Utrecht, 100 Heidelberglaan, 3584, CX Utrecht (Netherlands); Celeng, Csilla, E-mail: celengcsilla@gmail.com [MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st, 1122, Budapest (Hungary); Bartykowszki, Andrea, E-mail: bartyandi@gmail.com [MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st, 1122, Budapest (Hungary); Jermendy, Ádám L., E-mail: adam.jermendy@gmail.com [MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st, 1122, Budapest (Hungary); Panajotu, Alexisz, E-mail: panajotualexisz@gmail.com [MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st, 1122, Budapest (Hungary); Karády, Júlia, E-mail: karadyjulia@gmail.com [MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68. Varosmajor st, 1122, Budapest (Hungary); and others

2017-02-15

Objective: To assess the impact of iterative model reconstruction (IMR) on calcified plaque quantification as compared to filtered back projection reconstruction (FBP) and hybrid iterative reconstruction (HIR) in coronary computed tomography angiography (CTA). Methods: Raw image data of 52 patients who underwent 256-slice CTA were reconstructed with IMR, HIR and FBP. We evaluated qualitative, quantitative image quality parameters and quantified calcified and partially calcified plaque volumes using automated software. Results: Overall qualitative image quality significantly improved with HIR as compared to FBP, and further improved with IMR (p < 0.01 all). Contrast-to-noise ratios were improved with IMR, compared to HIR and FBP (51.0 [43.5–59.9], 20.3 [16.2–25.9] and 14.0 [11.2–17.7], respectively, all p < 0.01) Overall plaque volumes were lowest with IMR and highest with FBP (121.7 [79.3–168.4], 138.7 [90.6–191.7], 147.0 [100.7–183.6]). Similarly, calcified volumes (>130 HU) were decreased with IMR as compared to HIR and FBP (105.9 [62.1–144.6], 110.2 [63.8–166.6], 115.9 [81.7–164.2], respectively, p < 0.05 all). High-attenuation non-calcified volumes (90–129 HU) yielded similar values with FBP and HIR (p = 0.81), however it was lower with IMR (p < 0.05 both). Intermediate- (30–89 HU) and low-attenuation (<30 HU) non-calcified volumes showed no significant difference (p = 0.22 and p = 0.67, respectively). Conclusions: IMR improves image quality of coronary CTA and decreases calcified plaque volumes.

Poisson image reconstruction with Hessian Schatten-norm regularization.

Science.gov (United States)

Lefkimmiatis, Stamatios; Unser, Michael

2013-11-01

Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupted by Poisson noise. In this paper, we propose an efficient framework for Poisson image reconstruction, under a regularization approach, which depends on matrix-valued regularization operators. In particular, the employed regularizers involve the Hessian as the regularization operator and Schatten matrix norms as the potential functions. For the solution of the problem, we propose two optimization algorithms that are specifically tailored to the Poisson nature of the noise. These algorithms are based on an augmented-Lagrangian formulation of the problem and correspond to two variants of the alternating direction method of multipliers. Further, we derive a link that relates the proximal map of an l(p) norm with the proximal map of a Schatten matrix norm of order p. This link plays a key role in the development of one of the proposed algorithms. Finally, we provide experimental results on natural and biological images for the task of Poisson image deblurring and demonstrate the practical relevance and effectiveness of the proposed framework.

Multichannel Signals Reconstruction Based on Tunable Q-Factor Wavelet Transform-Morphological Component Analysis and Sparse Bayesian Iteration for Rotating Machines

Directory of Open Access Journals (Sweden)

Qing Li

2018-04-01

Full Text Available High-speed remote transmission and large-capacity data storage are difficult issues in signals acquisition of rotating machines condition monitoring. To address these concerns, a novel multichannel signals reconstruction approach based on tunable Q-factor wavelet transform-morphological component analysis (TQWT-MCA and sparse Bayesian iteration algorithm combined with step-impulse dictionary is proposed under the frame of compressed sensing (CS. To begin with, to prevent the periodical impulses loss and effectively separate periodical impulses from the external noise and additive interference components, the TQWT-MCA method is introduced to divide the raw vibration signal into low-resonance component (LRC, i.e., periodical impulses and high-resonance component (HRC, thus, the periodical impulses are preserved effectively. Then, according to the amplitude range of generated LRC, the step-impulse dictionary atom is designed to match the physical structure of periodical impulses. Furthermore, the periodical impulses and HRC are reconstructed by the sparse Bayesian iteration combined with step-impulse dictionary, respectively, finally, the final reconstructed raw signals are obtained by adding the LRC and HRC, meanwhile, the fidelity of the final reconstructed signals is tested by the envelop spectrum and error analysis, respectively. In this work, the proposed algorithm is applied to simulated signal and engineering multichannel signals of a gearbox with multiple faults. Experimental results demonstrate that the proposed approach significantly improves the reconstructive accuracy compared with the state-of-the-art methods such as non-convex Lq (q = 0.5 regularization, spatiotemporal sparse Bayesian learning (SSBL and L1-norm, etc. Additionally, the processing time, i.e., speed of storage and transmission has increased dramatically, more importantly, the fault characteristics of the gearbox with multiple faults are detected and saved, i.e., the

Modelling the physics in iterative reconstruction for transmission computed tomography

Science.gov (United States)

Nuyts, Johan; De Man, Bruno; Fessler, Jeffrey A.; Zbijewski, Wojciech; Beekman, Freek J.

2013-01-01

There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of X-ray CT imaging. IR has the ability to significantly reduce patient dose, it provides the flexibility to reconstruct images from arbitrary X-ray system geometries and it allows to include detailed models of photon transport and detection physics, to accurately correct for a wide variety of image degrading effects. This paper reviews discretisation issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. Widespread implementation of IR with highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling. PMID:23739261

Projection matrix acquisition for cone-beam computed tomography iterative reconstruction

Science.gov (United States)

Yang, Fuqiang; Zhang, Dinghua; Huang, Kuidong; Shi, Wenlong; Zhang, Caixin; Gao, Zongzhao

2017-02-01

Projection matrix is an essential and time-consuming part in computed tomography (CT) iterative reconstruction. In this article a novel calculation algorithm of three-dimensional (3D) projection matrix is proposed to quickly acquire the matrix for cone-beam CT (CBCT). The CT data needed to be reconstructed is considered as consisting of the three orthogonal sets of equally spaced and parallel planes, rather than the individual voxels. After getting the intersections the rays with the surfaces of the voxels, the coordinate points and vertex is compared to obtain the index value that the ray traversed. Without considering ray-slope to voxel, it just need comparing the position of two points. Finally, the computer simulation is used to verify the effectiveness of the algorithm.

Regularity dimension of sequences and its application to phylogenetic tree reconstruction

International Nuclear Information System (INIS)

Pham, Tuan D.

2012-01-01

The concept of dimension is a central development of chaos theory for studying nonlinear dynamical systems. Different types of dimensions have been derived to interpret different geometrical or physical observations. Approximate entropy and its modified methods have been introduced for studying regularity and complexity of time-series data in physiology and biology. Here, the concept of power laws and entropy measure are adopted to develop the regularity dimension of sequences to model a mathematical relationship between the frequency with which information about signal regularity changes in various scales. The proposed regularity dimension is applied to reconstruct phylogenetic trees using mitochondrial DNA (mtDNA) sequences for the family Hominidae, which can be validated according to the hypothesized evolutionary relationships between organisms.

Comparison of computational to human observer detection for evaluation of CT low dose iterative reconstruction

Science.gov (United States)

Eck, Brendan; Fahmi, Rachid; Brown, Kevin M.; Raihani, Nilgoun; Wilson, David L.

2014-03-01

Model observers were created and compared to human observers for the detection of low contrast targets in computed tomography (CT) images reconstructed with an advanced, knowledge-based, iterative image reconstruction method for low x-ray dose imaging. A 5-channel Laguerre-Gauss Hotelling Observer (CHO) was used with internal noise added to the decision variable (DV) and/or channel outputs (CO). Models were defined by parameters: (k1) DV-noise with standard deviation (std) proportional to DV std; (k2) DV-noise with constant std; (k3) CO-noise with constant std across channels; and (k4) CO-noise in each channel with std proportional to CO variance. Four-alternative forced choice (4AFC) human observer studies were performed on sub-images extracted from phantom images with and without a "pin" target. Model parameters were estimated using maximum likelihood comparison to human probability correct (PC) data. PC in human and all model observers increased with dose, contrast, and size, and was much higher for advanced iterative reconstruction (IMR) as compared to filtered back projection (FBP). Detection in IMR was better than FPB at 1/3 dose, suggesting significant dose savings. Model(k1,k2,k3,k4) gave the best overall fit to humans across independent variables (dose, size, contrast, and reconstruction) at fixed display window. However Model(k1) performed better when considering model complexity using the Akaike information criterion. Model(k1) fit the extraordinary detectability difference between IMR and FBP, despite the different noise quality. It is anticipated that the model observer will predict results from iterative reconstruction methods having similar noise characteristics, enabling rapid comparison of methods.

Bias in iterative reconstruction of low-statistics PET data: benefits of a resolution model

Energy Technology Data Exchange (ETDEWEB)

Walker, M D; Asselin, M-C; Julyan, P J; Feldmann, M; Matthews, J C [School of Cancer and Enabling Sciences, Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester M20 3LJ (United Kingdom); Talbot, P S [Mental Health and Neurodegeneration Research Group, Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester M20 3LJ (United Kingdom); Jones, T, E-mail: matthew.walker@manchester.ac.uk [Academic Department of Radiation Oncology, Christie Hospital, University of Manchester, Manchester M20 4BX (United Kingdom)

2011-02-21

Iterative image reconstruction methods such as ordered-subset expectation maximization (OSEM) are widely used in PET. Reconstructions via OSEM are however reported to be biased for low-count data. We investigated this and considered the impact for dynamic PET. Patient listmode data were acquired in [{sup 11}C]DASB and [{sup 15}O]H{sub 2}O scans on the HRRT brain PET scanner. These data were subsampled to create many independent, low-count replicates. The data were reconstructed and the images from low-count data were compared to the high-count originals (from the same reconstruction method). This comparison enabled low-statistics bias to be calculated for the given reconstruction, as a function of the noise-equivalent counts (NEC). Two iterative reconstruction methods were tested, one with and one without an image-based resolution model (RM). Significant bias was observed when reconstructing data of low statistical quality, for both subsampled human and simulated data. For human data, this bias was substantially reduced by including a RM. For [{sup 11}C]DASB the low-statistics bias in the caudate head at 1.7 M NEC (approx. 30 s) was -5.5% and -13% with and without RM, respectively. We predicted biases in the binding potential of -4% and -10%. For quantification of cerebral blood flow for the whole-brain grey- or white-matter, using [{sup 15}O]H{sub 2}O and the PET autoradiographic method, a low-statistics bias of <2.5% and <4% was predicted for reconstruction with and without the RM. The use of a resolution model reduces low-statistics bias and can hence be beneficial for quantitative dynamic PET.

Iterative reconstruction of magnetic induction using Lorentz transmission electron tomography

International Nuclear Information System (INIS)

Phatak, C.; Gürsoy, D.

2015-01-01

Intense ongoing research on complex nanomagnetic structures requires a fundamental understanding of the 3D magnetization and the stray fields around the nano-objects. 3D visualization of such fields offers the best way to achieve this. Lorentz transmission electron microscopy provides a suitable combination of high resolution and ability to quantitatively visualize the magnetization vectors using phase retrieval methods. In this paper, we present a formalism to represent the magnetic phase shift of electrons as a Radon transform of the magnetic induction of the sample. Using this formalism, we then present the application of common tomographic methods particularly the iterative methods, to reconstruct the 3D components of the vector field. We present an analysis of the effect of missing wedge and the limited angular sampling as well as reconstruction of complex 3D magnetization in a nanowire using simulations. - Highlights: • We present a formalism to represent electron-optical magnetic phase shift as a Radon transform of the 3D magnetic induction of the nano-object. • We have analyzed four different tomographic reconstruction methods for vectorial data reconstruction. • Reconstruction methods were tested for varying experimental limitations such as limited tilt range and limited angular sampling. • The analysis showed that Gridrec and SIRT methods performed better with lower errors than other reconstruction methods

Experimental reconstruction of a highly reflecting fiber Bragg grating by using spectral regularization and inverse scattering.

Science.gov (United States)

Rosenthal, Amir; Horowitz, Moshe; Kieckbusch, Sven; Brinkmeyer, Ernst

2007-10-01

We demonstrate experimentally, for the first time to our knowledge, a reconstruction of a highly reflecting fiber Bragg grating from its complex reflection spectrum by using a regularization algorithm. The regularization method is based on correcting the measured reflection spectrum at the Bragg zone frequencies and enables the reconstruction of the grating profile using the integral-layer-peeling algorithm. A grating with an approximately uniform profile and with a maximum reflectivity of 99.98% was accurately reconstructed by measuring only its complex reflection spectrum.

A framelet-based iterative maximum-likelihood reconstruction algorithm for spectral CT

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Wang, Yingmei; Wang, Ge; Mao, Shuwei; Cong, Wenxiang; Ji, Zhilong; Cai, Jian-Feng; Ye, Yangbo

2016-11-01

Standard computed tomography (CT) cannot reproduce spectral information of an object. Hardware solutions include dual-energy CT which scans the object twice in different x-ray energy levels, and energy-discriminative detectors which can separate lower and higher energy levels from a single x-ray scan. In this paper, we propose a software solution and give an iterative algorithm that reconstructs an image with spectral information from just one scan with a standard energy-integrating detector. The spectral information obtained can be used to produce color CT images, spectral curves of the attenuation coefficient μ (r,E) at points inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. Our software solution requires no change on hardware of a CT machine. With the Shepp-Logan phantom, we have found that although the photoelectric and Compton components were not perfectly reconstructed, their composite effect was very accurately reconstructed as compared to the ground truth and the dual-energy CT counterpart. This means that our proposed method has an intrinsic benefit in beam hardening correction and metal artifact reduction. The algorithm is based on a nonlinear polychromatic acquisition model for x-ray CT. The key technique is a sparse representation of iterations in a framelet system. Convergence of the algorithm is studied. This is believed to be the first application of framelet imaging tools to a nonlinear inverse problem.

Iterative reconstruction with attenuation compensation from cone-beam projections acquired via nonplanar orbits

International Nuclear Information System (INIS)

Zeng, G.L.; Weng, Y.; Gullberg, G.T.

1997-01-01

Single photon emission computed tomography (SPECT) imaging with cone-beam collimators provides improved sensitivity and spatial resolution for imaging small objects with large field-of-view detectors. It is known that Tuy's cone-beam data sufficiency condition must be met to obtain artifact-free reconstructions. Even though Tuy's condition was derived for an attenuation-free situation, the authors hypothesize that an artifact-free reconstruction can be obtained even if the cone-beam data are attenuated, provided the imaging orbit satisfies Tuy's condition and the exact attenuation map is known. In the authors' studies, emission data are acquired using nonplanar circle-and-line orbits to acquire cone-beam data for tomographic reconstructions. An extended iterative ML-EM (maximum likelihood-expectation maximization) reconstruction algorithm is derived and used to reconstruct projection data with either a pre-acquired or assumed attenuation map. Quantitative accuracy of the attenuation corrected emission reconstruction is significantly improved

Mean-variance analysis of block-iterative reconstruction algorithms modeling 3D detector response in SPECT

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Lalush, D. S.; Tsui, B. M. W.

1998-06-01

We study the statistical convergence properties of two fast iterative reconstruction algorithms, the rescaled block-iterative (RBI) and ordered subset (OS) EM algorithms, in the context of cardiac SPECT with 3D detector response modeling. The Monte Carlo method was used to generate nearly noise-free projection data modeling the effects of attenuation, detector response, and scatter from the MCAT phantom. One thousand noise realizations were generated with an average count level approximating a typical T1-201 cardiac study. Each noise realization was reconstructed using the RBI and OS algorithms for cases with and without detector response modeling. For each iteration up to twenty, we generated mean and variance images, as well as covariance images for six specific locations. Both OS and RBI converged in the mean to results that were close to the noise-free ML-EM result using the same projection model. When detector response was not modeled in the reconstruction, RBI exhibited considerably lower noise variance than OS for the same resolution. When 3D detector response was modeled, the RBI-EM provided a small improvement in the tradeoff between noise level and resolution recovery, primarily in the axial direction, while OS required about half the number of iterations of RBI to reach the same resolution. We conclude that OS is faster than RBI, but may be sensitive to errors in the projection model. Both OS-EM and RBI-EM are effective alternatives to the EVIL-EM algorithm, but noise level and speed of convergence depend on the projection model used.

Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in quantifying coronary calcium.

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Takahashi, Masahiro; Kimura, Fumiko; Umezawa, Tatsuya; Watanabe, Yusuke; Ogawa, Harumi

2016-01-01

Adaptive statistical iterative reconstruction (ASIR) has been used to reduce radiation dose in cardiac computed tomography. However, change of image parameters by ASIR as compared to filtered back projection (FBP) may influence quantification of coronary calcium. To investigate the influence of ASIR on calcium quantification in comparison to FBP. In 352 patients, CT images were reconstructed using FBP alone, FBP combined with ASIR 30%, 50%, 70%, and ASIR 100% based on the same raw data. Image noise, plaque density, Agatston scores and calcium volumes were compared among the techniques. Image noise, Agatston score, and calcium volume decreased significantly with ASIR compared to FBP (each P ASIR reduced Agatston score by 10.5% to 31.0%. In calcified plaques both of patients and a phantom, ASIR decreased maximum CT values and calcified plaque size. In comparison to FBP, adaptive statistical iterative reconstruction (ASIR) may significantly decrease Agatston scores and calcium volumes. Copyright © 2016 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2007-01-01

This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted...... to observations that show little change, i.e., for which the sum of squared, standardized MAD variates is small, and small weights are assigned to observations for which the sum is large. Like the original MAD method, the iterative extension is invariant to linear (affine) transformations of the original...... an agricultural region in Kenya, and hyperspectral airborne HyMap data from a small rural area in southeastern Germany are given. The latter case demonstrates the need for regularization....

[Impact to Z-score Mapping of Hyperacute Stroke Images by Computed Tomography in Adaptive Statistical Iterative Reconstruction].

Science.gov (United States)

Watanabe, Shota; Sakaguchi, Kenta; Hosono, Makoto; Ishii, Kazunari; Murakami, Takamichi; Ichikawa, Katsuhiro

The purpose of this study was to evaluate the effect of a hybrid-type iterative reconstruction method on Z-score mapping of hyperacute stroke in unenhanced computed tomography (CT) images. We used a hybrid-type iterative reconstruction [adaptive statistical iterative reconstruction (ASiR)] implemented in a CT system (Optima CT660 Pro advance, GE Healthcare). With 15 normal brain cases, we reconstructed CT images with a filtered back projection (FBP) and ASiR with a blending factor of 100% (ASiR100%). Two standardized normal brain data were created from normal databases of FBP images (FBP-NDB) and ASiR100% images (ASiR-NDB), and standard deviation (SD) values in basal ganglia were measured. The Z-score mapping was performed for 12 hyperacute stroke cases by using FBP-NDB and ASiR-NDB, and compared Z-score value on hyperacute stroke area and normal area between FBP-NDB and ASiR-NDB. By using ASiR-NDB, the SD value of standardized brain was decreased by 16%. The Z-score value of ASiR-NDB on hyperacute stroke area was significantly higher than FBP-NDB (pASiR100% for Z-score mapping had potential to improve the accuracy of Z-score mapping.

Full dose reduction potential of statistical iterative reconstruction for head CT protocols in a predominantly pediatric population

Science.gov (United States)

Mirro, Amy E.; Brady, Samuel L.; Kaufman, Robert. A.

2016-01-01

Purpose To implement the maximum level of statistical iterative reconstruction that can be used to establish dose-reduced head CT protocols in a primarily pediatric population. Methods Select head examinations (brain, orbits, sinus, maxilla and temporal bones) were investigated. Dose-reduced head protocols using an adaptive statistical iterative reconstruction (ASiR) were compared for image quality with the original filtered back projection (FBP) reconstructed protocols in phantom using the following metrics: image noise frequency (change in perceived appearance of noise texture), image noise magnitude, contrast-to-noise ratio (CNR), and spatial resolution. Dose reduction estimates were based on computed tomography dose index (CTDIvol) values. Patient CTDIvol and image noise magnitude were assessed in 737 pre and post dose reduced examinations. Results Image noise texture was acceptable up to 60% ASiR for Soft reconstruction kernel (at both 100 and 120 kVp), and up to 40% ASiR for Standard reconstruction kernel. Implementation of 40% and 60% ASiR led to an average reduction in CTDIvol of 43% for brain, 41% for orbits, 30% maxilla, 43% for sinus, and 42% for temporal bone protocols for patients between 1 month and 26 years, while maintaining an average noise magnitude difference of 0.1% (range: −3% to 5%), improving CNR of low contrast soft tissue targets, and improving spatial resolution of high contrast bony anatomy, as compared to FBP. Conclusion The methodology in this study demonstrates a methodology for maximizing patient dose reduction and maintaining image quality using statistical iterative reconstruction for a primarily pediatric population undergoing head CT examination. PMID:27056425

Iterative choice of the optimal regularization parameter in TV image deconvolution

International Nuclear Information System (INIS)

Sixou, B; Toma, A; Peyrin, F; Denis, L

2013-01-01

We present an iterative method for choosing the optimal regularization parameter for the linear inverse problem of Total Variation image deconvolution. This approach is based on the Morozov discrepancy principle and on an exponential model function for the data term. The Total Variation image deconvolution is performed with the Alternating Direction Method of Multipliers (ADMM). With a smoothed l 2 norm, the differentiability of the value of the Lagrangian at the saddle point can be shown and an approximate model function obtained. The choice of the optimal parameter can be refined with a Newton method. The efficiency of the method is demonstrated on a blurred and noisy bone CT cross section

Image Reconstruction Based on Homotopy Perturbation Inversion Method for Electrical Impedance Tomography

Directory of Open Access Journals (Sweden)

Jing Wang

2013-01-01

Full Text Available The image reconstruction for electrical impedance tomography (EIT mathematically is a typed nonlinear ill-posed inverse problem. In this paper, a novel iteration regularization scheme based on the homotopy perturbation technique, namely, homotopy perturbation inversion method, is applied to investigate the EIT image reconstruction problem. To verify the feasibility and effectiveness, simulations of image reconstruction have been performed in terms of considering different locations, sizes, and numbers of the inclusions, as well as robustness to data noise. Numerical results indicate that this method can overcome the numerical instability and is robust to data noise in the EIT image reconstruction. Moreover, compared with the classical Landweber iteration method, our approach improves the convergence rate. The results are promising.

Improving thoracic four-dimensional cone-beam CT reconstruction with anatomical-adaptive image regularization (AAIR)

International Nuclear Information System (INIS)

Shieh, Chun-Chien; Kipritidis, John; O'Brien, Ricky T; Cooper, Benjamin J; Keall, Paul J; Kuncic, Zdenka

2015-01-01

Total-variation (TV) minimization reconstructions can significantly reduce noise and streaks in thoracic four-dimensional cone-beam computed tomography (4D CBCT) images compared to the Feldkamp–Davis–Kress (FDK) algorithm currently used in practice. TV minimization reconstructions are, however, prone to over-smoothing anatomical details and are also computationally inefficient. The aim of this study is to demonstrate a proof of concept that these disadvantages can be overcome by incorporating the general knowledge of the thoracic anatomy via anatomy segmentation into the reconstruction. The proposed method, referred as the anatomical-adaptive image regularization (AAIR) method, utilizes the adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS) framework, but introduces an additional anatomy segmentation step in every iteration. The anatomy segmentation information is implemented in the reconstruction using a heuristic approach to adaptively suppress over-smoothing at anatomical structures of interest. The performance of AAIR depends on parameters describing the weighting of the anatomy segmentation prior and segmentation threshold values. A sensitivity study revealed that the reconstruction outcome is not sensitive to these parameters as long as they are chosen within a suitable range. AAIR was validated using a digital phantom and a patient scan and was compared to FDK, ASD-POCS and the prior image constrained compressed sensing (PICCS) method. For the phantom case, AAIR reconstruction was quantitatively shown to be the most accurate as indicated by the mean absolute difference and the structural similarity index. For the patient case, AAIR resulted in the highest signal-to-noise ratio (i.e. the lowest level of noise and streaking) and the highest contrast-to-noise ratios for the tumor and the bony anatomy (i.e. the best visibility of anatomical details). Overall, AAIR was much less prone to over-smoothing anatomical details compared to ASD-POCS and

Ultralow-dose CT of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction and model-based iterative reconstruction: 2D and 3D image quality.

Science.gov (United States)

Widmann, Gerlig; Schullian, Peter; Gassner, Eva-Maria; Hoermann, Romed; Bale, Reto; Puelacher, Wolfgang

2015-03-01

OBJECTIVE. The purpose of this article is to evaluate 2D and 3D image quality of high-resolution ultralow-dose CT images of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) in comparison with standard filtered backprojection (FBP). MATERIALS AND METHODS. A formalin-fixed human cadaver head was scanned using a clinical reference protocol at a CT dose index volume of 30.48 mGy and a series of five ultralow-dose protocols at 3.48, 2.19, 0.82, 0.44, and 0.22 mGy using FBP and ASIR at 50% (ASIR-50), ASIR at 100% (ASIR-100), and MBIR. Blinded 2D axial and 3D volume-rendered images were compared with each other by three readers using top-down scoring. Scores were analyzed per protocol or dose and reconstruction. All images were compared with the FBP reference at 30.48 mGy. A nonparametric Mann-Whitney U test was used. Statistical significance was set at p ASIR-100 at 3.48 mGy, ASIR-100 at 2.19 mGy, and MBIR at 0.82 mGy. MBIR at 2.19 and 3.48 mGy scored statistically significantly better than the FBP reference (p = 0.032 and 0.001, respectively). For 3D images, the FBP reference at 30.48 mGy did not statistically significantly differ from all reconstructions at 3.48 mGy; FBP and ASIR-100 at 2.19 mGy; FBP, ASIR-100, and MBIR at 0.82 mGy; MBIR at 0.44 mGy; and MBIR at 0.22 mGy. CONCLUSION. MBIR (2D and 3D) and ASIR-100 (2D) may significantly improve subjective image quality of ultralow-dose images and may allow more than 90% dose reductions.

Initial phantom study comparing image quality in computed tomography using adaptive statistical iterative reconstruction and new adaptive statistical iterative reconstruction v.

Science.gov (United States)

Lim, Kyungjae; Kwon, Heejin; Cho, Jinhan; Oh, Jongyoung; Yoon, Seongkuk; Kang, Myungjin; Ha, Dongho; Lee, Jinhwa; Kang, Eunju

2015-01-01

The purpose of this study was to assess the image quality of a novel advanced iterative reconstruction (IR) method called as "adaptive statistical IR V" (ASIR-V) by comparing the image noise, contrast-to-noise ratio (CNR), and spatial resolution from those of filtered back projection (FBP) and adaptive statistical IR (ASIR) on computed tomography (CT) phantom image. We performed CT scans at 5 different tube currents (50, 70, 100, 150, and 200 mA) using 3 types of CT phantoms. Scanned images were subsequently reconstructed in 7 different scan settings, such as FBP, and 3 levels of ASIR and ASIR-V (30%, 50%, and 70%). The image noise was measured in the first study using body phantom. The CNR was measured in the second study using contrast phantom and the spatial resolutions were measured in the third study using a high-resolution phantom. We compared the image noise, CNR, and spatial resolution among the 7 reconstructed image scan settings to determine whether noise reduction, high CNR, and high spatial resolution could be achieved at ASIR-V. At quantitative analysis of the first and second studies, it showed that the images reconstructed using ASIR-V had reduced image noise and improved CNR compared with those of FBP and ASIR (P ASIR-V had significantly improved spatial resolution than those of FBP and ASIR (P ASIR-V provides a significant reduction in image noise and a significant improvement in CNR as well as spatial resolution. Therefore, this technique has the potential to reduce the radiation dose further without compromising image quality.

The effect of iterative reconstruction on computed tomography assessment of emphysema, air trapping and airway dimensions

International Nuclear Information System (INIS)

Mets, Onno M.; Willemink, Martin J.; Kort, Freek P.L. de; Leiner, Tim; Jong, Pim A. de; Mol, Christian P.; Oudkerk, Matthijs; Prokop, Mathias

2012-01-01

To determine the influence of iterative reconstruction (IR) on quantitative computed tomography (CT) measurements of emphysema, air trapping, and airway wall and lumen dimensions, compared to filtered back-projection (FBP). Inspiratory and expiratory chest CTs of 75 patients (37 male, 38 female; mean age 64.0 ± 5.7 years) were reconstructed using FBP and IR. CT emphysema, CT air trapping and airway dimensions of a segmental bronchus were quantified using several commonly used quantification methods. The two algorithms were compared using the concordance correlation coefficient (p c ) and Wilcoxon signed rank test. Only the E/I-ratio MLD as a measure of CT air trapping and airway dimensions showed no significant differences between the algorithms, whereas all CT emphysema and the other CT air trapping measures were significantly different at IR when compared to FBP (P MLD as a measure of CT air trapping, as well as the airway measurements, is unaffected by this reconstruction method. Quantitative CT of the lungs should be performed with careful attention to the CT protocol, especially when iterative reconstruction is introduced. (orig.)

The effects of iterative reconstruction in CT on low-contrast liver lesion volumetry: a phantom study

Science.gov (United States)

Li, Qin; Berman, Benjamin P.; Schumacher, Justin; Liang, Yongguang; Gavrielides, Marios A.; Yang, Hao; Zhao, Binsheng; Petrick, Nicholas

2017-03-01

Tumor volume measured from computed tomography images is considered a biomarker for disease progression or treatment response. The estimation of the tumor volume depends on the imaging system parameters selected, as well as lesion characteristics. In this study, we examined how different image reconstruction methods affect the measurement of lesions in an anthropomorphic liver phantom with a non-uniform background. Iterative statistics-based and model-based reconstructions, as well as filtered back-projection, were evaluated and compared in this study. Statistics-based and filtered back-projection yielded similar estimation performance, while model-based yielded higher precision but lower accuracy in the case of small lesions. Iterative reconstructions exhibited higher signal-to-noise ratio but slightly lower contrast of the lesion relative to the background. A better understanding of lesion volumetry performance as a function of acquisition parameters and lesion characteristics can lead to its incorporation as a routine sizing tool.

Three-dimensional focus of attention for iterative cone-beam micro-CT reconstruction

International Nuclear Information System (INIS)

Benson, T M; Gregor, J

2006-01-01

Three-dimensional iterative reconstruction of high-resolution, circular orbit cone-beam x-ray CT data is often considered impractical due to the demand for vast amounts of computer cycles and associated memory. In this paper, we show that the computational burden can be reduced by limiting the reconstruction to a small, well-defined portion of the image volume. We first discuss using the support region defined by the set of voxels covered by all of the projection views. We then present a data-driven preprocessing technique called focus of attention that heuristically separates both image and projection data into object and background before reconstruction, thereby further reducing the reconstruction region of interest. We present experimental results for both methods based on mouse data and a parallelized implementation of the SIRT algorithm. The computational savings associated with the support region are substantial. However, the results for focus of attention are even more impressive in that only about one quarter of the computer cycles and memory are needed compared with reconstruction of the entire image volume. The image quality is not compromised by either method

SU-F-I-49: Vendor-Independent, Model-Based Iterative Reconstruction On a Rotating Grid with Coordinate-Descent Optimization for CT Imaging Investigations

International Nuclear Information System (INIS)

Young, S; Hoffman, J; McNitt-Gray, M; Noo, F

2016-01-01

Purpose: Iterative reconstruction methods show promise for improving image quality and lowering the dose in helical CT. We aim to develop a novel model-based reconstruction method that offers potential for dose reduction with reasonable computation speed and storage requirements for vendor-independent reconstruction from clinical data on a normal desktop computer. Methods: In 2012, Xu proposed reconstructing on rotating slices to exploit helical symmetry and reduce the storage requirements for the CT system matrix. Inspired by this concept, we have developed a novel reconstruction method incorporating the stored-system-matrix approach together with iterative coordinate-descent (ICD) optimization. A penalized-least-squares objective function with a quadratic penalty term is solved analytically voxel-by-voxel, sequentially iterating along the axial direction first, followed by the transaxial direction. 8 in-plane (transaxial) neighbors are used for the ICD algorithm. The forward problem is modeled via a unique approach that combines the principle of Joseph’s method with trilinear B-spline interpolation to enable accurate reconstruction with low storage requirements. Iterations are accelerated with multi-CPU OpenMP libraries. For preliminary evaluations, we reconstructed (1) a simulated 3D ellipse phantom and (2) an ACR accreditation phantom dataset exported from a clinical scanner (Definition AS, Siemens Healthcare). Image quality was evaluated in the resolution module. Results: Image quality was excellent for the ellipse phantom. For the ACR phantom, image quality was comparable to clinical reconstructions and reconstructions using open-source FreeCT-wFBP software. Also, we did not observe any deleterious impact associated with the utilization of rotating slices. The system matrix storage requirement was only 4.5GB, and reconstruction time was 50 seconds per iteration. Conclusion: Our reconstruction method shows potential for furthering research in low

SU-F-I-49: Vendor-Independent, Model-Based Iterative Reconstruction On a Rotating Grid with Coordinate-Descent Optimization for CT Imaging Investigations

Energy Technology Data Exchange (ETDEWEB)

Young, S; Hoffman, J; McNitt-Gray, M [UCLA School of Medicine, Los Angeles, CA (United States); Noo, F [University of Utah, Salt Lake City, UT (United States)

2016-06-15

Purpose: Iterative reconstruction methods show promise for improving image quality and lowering the dose in helical CT. We aim to develop a novel model-based reconstruction method that offers potential for dose reduction with reasonable computation speed and storage requirements for vendor-independent reconstruction from clinical data on a normal desktop computer. Methods: In 2012, Xu proposed reconstructing on rotating slices to exploit helical symmetry and reduce the storage requirements for the CT system matrix. Inspired by this concept, we have developed a novel reconstruction method incorporating the stored-system-matrix approach together with iterative coordinate-descent (ICD) optimization. A penalized-least-squares objective function with a quadratic penalty term is solved analytically voxel-by-voxel, sequentially iterating along the axial direction first, followed by the transaxial direction. 8 in-plane (transaxial) neighbors are used for the ICD algorithm. The forward problem is modeled via a unique approach that combines the principle of Joseph’s method with trilinear B-spline interpolation to enable accurate reconstruction with low storage requirements. Iterations are accelerated with multi-CPU OpenMP libraries. For preliminary evaluations, we reconstructed (1) a simulated 3D ellipse phantom and (2) an ACR accreditation phantom dataset exported from a clinical scanner (Definition AS, Siemens Healthcare). Image quality was evaluated in the resolution module. Results: Image quality was excellent for the ellipse phantom. For the ACR phantom, image quality was comparable to clinical reconstructions and reconstructions using open-source FreeCT-wFBP software. Also, we did not observe any deleterious impact associated with the utilization of rotating slices. The system matrix storage requirement was only 4.5GB, and reconstruction time was 50 seconds per iteration. Conclusion: Our reconstruction method shows potential for furthering research in low

A Regularized Approach for Solving Magnetic Differential Equations and a Revised Iterative Equilibrium Algorithm

International Nuclear Information System (INIS)

Hudson, S.R.

2010-01-01

A method for approximately solving magnetic differential equations is described. The approach is to include a small diffusion term to the equation, which regularizes the linear operator to be inverted. The extra term allows a 'source-correction' term to be defined, which is generally required in order to satisfy the solvability conditions. The approach is described in the context of computing the pressure and parallel currents in the iterative approach for computing magnetohydrodynamic equilibria.

Rapid alignment of nanotomography data using joint iterative reconstruction and reprojection.

Science.gov (United States)

Gürsoy, Doğa; Hong, Young P; He, Kuan; Hujsak, Karl; Yoo, Seunghwan; Chen, Si; Li, Yue; Ge, Mingyuan; Miller, Lisa M; Chu, Yong S; De Andrade, Vincent; He, Kai; Cossairt, Oliver; Katsaggelos, Aggelos K; Jacobsen, Chris

2017-09-18

As x-ray and electron tomography is pushed further into the nanoscale, the limitations of rotation stages become more apparent, leading to challenges in the alignment of the acquired projection images. Here we present an approach for rapid post-acquisition alignment of these projections to obtain high quality three-dimensional images. Our approach is based on a joint estimation of alignment errors, and the object, using an iterative refinement procedure. With simulated data where we know the alignment error of each projection image, our approach shows a residual alignment error that is a factor of a thousand smaller, and it reaches the same error level in the reconstructed image in less than half the number of iterations. We then show its application to experimental data in x-ray and electron nanotomography.

Investigation of vessel visibility of iterative reconstruction method in coronary computed tomography angiography using simulated vessel phantom

International Nuclear Information System (INIS)

Inoue, Takeshi; Uto, Fumiaki; Ichikawa, Katsuhiro; Hara, Takanori; Urikura, Atsushi; Hoshino, Takashi; Miura, Youhei; Terakawa, Syouichi

2012-01-01

Iterative reconstruction methods can reduce the noise of computed tomography (CT) images, which are expected to contribute to the reduction of patient dose CT examinations. The purpose of this study was to investigate impact of an iterative reconstruction method (iDose 4 , Philips Healthcare) on vessel visibility in coronary CT angiography (CTA) by using phantom studies. A simulated phantom was scanned by a CT system (iCT, Philips Healthcare), and the axial images were reconstructed by filtered back projection (FBP) and given a level of 1 to 7 (L1-L7) of the iterative reconstruction (IR). The vessel visibility was evaluated by a quantitative analysis using profiles across a 1.5-mm diameter simulated vessel as well as visual evaluation for multi planar reformation (MPR) images and volume rendering (VR) images in terms of the normalized-rank method with analysis of variance. The peak CT value of the profiles decreased with IR level and full width at half maximum of the profile also decreased with the IR level. For normalized-rank method, there was no statistical difference between FBP and L1 (20% dose reduction) for both MPR and VR images. The IR levels higher than L1 sacrificed the spatial resolution for the 1.5-mm simulated vessel, and their visual vessel visibilities were significantly inferior to that of the FBP. (author)

Radiation dose reduction in soft tissue neck CT using adaptive statistical iterative reconstruction (ASIR)

Energy Technology Data Exchange (ETDEWEB)

Vachha, Behroze, E-mail: bvachha@partners.org [Neuroradiology Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114 (United States); Brodoefel, Harald; Wilcox, Carol; Hackney, David B.; Moonis, Gul [Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215 (United States)

2013-12-01

Purpose: To compare objective and subjective image quality in neck CT images acquired at different tube current–time products (275 mA s and 340 mA s) and reconstructed with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction (ASIR). Materials and methods: HIPAA-compliant study with IRB approval and waiver of informed consent. 66 consecutive patients were randomly assigned to undergo contrast-enhanced neck CT at a standard tube-current–time-product (340 mA s; n = 33) or reduced tube-current–time-product (275 mA s, n = 33). Data sets were reconstructed with FBP and 2 levels (30%, 40%) of ASIR-FBP blending at 340 mA s and 275 mA s. Two neuroradiologists assessed subjective image quality in a blinded and randomized manner. Volume CT dose index (CTDIvol), dose-length-product (DLP), effective dose, and objective image noise were recorded. Signal-to-noise ratio (SNR) was computed as mean attenuation in a region of interest in the sternocleidomastoid muscle divided by image noise. Results: Compared with FBP, ASIR resulted in a reduction of image noise at both 340 mA s and 275 mA s. Reduction of tube current from 340 mA s to 275 mA s resulted in an increase in mean objective image noise (p = 0.02) and a decrease in SNR (p = 0.03) when images were reconstructed with FBP. However, when the 275 mA s images were reconstructed using ASIR, the mean objective image noise and SNR were similar to those of the standard 340 mA s CT images reconstructed with FBP (p > 0.05). Subjective image noise was ranked by both raters as either average or less-than-average irrespective of the tube current and iterative reconstruction technique. Conclusion: Adapting ASIR into neck CT protocols reduced effective dose by 17% without compromising image quality.

Radiation dose reduction in soft tissue neck CT using adaptive statistical iterative reconstruction (ASIR)

International Nuclear Information System (INIS)

Vachha, Behroze; Brodoefel, Harald; Wilcox, Carol; Hackney, David B.; Moonis, Gul

2013-01-01

Purpose: To compare objective and subjective image quality in neck CT images acquired at different tube current–time products (275 mA s and 340 mA s) and reconstructed with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction (ASIR). Materials and methods: HIPAA-compliant study with IRB approval and waiver of informed consent. 66 consecutive patients were randomly assigned to undergo contrast-enhanced neck CT at a standard tube-current–time-product (340 mA s; n = 33) or reduced tube-current–time-product (275 mA s, n = 33). Data sets were reconstructed with FBP and 2 levels (30%, 40%) of ASIR-FBP blending at 340 mA s and 275 mA s. Two neuroradiologists assessed subjective image quality in a blinded and randomized manner. Volume CT dose index (CTDIvol), dose-length-product (DLP), effective dose, and objective image noise were recorded. Signal-to-noise ratio (SNR) was computed as mean attenuation in a region of interest in the sternocleidomastoid muscle divided by image noise. Results: Compared with FBP, ASIR resulted in a reduction of image noise at both 340 mA s and 275 mA s. Reduction of tube current from 340 mA s to 275 mA s resulted in an increase in mean objective image noise (p = 0.02) and a decrease in SNR (p = 0.03) when images were reconstructed with FBP. However, when the 275 mA s images were reconstructed using ASIR, the mean objective image noise and SNR were similar to those of the standard 340 mA s CT images reconstructed with FBP (p > 0.05). Subjective image noise was ranked by both raters as either average or less-than-average irrespective of the tube current and iterative reconstruction technique. Conclusion: Adapting ASIR into neck CT protocols reduced effective dose by 17% without compromising image quality

Radiation dose reduction in soft tissue neck CT using adaptive statistical iterative reconstruction (ASIR).

Science.gov (United States)

Vachha, Behroze; Brodoefel, Harald; Wilcox, Carol; Hackney, David B; Moonis, Gul

2013-12-01

To compare objective and subjective image quality in neck CT images acquired at different tube current-time products (275 mAs and 340 mAs) and reconstructed with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction (ASIR). HIPAA-compliant study with IRB approval and waiver of informed consent. 66 consecutive patients were randomly assigned to undergo contrast-enhanced neck CT at a standard tube-current-time-product (340 mAs; n = 33) or reduced tube-current-time-product (275 mAs, n = 33). Data sets were reconstructed with FBP and 2 levels (30%, 40%) of ASIR-FBP blending at 340 mAs and 275 mAs. Two neuroradiologists assessed subjective image quality in a blinded and randomized manner. Volume CT dose index (CTDIvol), dose-length-product (DLP), effective dose, and objective image noise were recorded. Signal-to-noise ratio (SNR) was computed as mean attenuation in a region of interest in the sternocleidomastoid muscle divided by image noise. Compared with FBP, ASIR resulted in a reduction of image noise at both 340 mAs and 275 mAs. Reduction of tube current from 340 mAs to 275 mAs resulted in an increase in mean objective image noise (p=0.02) and a decrease in SNR (p = 0.03) when images were reconstructed with FBP. However, when the 275 mAs images were reconstructed using ASIR, the mean objective image noise and SNR were similar to those of the standard 340 mAs CT images reconstructed with FBP (p>0.05). Subjective image noise was ranked by both raters as either average or less-than-average irrespective of the tube current and iterative reconstruction technique. Adapting ASIR into neck CT protocols reduced effective dose by 17% without compromising image quality. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Optimization of image quality and acquisition time for lab-based X-ray microtomography using an iterative reconstruction algorithm

Science.gov (United States)

Lin, Qingyang; Andrew, Matthew; Thompson, William; Blunt, Martin J.; Bijeljic, Branko

2018-05-01

Non-invasive laboratory-based X-ray microtomography has been widely applied in many industrial and research disciplines. However, the main barrier to the use of laboratory systems compared to a synchrotron beamline is its much longer image acquisition time (hours per scan compared to seconds to minutes at a synchrotron), which results in limited application for dynamic in situ processes. Therefore, the majority of existing laboratory X-ray microtomography is limited to static imaging; relatively fast imaging (tens of minutes per scan) can only be achieved by sacrificing imaging quality, e.g. reducing exposure time or number of projections. To alleviate this barrier, we introduce an optimized implementation of a well-known iterative reconstruction algorithm that allows users to reconstruct tomographic images with reasonable image quality, but requires lower X-ray signal counts and fewer projections than conventional methods. Quantitative analysis and comparison between the iterative and the conventional filtered back-projection reconstruction algorithm was performed using a sandstone rock sample with and without liquid phases in the pore space. Overall, by implementing the iterative reconstruction algorithm, the required image acquisition time for samples such as this, with sparse object structure, can be reduced by a factor of up to 4 without measurable loss of sharpness or signal to noise ratio.

Optimization of PET image quality by means of 3D data acquisition and iterative image reconstruction

International Nuclear Information System (INIS)

Doll, J.; Zaers, J.; Trojan, H.; Bellemann, M.E.; Adam, L.E.; Haberkorn, U.; Brix, G.

1998-01-01

The experiments were performed at the latest-generation whole-body PET system ECAT EXACT HR + . For 2D data acquisition, a collimator of thin tungsten septa was positioned in the field-of-view. Prior to image reconstruction, the measured 3D data were sorted into 2D sinograms by using the Fourier rebinning (FORE) algorithm developed by M. Defrise. The standard filtered backprojection (FBP) method and an optimized ML/EM algorithm with overrelaxation for accelerated convergence were employed for image reconstruction. The spatial resolution of both methods as well as the convergence and noise properties of the ML/EM algorithm were studied in phantom measurements. Furthermore, patient data were acquired in the 2D mode as well as in the 3D mode and reconstructed with both techniques. At the same spatial resolution, the ML/EM-reconstructed images showed fewer and less prominent artefacts than the FBP-reconstructed images. The resulting improved detail conspicuously was achieved for the data acquired in the 2D mode as well as in the 3D mode. The best image quality was obtained by iterative 2D reconstruction of 3D data sets which were previously rebinned into 2D sinograms with help of the FORE algorithm. The phantom measurements revealed that 50 iteration steps with the otpimized ML/EM algorithm were sufficient to keep the relative quantitation error below 5%. (orig./MG) [de

SU-D-206-01: Employing a Novel Consensus Optimization Strategy to Achieve Iterative Cone Beam CT Reconstruction On a Multi-GPU Platform

International Nuclear Information System (INIS)

Li, B; Tian, Z; Jiang, S; Jia, X; Zhou, L

2016-01-01

Purpose: While compressed sensing-based cone-beam CT (CBCT) iterative reconstruction techniques have demonstrated tremendous capability of reconstructing high-quality images from undersampled noisy data, its long computation time still hinders wide application in routine clinic. The purpose of this study is to develop a reconstruction framework that employs modern consensus optimization techniques to achieve CBCT reconstruction on a multi-GPU platform for improved computational efficiency. Methods: Total projection data were evenly distributed to multiple GPUs. Each GPU performed reconstruction using its own projection data with a conventional total variation regularization approach to ensure image quality. In addition, the solutions from GPUs were subject to a consistency constraint that they should be identical. We solved the optimization problem with all the constraints considered rigorously using an alternating direction method of multipliers (ADMM) algorithm. The reconstruction framework was implemented using OpenCL on a platform with two Nvidia GTX590 GPU cards, each with two GPUs. We studied the performance of our method and demonstrated its advantages through a simulation case with a NCAT phantom and an experimental case with a Catphan phantom. Result: Compared with the CBCT images reconstructed using conventional FDK method with full projection datasets, our proposed method achieved comparable image quality with about one third projection numbers. The computation time on the multi-GPU platform was ∼55 s and ∼ 35 s in the two cases respectively, achieving a speedup factor of ∼ 3.0 compared with single GPU reconstruction. Conclusion: We have developed a consensus ADMM-based CBCT reconstruction method which enabled performing reconstruction on a multi-GPU platform. The achieved efficiency made this method clinically attractive.

SU-D-206-01: Employing a Novel Consensus Optimization Strategy to Achieve Iterative Cone Beam CT Reconstruction On a Multi-GPU Platform

Energy Technology Data Exchange (ETDEWEB)

Li, B [University of Texas Southwestern Medical Center, Dallas, TX (United States); Southern Medical University, Guangzhou, Guangdong (China); Tian, Z; Jiang, S; Jia, X [University of Texas Southwestern Medical Center, Dallas, TX (United States); Zhou, L [Southern Medical University, Guangzhou, Guangdong (China)

2016-06-15

Purpose: While compressed sensing-based cone-beam CT (CBCT) iterative reconstruction techniques have demonstrated tremendous capability of reconstructing high-quality images from undersampled noisy data, its long computation time still hinders wide application in routine clinic. The purpose of this study is to develop a reconstruction framework that employs modern consensus optimization techniques to achieve CBCT reconstruction on a multi-GPU platform for improved computational efficiency. Methods: Total projection data were evenly distributed to multiple GPUs. Each GPU performed reconstruction using its own projection data with a conventional total variation regularization approach to ensure image quality. In addition, the solutions from GPUs were subject to a consistency constraint that they should be identical. We solved the optimization problem with all the constraints considered rigorously using an alternating direction method of multipliers (ADMM) algorithm. The reconstruction framework was implemented using OpenCL on a platform with two Nvidia GTX590 GPU cards, each with two GPUs. We studied the performance of our method and demonstrated its advantages through a simulation case with a NCAT phantom and an experimental case with a Catphan phantom. Result: Compared with the CBCT images reconstructed using conventional FDK method with full projection datasets, our proposed method achieved comparable image quality with about one third projection numbers. The computation time on the multi-GPU platform was ∼55 s and ∼ 35 s in the two cases respectively, achieving a speedup factor of ∼ 3.0 compared with single GPU reconstruction. Conclusion: We have developed a consensus ADMM-based CBCT reconstruction method which enabled performing reconstruction on a multi-GPU platform. The achieved efficiency made this method clinically attractive.

A noise power spectrum study of a new model-based iterative reconstruction system: Veo 3.0.

Science.gov (United States)

Li, Guang; Liu, Xinming; Dodge, Cristina T; Jensen, Corey T; Rong, X John

2016-09-08

The purpose of this study was to evaluate performance of the third generation of model-based iterative reconstruction (MBIR) system, Veo 3.0, based on noise power spectrum (NPS) analysis with various clinical presets over a wide range of clinically applicable dose levels. A CatPhan 600 surrounded by an oval, fat-equivalent ring to mimic patient size/shape was scanned 10 times at each of six dose levels on a GE HD 750 scanner. NPS analysis was performed on images reconstructed with various Veo 3.0 preset combinations for comparisons of those images reconstructed using Veo 2.0, filtered back projection (FBP) and adaptive statistical iterative reconstruc-tion (ASiR). The new Target Thickness setting resulted in higher noise in thicker axial images. The new Texture Enhancement function achieved a more isotropic noise behavior with less image artifacts. Veo 3.0 provides additional reconstruction options designed to allow the user choice of balance between spatial resolution and image noise, relative to Veo 2.0. Veo 3.0 provides more user selectable options and in general improved isotropic noise behavior in comparison to Veo 2.0. The overall noise reduction performance of both versions of MBIR was improved in comparison to FBP and ASiR, especially at low-dose levels. © 2016 The Authors.

Block matching sparsity regularization-based image reconstruction for incomplete projection data in computed tomography

Science.gov (United States)

Cai, Ailong; Li, Lei; Zheng, Zhizhong; Zhang, Hanming; Wang, Linyuan; Hu, Guoen; Yan, Bin

2018-02-01

In medical imaging many conventional regularization methods, such as total variation or total generalized variation, impose strong prior assumptions which can only account for very limited classes of images. A more reasonable sparse representation frame for images is still badly needed. Visually understandable images contain meaningful patterns, and combinations or collections of these patterns can be utilized to form some sparse and redundant representations which promise to facilitate image reconstructions. In this work, we propose and study block matching sparsity regularization (BMSR) and devise an optimization program using BMSR for computed tomography (CT) image reconstruction for an incomplete projection set. The program is built as a constrained optimization, minimizing the L1-norm of the coefficients of the image in the transformed domain subject to data observation and positivity of the image itself. To solve the program efficiently, a practical method based on the proximal point algorithm is developed and analyzed. In order to accelerate the convergence rate, a practical strategy for tuning the BMSR parameter is proposed and applied. The experimental results for various settings, including real CT scanning, have verified the proposed reconstruction method showing promising capabilities over conventional regularization.

Impact of the algorithm of iterative reconstruction ASIR in the CTDI of studies in TCHMC

International Nuclear Information System (INIS)

Ambroa Rey, E. M.; Vazquez Vazquez, R.; Gimenez Insua, M.; Sanchez Garcia, M.; Otero Martinez, C.; Luna Vega, V.; Mosquera Sueiro, J.; Lobato Busto, R.; Pombar Camean, M.

2013-01-01

The objective of this work is to make a comparison of the doses in the 10 protocols most commonly used in our Center, before and after the commissioning of the software ASIR (Adaptive statistical iterative reconstruction). (Author)

Reduction of radiation exposure and improvement of image quality with BMI-adapted prospective cardiac computed tomography and iterative reconstruction

International Nuclear Information System (INIS)

Hosch, Waldemar; Stiller, Wolfram; Mueller, Dirk; Gitsioudis, Gitsios; Welzel, Johanna; Dadrich, Monika; Buss, Sebastian J.; Giannitsis, Evangelos; Kauczor, Hans U.; Katus, Hugo A.; Korosoglou, Grigorios

2012-01-01

Purpose: To assess the impact of body mass index (BMI)-adapted protocols and iterative reconstruction algorithms (iDose) on patient radiation exposure and image quality in patients undergoing prospective ECG-triggered 256-slice coronary computed tomography angiography (CCTA). Methods: Image quality and radiation exposure were systematically analyzed in 100 patients. 60 Patients underwent prospective ECG-triggered CCTA using a non-tailored protocol and served as a ‘control’ group (Group 1: 120 kV, 200 mA s). 40 Consecutive patients with suspected coronary artery disease (CAD) underwent prospective CCTA, using BMI-adapted tube voltage and standard (Group 2: 100/120 kV, 100–200 mA s) versus reduced tube current (Group 3: 100/120 kV, 75–150 mA s). Iterative reconstructions were provided with different iDose levels and were compared to filtered back projection (FBP) reconstructions. Image quality was assessed in consensus of 2 experienced observers and using a 5-grade scale (1 = best to 5 = worse), and signal- and contrast-to-noise ratios (SNR and CNR) were quantified. Results: CCTA was performed without adverse events in all patients (n = 100, heart rate of 47–87 bpm and BMI of 19–38 kg/m 2 ). Patients examined using the non-tailored protocol in Group 1 had the highest radiation exposure (3.2 ± 0.4 mSv), followed by Group 2 (1.7 ± 0.7 mSv) and Group 3 (1.2 ± 0.6 mSv) (radiation savings of 47% and 63%, respectively, p < 0.001). Iterative reconstructions provided increased SNR and CNR, particularly when higher iDose level 5 was applied with Multi-Frequency reconstruction (iDose5 MFR) (14.1 ± 4.6 versus 21.2 ± 7.3 for SNR and 12.0 ± 4.2 versus 18.1 ± 6.6 for CNR, for FBP versus iDose5 MFR, respectively, p < 0.001). The combination of BMI adaptation with iterative reconstruction reduced radiation exposure and simultaneously improved image quality (subjective image quality of 1.4 ± 0.4 versus 1.9 ± 0.5 for Group 2 reconstructed using iDose5 MFR versus

A regularized relaxed ordered subset list-mode reconstruction algorithm and its preliminary application to undersampling PET imaging

International Nuclear Information System (INIS)

Cao, Xiaoqing; Xie, Qingguo; Xiao, Peng

2015-01-01

List mode format is commonly used in modern positron emission tomography (PET) for image reconstruction due to certain special advantages. In this work, we proposed a list mode based regularized relaxed ordered subset (LMROS) algorithm for static PET imaging. LMROS is able to work with regularization terms which can be formulated as twice differentiable convex functions. Such a versatility would make LMROS a convenient and general framework for fulfilling different regularized list mode reconstruction methods. LMROS was applied to two simulated undersampling PET imaging scenarios to verify its effectiveness. Convex quadratic function, total variation constraint, non-local means and dictionary learning based regularization methods were successfully realized for different cases. The results showed that the LMROS algorithm was effective and some regularization methods greatly reduced the distortions and artifacts caused by undersampling. (paper)

Comparison of iterative model, hybrid iterative, and filtered back projection reconstruction techniques in low-dose brain CT: impact of thin-slice imaging

Energy Technology Data Exchange (ETDEWEB)

Nakaura, Takeshi; Iyama, Yuji; Kidoh, Masafumi; Yokoyama, Koichi [Amakusa Medical Center, Diagnostic Radiology, Amakusa, Kumamoto (Japan); Kumamoto University, Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto (Japan); Oda, Seitaro; Yamashita, Yasuyuki [Kumamoto University, Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto (Japan); Tokuyasu, Shinichi [Philips Electronics, Kumamoto (Japan); Harada, Kazunori [Amakusa Medical Center, Department of Surgery, Kumamoto (Japan)

2016-03-15

The purpose of this study was to evaluate the utility of iterative model reconstruction (IMR) in brain CT especially with thin-slice images. This prospective study received institutional review board approval, and prior informed consent to participate was obtained from all patients. We enrolled 34 patients who underwent brain CT and reconstructed axial images with filtered back projection (FBP), hybrid iterative reconstruction (HIR) and IMR with 1 and 5 mm slice thicknesses. The CT number, image noise, contrast, and contrast noise ratio (CNR) between the thalamus and internal capsule, and the rate of increase of image noise in 1 and 5 mm thickness images between the reconstruction methods, were assessed. Two independent radiologists assessed image contrast, image noise, image sharpness, and overall image quality on a 4-point scale. The CNRs in 1 and 5 mm slice thickness were significantly higher with IMR (1.2 ± 0.6 and 2.2 ± 0.8, respectively) than with FBP (0.4 ± 0.3 and 1.0 ± 0.4, respectively) and HIR (0.5 ± 0.3 and 1.2 ± 0.4, respectively) (p < 0.01). The mean rate of increasing noise from 5 to 1 mm thickness images was significantly lower with IMR (1.7 ± 0.3) than with FBP (2.3 ± 0.3) and HIR (2.3 ± 0.4) (p < 0.01). There were no significant differences in qualitative analysis of unfamiliar image texture between the reconstruction techniques. IMR offers significant noise reduction and higher contrast and CNR in brain CT, especially for thin-slice images, when compared to FBP and HIR. (orig.)

Projector and backprojector for iterative CT reconstruction with blobs using CUDA

Energy Technology Data Exchange (ETDEWEB)

Bippus, Rolf-Dieter; Koehler, Thomas; Bergner, Frank; Brendel, Bernhard; Proksa, Roland [Philips Research Laboratories, Hamburg (Germany); Hansis, Eberhard [Philips Healthcare, Nuclear Medicine, San Jose, CA (United States)

2011-07-01

Using blobs allows modeling the CT system's geometry more correctly within an iterative reconstruction framework. However their application comes with an increased computational demand. This led us to use blobs for image representation and a dedicated GPU hardware implementation to counteract their computational demand. Making extensive use of the texture interpolation capabilities of CUDA and implementing an asymmetric projector/backprojector pair we achieve reasonable processing times and good system modeling at the same time. (orig.)

Dose reduction with adaptive statistical iterative reconstruction for paediatric CT: phantom study and clinical experience on chest and abdomen CT

Energy Technology Data Exchange (ETDEWEB)

Gay, F.; Lasalle, S.; Neuenschwander, S.; Brisse, H.J. [Institut Curie, Imaging Department, Paris (France); Pavia, Y.; Pierrat, N. [Institut Curie, Medical Physics Department, Paris (France)

2014-01-15

To assess the benefit and limits of iterative reconstruction of paediatric chest and abdominal computed tomography (CT). The study compared adaptive statistical iterative reconstruction (ASIR) with filtered back projection (FBP) on 64-channel MDCT. A phantom study was first performed using variable tube potential, tube current and ASIR settings. The assessed image quality indices were the signal-to-noise ratio (SNR), the noise power spectrum, low contrast detectability (LCD) and spatial resolution. A clinical retrospective study of 26 children (M:F = 14/12, mean age: 4 years, range: 1-9 years) was secondarily performed allowing comparison of 18 chest and 14 abdominal CT pairs, one with a routine CT dose and FBP reconstruction, and the other with 30 % lower dose and 40 % ASIR reconstruction. Two radiologists independently compared the images for overall image quality, noise, sharpness and artefacts, and measured image noise. The phantom study demonstrated a significant increase in SNR without impairment of the LCD or spatial resolution, except for tube current values below 30-50 mA. On clinical images, no significant difference was observed between FBP and reduced dose ASIR images. Iterative reconstruction allows at least 30 % dose reduction in paediatric chest and abdominal CT, without impairment of image quality. (orig.)

Dose reduction with adaptive statistical iterative reconstruction for paediatric CT: phantom study and clinical experience on chest and abdomen CT

International Nuclear Information System (INIS)

Gay, F.; Lasalle, S.; Neuenschwander, S.; Brisse, H.J.; Pavia, Y.; Pierrat, N.

2014-01-01

To assess the benefit and limits of iterative reconstruction of paediatric chest and abdominal computed tomography (CT). The study compared adaptive statistical iterative reconstruction (ASIR) with filtered back projection (FBP) on 64-channel MDCT. A phantom study was first performed using variable tube potential, tube current and ASIR settings. The assessed image quality indices were the signal-to-noise ratio (SNR), the noise power spectrum, low contrast detectability (LCD) and spatial resolution. A clinical retrospective study of 26 children (M:F = 14/12, mean age: 4 years, range: 1-9 years) was secondarily performed allowing comparison of 18 chest and 14 abdominal CT pairs, one with a routine CT dose and FBP reconstruction, and the other with 30 % lower dose and 40 % ASIR reconstruction. Two radiologists independently compared the images for overall image quality, noise, sharpness and artefacts, and measured image noise. The phantom study demonstrated a significant increase in SNR without impairment of the LCD or spatial resolution, except for tube current values below 30-50 mA. On clinical images, no significant difference was observed between FBP and reduced dose ASIR images. Iterative reconstruction allows at least 30 % dose reduction in paediatric chest and abdominal CT, without impairment of image quality. (orig.)

Iterative methods for photoacoustic tomography in attenuating acoustic media

Science.gov (United States)

Haltmeier, Markus; Kowar, Richard; Nguyen, Linh V.

2017-11-01

The development of efficient and accurate reconstruction methods is an important aspect of tomographic imaging. In this article, we address this issue for photoacoustic tomography. To this aim, we use models for acoustic wave propagation accounting for frequency dependent attenuation according to a wide class of attenuation laws that may include memory. We formulate the inverse problem of photoacoustic tomography in attenuating medium as an ill-posed operator equation in a Hilbert space framework that is tackled by iterative regularization methods. Our approach comes with a clear convergence analysis. For that purpose we derive explicit expressions for the adjoint problem that can efficiently be implemented. In contrast to time reversal, the employed adjoint wave equation is again damping and, thus has a stable solution. This stability property can be clearly seen in our numerical results. Moreover, the presented numerical results clearly demonstrate the efficiency and accuracy of the derived iterative reconstruction algorithms in various situations including the limited view case.

Two-way regularization for MEG source reconstruction via multilevel coordinate descent

KAUST Repository

Siva Tian, Tian

2013-12-01

Magnetoencephalography (MEG) source reconstruction refers to the inverse problem of recovering the neural activity from the MEG time course measurements. A spatiotemporal two-way regularization (TWR) method was recently proposed by Tian et al. to solve this inverse problem and was shown to outperform several one-way regularization methods and spatiotemporal methods. This TWR method is a two-stage procedure that first obtains a raw estimate of the source signals and then refines the raw estimate to ensure spatial focality and temporal smoothness using spatiotemporal regularized matrix decomposition. Although proven to be effective, the performance of two-stage TWR depends on the quality of the raw estimate. In this paper we directly solve the MEG source reconstruction problem using a multivariate penalized regression where the number of variables is much larger than the number of cases. A special feature of this regression is that the regression coefficient matrix has a spatiotemporal two-way structure that naturally invites a two-way penalty. Making use of this structure, we develop a computationally efficient multilevel coordinate descent algorithm to implement the method. This new one-stage TWR method has shown its superiority to the two-stage TWR method in three simulation studies with different levels of complexity and a real-world MEG data analysis. © 2013 Wiley Periodicals, Inc., A Wiley Company.

Breast ultrasound tomography with total-variation regularization

Energy Technology Data Exchange (ETDEWEB)

Huang, Lianjie [Los Alamos National Laboratory; Li, Cuiping [KARMANOS CANCER INSTIT.; Duric, Neb [KARMANOS CANCER INSTIT

2009-01-01

Breast ultrasound tomography is a rapidly developing imaging modality that has the potential to impact breast cancer screening and diagnosis. A new ultrasound breast imaging device (CURE) with a ring array of transducers has been designed and built at Karmanos Cancer Institute, which acquires both reflection and transmission ultrasound signals. To extract the sound-speed information from the breast data acquired by CURE, we have developed an iterative sound-speed image reconstruction algorithm for breast ultrasound transmission tomography based on total-variation (TV) minimization. We investigate applicability of the TV tomography algorithm using in vivo ultrasound breast data from 61 patients, and compare the results with those obtained using the Tikhonov regularization method. We demonstrate that, compared to the Tikhonov regularization scheme, the TV regularization method significantly improves image quality, resulting in sound-speed tomography images with sharp (preserved) edges of abnormalities and few artifacts.

A Novel Kernel-Based Regularization Technique for PET Image Reconstruction

Directory of Open Access Journals (Sweden)

Abdelwahhab Boudjelal

2017-06-01

Full Text Available Positron emission tomography (PET is an imaging technique that generates 3D detail of physiological processes at the cellular level. The technique requires a radioactive tracer, which decays and releases a positron that collides with an electron; consequently, annihilation photons are emitted, which can be measured. The purpose of PET is to use the measurement of photons to reconstruct the distribution of radioisotopes in the body. Currently, PET is undergoing a revamp, with advancements in data measurement instruments and the computing methods used to create the images. These computer methods are required to solve the inverse problem of “image reconstruction from projection”. This paper proposes a novel kernel-based regularization technique for maximum-likelihood expectation-maximization ( κ -MLEM to reconstruct the image. Compared to standard MLEM, the proposed algorithm is more robust and is more effective in removing background noise, whilst preserving the edges; this suppresses image artifacts, such as out-of-focus slice blur.

3D dictionary learning based iterative cone beam CT reconstruction

Directory of Open Access Journals (Sweden)

Ti Bai

2014-03-01

Full Text Available Purpose: This work is to develop a 3D dictionary learning based cone beam CT (CBCT reconstruction algorithm on graphic processing units (GPU to improve the quality of sparse-view CBCT reconstruction with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms of 3 × 3 × 3 was trained from a large number of blocks extracted from a high quality volume image. On the basis, we utilized cholesky decomposition based orthogonal matching pursuit algorithm to find the sparse representation of each block. To accelerate the time-consuming sparse coding in the 3D case, we implemented the sparse coding in a parallel fashion by taking advantage of the tremendous computational power of GPU. Conjugate gradient least square algorithm was adopted to minimize the data fidelity term. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with tight frame (TF by performing reconstructions on a subset data of 121 projections. Results: Compared to TF based CBCT reconstruction that shows good overall performance, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, remove more streaking artifacts and also induce less blocky artifacts. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppress the noise, and hence to achieve high quality reconstruction under the case of sparse view. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application.-------------------------------Cite this article as: Bai T, Yan H, Shi F, Jia X, Lou Y, Xu Q, Jiang S, Mou X. 3D dictionary learning based iterative cone beam CT reconstruction. Int J Cancer Ther Oncol 2014; 2(2:020240. DOI: 10

Noise-based tube current reduction method with iterative reconstruction for reduction of radiation exposure in coronary CT angiography

International Nuclear Information System (INIS)

Shen, Junlin; Du, Xiangying; Guo, Daode; Cao, Lizhen; Gao, Yan; Bai, Mei; Li, Pengyu; Liu, Jiabin; Li, Kuncheng

2013-01-01

Purpose: To investigate the potential of noise-based tube current reduction method with iterative reconstruction to reduce radiation exposure while achieving consistent image quality in coronary CT angiography (CCTA). Materials and methods: 294 patients underwent CCTA on a 64-detector row CT equipped with iterative reconstruction. 102 patients with fixed tube current were assigned to Group 1, which was used to establish noise-based tube current modulation formulas, where tube current was modulated by the noise of test bolus image. 192 patients with noise-based tube current were randomly assigned to Group 2 and Group 3. Filtered back projection was applied for Group 2 and iterative reconstruction for Group 3. Qualitative image quality was assessed with a 5 point score. Image noise, signal intensity, volume CT dose index, and dose-length product were measured. Results: The noise-based tube current modulation formulas were established through regression analysis using image noise measurements in Group 1. Image noise was precisely maintained at the target value of 35.00 HU with small interquartile ranges for Group 2 (34.17–35.08 HU) and Group 3 (34.34–35.03 HU), while it was from 28.41 to 36.49 HU for Group 1. All images in the three groups were acceptable for diagnosis. A relative 14% and 41% reduction in effective dose for Group 2 and Group 3 were observed compared with Group 1. Conclusion: Adequate image quality could be maintained at a desired and consistent noise level with overall 14% dose reduction using noise-based tube current reduction method. The use of iterative reconstruction further achieved approximately 40% reduction in effective dose

Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

Directory of Open Access Journals (Sweden)

Christian L Barrett

2006-05-01

Full Text Available The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12 that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study.

Science.gov (United States)

Wang, Chunhao; Yin, Fang-Fang; Kirkpatrick, John P; Chang, Zheng

2017-08-01

To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging. Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant K trans in the extended Tofts model and blood flow F B and blood volume V B from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated. The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between

Computed Tomography Imaging of a Hip Prosthesis Using Iterative Model-Based Reconstruction and Orthopaedic Metal Artefact Reduction: A Quantitative Analysis.

Science.gov (United States)

Wellenberg, Ruud H H; Boomsma, Martijn F; van Osch, Jochen A C; Vlassenbroek, Alain; Milles, Julien; Edens, Mireille A; Streekstra, Geert J; Slump, Cornelis H; Maas, Mario

To quantify the combined use of iterative model-based reconstruction (IMR) and orthopaedic metal artefact reduction (O-MAR) in reducing metal artefacts and improving image quality in a total hip arthroplasty phantom. Scans acquired at several dose levels and kVps were reconstructed with filtered back-projection (FBP), iterative reconstruction (iDose) and IMR, with and without O-MAR. Computed tomography (CT) numbers, noise levels, signal-to-noise-ratios and contrast-to-noise-ratios were analysed. Iterative model-based reconstruction results in overall improved image quality compared to iDose and FBP (P < 0.001). Orthopaedic metal artefact reduction is most effective in reducing severe metal artefacts improving CT number accuracy by 50%, 60%, and 63% (P < 0.05) and reducing noise by 1%, 62%, and 85% (P < 0.001) whereas improving signal-to-noise-ratios by 27%, 47%, and 46% (P < 0.001) and contrast-to-noise-ratios by 16%, 25%, and 19% (P < 0.001) with FBP, iDose, and IMR, respectively. The combined use of IMR and O-MAR strongly improves overall image quality and strongly reduces metal artefacts in the CT imaging of a total hip arthroplasty phantom.

Prospective regularization design in prior-image-based reconstruction

International Nuclear Information System (INIS)

Dang, Hao; Siewerdsen, Jeffrey H; Stayman, J Webster

2015-01-01

Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in

Development of an iterative 3D reconstruction method for the control of heavy-ion oncotherapy with PET

International Nuclear Information System (INIS)

Lauckner, K.

1999-06-01

The dissertation reports the approach and work for developing and implementing an image space reconstruction method that allows to check the 3D activity distribution and detect possible deviations from irradiation planning data. Other than usual PET scanners, the BASTEI instrument is equipped with two detectors positioned at opposite sides above and below the patient, so that there is enough space for suitable positioning of patient and radiation source. Due to the restricted field of view of the positron camera, the 3D imaging process is subject to displacement-dependent variations, creating bad reconstruction conditions. In addition, the counting rate is lower by two or three orders of magnitude than the usual counting rates of nuclear-medicine PET applications. This is why an iterative 3D algorithm is needed. Two iterative methods known from conventional PET were examined for their suitability and compared with respect to results. The MLEM algorithm proposed by Shepp and Vardi interprets the measured data as a random sample of independent variables of Poisson distributions, to be used for assessing the unknown activity distribution. A disadvantage of this algorithm is the considerable calculation effort required. For minimizing the calculation effort, and in order to make iterative statistical methods applicable to measured 3D data, Daube-Whitherspoon and Muehllehner developed the Iterative Image Space Reconstruction Algorithm, ISRA, derived through modification of the sequence of development steps of the MLEM algorithm. Problem solution with ISRA is based on least square deviation method, other than with the MLEM algorithm which uses the best probability method. (orig./CB) [de

Technical Note: Measuring contrast- and noise-dependent spatial resolution of an iterative reconstruction method in CT using ensemble averaging

Energy Technology Data Exchange (ETDEWEB)

Yu, Lifeng, E-mail: yu.lifeng@mayo.edu; Vrieze, Thomas J.; Leng, Shuai; Fletcher, Joel G.; McCollough, Cynthia H. [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States)

2015-05-15

Purpose: The spatial resolution of iterative reconstruction (IR) in computed tomography (CT) is contrast- and noise-dependent because of the nonlinear regularization. Due to the severe noise contamination, it is challenging to perform precise spatial-resolution measurements at very low-contrast levels. The purpose of this study was to measure the spatial resolution of a commercially available IR method using ensemble-averaged images acquired from repeated scans. Methods: A low-contrast phantom containing three rods (7, 14, and 21 HU below background) was scanned on a 128-slice CT scanner at three dose levels (CTDI{sub vol} = 16, 8, and 4 mGy). Images were reconstructed using two filtered-backprojection (FBP) kernels (B40 and B20) and a commercial IR method (sinogram affirmed iterative reconstruction, SAFIRE, Siemens Healthcare) with two strength settings (I40-3 and I40-5). The same scan was repeated 100 times at each dose level. The modulation transfer function (MTF) was calculated based on the edge profile measured on the ensemble-averaged images. Results: The spatial resolution of the two FBP kernels, B40 and B20, remained relatively constant across contrast and dose levels. However, the spatial resolution of the two IR kernels degraded relative to FBP as contrast or dose level decreased. For a given dose level at 16 mGy, the MTF{sub 50%} value normalized to the B40 kernel decreased from 98.4% at 21 HU to 88.5% at 7 HU for I40-3 and from 97.6% to 82.1% for I40-5. At 21 HU, the relative MTF{sub 50%} value decreased from 98.4% at 16 mGy to 90.7% at 4 mGy for I40-3 and from 97.6% to 85.6% for I40-5. Conclusions: A simple technique using ensemble averaging from repeated CT scans can be used to measure the spatial resolution of IR techniques in CT at very low contrast levels. The evaluated IR method degraded the spatial resolution at low contrast and high noise levels.

Incorporating HYPR de-noising within iterative PET reconstruction (HYPR-OSEM)

Science.gov (United States)

(Kevin Cheng, Ju-Chieh; Matthews, Julian; Sossi, Vesna; Anton-Rodriguez, Jose; Salomon, André; Boellaard, Ronald

2017-08-01

HighlY constrained back-PRojection (HYPR) is a post-processing de-noising technique originally developed for time-resolved magnetic resonance imaging. It has been recently applied to dynamic imaging for positron emission tomography and shown promising results. In this work, we have developed an iterative reconstruction algorithm (HYPR-OSEM) which improves the signal-to-noise ratio (SNR) in static imaging (i.e. single frame reconstruction) by incorporating HYPR de-noising directly within the ordered subsets expectation maximization (OSEM) algorithm. The proposed HYPR operator in this work operates on the target image(s) from each subset of OSEM and uses the sum of the preceding subset images as the composite which is updated every iteration. Three strategies were used to apply the HYPR operator in OSEM: (i) within the image space modeling component of the system matrix in forward-projection only, (ii) within the image space modeling component in both forward-projection and back-projection, and (iii) on the image estimate after the OSEM update for each subset thus generating three forms: (i) HYPR-F-OSEM, (ii) HYPR-FB-OSEM, and (iii) HYPR-AU-OSEM. Resolution and contrast phantom simulations with various sizes of hot and cold regions as well as experimental phantom and patient data were used to evaluate the performance of the three forms of HYPR-OSEM, and the results were compared to OSEM with and without a post reconstruction filter. It was observed that the convergence in contrast recovery coefficients (CRC) obtained from all forms of HYPR-OSEM was slower than that obtained from OSEM. Nevertheless, HYPR-OSEM improved SNR without degrading accuracy in terms of resolution and contrast. It achieved better accuracy in CRC at equivalent noise level and better precision than OSEM and better accuracy than filtered OSEM in general. In addition, HYPR-AU-OSEM has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision based on the studies

Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization

Energy Technology Data Exchange (ETDEWEB)

Mory, Cyril, E-mail: cyril.mory@philips.com [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, F-69621 Villeurbanne Cedex (France); Philips Research Medisys, 33 rue de Verdun, 92156 Suresnes (France); Auvray, Vincent; Zhang, Bo [Philips Research Medisys, 33 rue de Verdun, 92156 Suresnes (France); Grass, Michael; Schäfer, Dirk [Philips Research, Röntgenstrasse 24–26, D-22335 Hamburg (Germany); Chen, S. James; Carroll, John D. [Department of Medicine, Division of Cardiology, University of Colorado Denver, 12605 East 16th Avenue, Aurora, Colorado 80045 (United States); Rit, Simon [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1 (France); Centre Léon Bérard, 28 rue Laënnec, F-69373 Lyon (France); Peyrin, Françoise [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, F-69621 Villeurbanne Cedex (France); X-ray Imaging Group, European Synchrotron, Radiation Facility, BP 220, F-38043 Grenoble Cedex (France); Douek, Philippe; Boussel, Loïc [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1 (France); Hospices Civils de Lyon, 28 Avenue du Doyen Jean Lépine, 69500 Bron (France)

2014-02-15

Purpose: Reconstruction of the beating heart in 3D + time in the catheter laboratory using only the available C-arm system would improve diagnosis, guidance, device sizing, and outcome control for intracardiac interventions, e.g., electrophysiology, valvular disease treatment, structural or congenital heart disease. To obtain such a reconstruction, the patient's electrocardiogram (ECG) must be recorded during the acquisition and used in the reconstruction. In this paper, the authors present a 4D reconstruction method aiming to reconstruct the heart from a single sweep 10 s acquisition. Methods: The authors introduce the 4D RecOnstructiOn using Spatial and TEmporal Regularization (short 4D ROOSTER) method, which reconstructs all cardiac phases at once, as a 3D + time volume. The algorithm alternates between a reconstruction step based on conjugate gradient and four regularization steps: enforcing positivity, averaging along time outside a motion mask that contains the heart and vessels, 3D spatial total variation minimization, and 1D temporal total variation minimization. Results: 4D ROOSTER recovers the different temporal representations of a moving Shepp and Logan phantom, and outperforms both ECG-gated simultaneous algebraic reconstruction technique and prior image constrained compressed sensing on a clinical case. It generates 3D + time reconstructions with sharp edges which can be used, for example, to estimate the patient's left ventricular ejection fraction. Conclusions: 4D ROOSTER can be applied for human cardiac C-arm CT, and potentially in other dynamic tomography areas. It can easily be adapted to other problems as regularization is decoupled from projection and back projection.

Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization

International Nuclear Information System (INIS)

Mory, Cyril; Auvray, Vincent; Zhang, Bo; Grass, Michael; Schäfer, Dirk; Chen, S. James; Carroll, John D.; Rit, Simon; Peyrin, Françoise; Douek, Philippe; Boussel, Loïc

2014-01-01

Purpose: Reconstruction of the beating heart in 3D + time in the catheter laboratory using only the available C-arm system would improve diagnosis, guidance, device sizing, and outcome control for intracardiac interventions, e.g., electrophysiology, valvular disease treatment, structural or congenital heart disease. To obtain such a reconstruction, the patient's electrocardiogram (ECG) must be recorded during the acquisition and used in the reconstruction. In this paper, the authors present a 4D reconstruction method aiming to reconstruct the heart from a single sweep 10 s acquisition. Methods: The authors introduce the 4D RecOnstructiOn using Spatial and TEmporal Regularization (short 4D ROOSTER) method, which reconstructs all cardiac phases at once, as a 3D + time volume. The algorithm alternates between a reconstruction step based on conjugate gradient and four regularization steps: enforcing positivity, averaging along time outside a motion mask that contains the heart and vessels, 3D spatial total variation minimization, and 1D temporal total variation minimization. Results: 4D ROOSTER recovers the different temporal representations of a moving Shepp and Logan phantom, and outperforms both ECG-gated simultaneous algebraic reconstruction technique and prior image constrained compressed sensing on a clinical case. It generates 3D + time reconstructions with sharp edges which can be used, for example, to estimate the patient's left ventricular ejection fraction. Conclusions: 4D ROOSTER can be applied for human cardiac C-arm CT, and potentially in other dynamic tomography areas. It can easily be adapted to other problems as regularization is decoupled from projection and back projection

First experiences with model based iterative reconstructions influence on quantitative plaque volume and intensity measurements in coronary computed tomography angiography

DEFF Research Database (Denmark)

Precht, Helle; Kitslaar, Pieter H.; Broersen, Alexander

2017-01-01

Purpose: Investigate the influence of adaptive statistical iterative reconstruction (ASIR) and the model- based IR (Veo) reconstruction algorithm in coronary computed tomography angiography (CCTA) im- ages on quantitative measurements in coronary arteries for plaque volumes and intensities. Methods...

SU-G-IeP2-12: The Effect of Iterative Reconstruction and CT Tube Voltage On Hounsfield Unit Values of Iodinated Contrast

Energy Technology Data Exchange (ETDEWEB)

Ogden, K; Greene-Donnelly, K; Vallabhaneni, D; Scalzetti, E [SUNY Upstate Medical University, Syracuse, New York (United States)

2016-06-15

Purpose: To investigate the effects of changing iterative reconstruction strength and tube voltage on Hounsfield Unit (HU) values of varying concentrations of Iodinated contrast medium in a phantom. Method: Iodinated contrast (Omnipaque 300, GE Healthcare, Princeton NJ) was diluted with distilled water to concentrations of 0.6, 0.9, 1.8, 3.6, 7.2, and 10.8 mg/mL of Iodine. The solutions were scanned in a patient equivalent water phantom on two MDCT scanners: VCT 64 slice (GE Medical Systems, Waukesha, WI) and an Aquilion One 320 slice scanner (Toshiba America Medical Systems, Tustin CA). The phantom was scanned at 80, 100, 120, 140 kV using 400, 255, 180, and 130 mAs, respectively, for the VCT scanner, and 80, 100, 120, and 135 kV using 400, 250, 200, and 150 mAs, respectively, on the Aquilion One. Images were reconstructed at 2.5 mm (VCT) and 0.5 mm (Aquilion One). The VCT images were reconstructed using Advanced Statistical Iterative Reconstruction (ASIR) at 6 different strengths: 0%, 20%, 40%, 60%, 80%, and 100%. Aquilion One images were reconstructed using Adaptive Iterative Dose Reduction (AIDR) at 4 strengths: no AIDR, Weak AIDR, Standard AIDR, and Strong AIDR. Regions of interest (ROIs) were drawn on the images to measure the HU values and standard deviations of the diluted contrast. Second order polynomials were used to fit the HU values as a function of Iodine concentration. Results: For both scanners, there was no significant effect of changing the iterative reconstruction strength. The polynomial fits yielded goodness-of-fit (R2) values averaging 0.997. Conclusion: Changing the strength of the iterative reconstruction has no significant effect on the HU values of Iodinated contrast in a tissue-equivalent phantom. Fit values of HU vs Iodine concentration are useful in quantitative imaging protocols such as the determination of cardiac output from time-density curves in the main pulmonary artery.

The effect of iterative reconstruction on computed tomography assessment of emphysema, air trapping and airway dimensions

Energy Technology Data Exchange (ETDEWEB)

Mets, Onno M.; Willemink, Martin J.; Kort, Freek P.L. de; Leiner, Tim; Jong, Pim A. de [UMC Utrecht, Department of Radiology, P.O. Box 85500, GA, Utrecht (Netherlands); Mol, Christian P. [Utrecht University Medical Center, Image Sciences Institute, Utrecht (Netherlands); Oudkerk, Matthijs [Groningen University Medical Center, Department of Radiology, Groningen (Netherlands); Prokop, Mathias [Nijmegen University Medical Center, Department of Radiology, Nijmegen (Netherlands)

2012-10-15

To determine the influence of iterative reconstruction (IR) on quantitative computed tomography (CT) measurements of emphysema, air trapping, and airway wall and lumen dimensions, compared to filtered back-projection (FBP). Inspiratory and expiratory chest CTs of 75 patients (37 male, 38 female; mean age 64.0 {+-} 5.7 years) were reconstructed using FBP and IR. CT emphysema, CT air trapping and airway dimensions of a segmental bronchus were quantified using several commonly used quantification methods. The two algorithms were compared using the concordance correlation coefficient (p{sub c}) and Wilcoxon signed rank test. Only the E/I-ratio{sub MLD} as a measure of CT air trapping and airway dimensions showed no significant differences between the algorithms, whereas all CT emphysema and the other CT air trapping measures were significantly different at IR when compared to FBP (P < 0.001). The evaluated IR algorithm significantly influences quantitative CT measures in the assessment of emphysema and air trapping. However, the E/I-ratio{sub MLD} as a measure of CT air trapping, as well as the airway measurements, is unaffected by this reconstruction method. Quantitative CT of the lungs should be performed with careful attention to the CT protocol, especially when iterative reconstruction is introduced. (orig.)

Phillips-Tikhonov regularization with a priori information for neutron emission tomographic reconstruction on Joint European Torus

Energy Technology Data Exchange (ETDEWEB)

Bielecki, J.; Scholz, M.; Drozdowicz, K. [Institute of Nuclear Physics, Polish Academy of Sciences, PL-31342 Krakow (Poland); Giacomelli, L. [CCFE, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Istituto di Fisica del Plasma “P. Caldirola,” Milano (Italy); Kiptily, V.; Kempenaars, M. [CCFE, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Conroy, S. [CCFE, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Department of Physics and Astronomy, Uppsala University (Sweden); Craciunescu, T. [IAP, National Institute for Laser Plasma and Radiation Physics, Bucharest (Romania); Collaboration: EUROfusion Consortium, JET, Culham Science Centre, Abingdon OX14 3DB (United Kingdom)

2015-09-15

A method of tomographic reconstruction of the neutron emissivity in the poloidal cross section of the Joint European Torus (JET, Culham, UK) tokamak was developed. Due to very limited data set (two projection angles, 19 lines of sight only) provided by the neutron emission profile monitor (KN3 neutron camera), the reconstruction is an ill-posed inverse problem. The aim of this work consists in making a contribution to the development of reliable plasma tomography reconstruction methods that could be routinely used at JET tokamak. The proposed method is based on Phillips-Tikhonov regularization and incorporates a priori knowledge of the shape of normalized neutron emissivity profile. For the purpose of the optimal selection of the regularization parameters, the shape of normalized neutron emissivity profile is approximated by the shape of normalized electron density profile measured by LIDAR or high resolution Thomson scattering JET diagnostics. In contrast with some previously developed methods of ill-posed plasma tomography reconstruction problem, the developed algorithms do not include any post-processing of the obtained solution and the physical constrains on the solution are imposed during the regularization process. The accuracy of the method is at first evaluated by several tests with synthetic data based on various plasma neutron emissivity models (phantoms). Then, the method is applied to the neutron emissivity reconstruction for JET D plasma discharge #85100. It is demonstrated that this method shows good performance and reliability and it can be routinely used for plasma neutron emissivity reconstruction on JET.

Use of scanner characteristics in iterative image reconstruction for high-resolution positron emission tomography studies of small animals

Energy Technology Data Exchange (ETDEWEB)

Brix, G. [Research Program ``Radiological Diagnostics and Therapy``, German Cancer Research Center (DKFZ), Heidelberg (Germany); Doll, J. [Research Program ``Radiological Diagnostics and Therapy``, German Cancer Research Center (DKFZ), Heidelberg (Germany); Bellemann, M.E. [Research Program ``Radiological Diagnostics and Therapy``, German Cancer Research Center (DKFZ), Heidelberg (Germany); Trojan, H. [Research Program ``Radiological Diagnostics and Therapy``, German Cancer Research Center (DKFZ), Heidelberg (Germany); Haberkorn, U. [Research Program ``Radiological Diagnostics and Therapy``, German Cancer Research Center (DKFZ), Heidelberg (Germany); Schmidlin, P. [Research Program ``Radiological Diagnostics and Therapy``, German Cancer Research Center (DKFZ), Heidelberg (Germany); Ostertag, H. [Research Program ``Radiological Diagnostics and Therapy``, German Cancer Research Center (DKFZ), Heidelberg (Germany)

1997-07-01

The purpose of this work was to improve of the spatial resolution of a whole-body PET system for experimental studies of small animals by incorporation of scanner characteristics into the process of iterative image reconstruction. The image-forming characteristics of the PET camera were characterized by a spatially variant line-spread function (LSF), which was determined from 49 activated copper-64 line sources positioned over a field of view (FOV) of 21.0 cm. During the course of iterative image reconstruction, the forward projection of the estimated image was blurred with the LSF at each iteration step before the estimated projections were compared with the measured projections. Moreover, imaging studies of a rat and two nude mice were performed to evaluate the imaging properties of our approach in vivo. The spatial resolution of the scanner perpendicular to the direction of projection could be approximated by a one-dimensional Gaussian-shaped LSF with a full-width at half-maximum increasing from 6.5 mm at the centre to 6.7 mm at a radial distance of 10.5 cm. The incorporation of this blurring kernel into the iteration formula resulted in a significantly improved spatial resolution of about 3.9 mm over the examined FOV. As demonstrated by the phantom and the animal experiments, the high-resolution algorithm not only led to a better contrast resolution in the reconstructed emission scans but also improved the accuracy for quantitating activity concentrations in small tissue structures without leading to an amplification of image noise or image mottle. The presented data-handling strategy incorporates the image restoration step directly into the process of algebraic image reconstruction and obviates the need for ill-conditioned ``deconvolution`` procedures to be performed on the projections or on the reconstructed image. In our experience, the proposed algorithm is of special interest in experimental studies of small animals. (orig./AJ). With 9 figs.

Use of scanner characteristics in iterative image reconstruction for high-resolution positron emission tomography studies of small animals

International Nuclear Information System (INIS)

Brix, G.; Doll, J.; Bellemann, M.E.; Trojan, H.; Haberkorn, U.; Schmidlin, P.; Ostertag, H.

1997-01-01

The purpose of this work was to improve of the spatial resolution of a whole-body PET system for experimental studies of small animals by incorporation of scanner characteristics into the process of iterative image reconstruction. The image-forming characteristics of the PET camera were characterized by a spatially variant line-spread function (LSF), which was determined from 49 activated copper-64 line sources positioned over a field of view (FOV) of 21.0 cm. During the course of iterative image reconstruction, the forward projection of the estimated image was blurred with the LSF at each iteration step before the estimated projections were compared with the measured projections. Moreover, imaging studies of a rat and two nude mice were performed to evaluate the imaging properties of our approach in vivo. The spatial resolution of the scanner perpendicular to the direction of projection could be approximated by a one-dimensional Gaussian-shaped LSF with a full-width at half-maximum increasing from 6.5 mm at the centre to 6.7 mm at a radial distance of 10.5 cm. The incorporation of this blurring kernel into the iteration formula resulted in a significantly improved spatial resolution of about 3.9 mm over the examined FOV. As demonstrated by the phantom and the animal experiments, the high-resolution algorithm not only led to a better contrast resolution in the reconstructed emission scans but also improved the accuracy for quantitating activity concentrations in small tissue structures without leading to an amplification of image noise or image mottle. The presented data-handling strategy incorporates the image restoration step directly into the process of algebraic image reconstruction and obviates the need for ill-conditioned ''deconvolution'' procedures to be performed on the projections or on the reconstructed image. In our experience, the proposed algorithm is of special interest in experimental studies of small animals. (orig./AJ). With 9 figs

Coronary stent on coronary CT angiography: Assessment with model-based iterative reconstruction technique

Energy Technology Data Exchange (ETDEWEB)

Lee, Eun Chae; Kim, Yeo Koon; Chun, Eun Ju; Choi, Sang IL [Dept. of of Radiology, Seoul National University Bundang Hospital, Seongnam (Korea, Republic of)

2016-05-15

To assess the performance of model-based iterative reconstruction (MBIR) technique for evaluation of coronary artery stents on coronary CT angiography (CCTA). Twenty-two patients with coronary stent implantation who underwent CCTA were retrospectively enrolled for comparison of image quality between filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR) and MBIR. In each data set, image noise was measured as the standard deviation of the measured attenuation units within circular regions of interest in the ascending aorta (AA) and left main coronary artery (LM). To objectively assess the noise and blooming artifacts in coronary stent, we additionally measured the standard deviation of the measured attenuation and intra-luminal stent diameters of total 35 stents with dedicated software. All image noise measured in the AA (all p < 0.001), LM (p < 0.001, p = 0.001) and coronary stent (all p < 0.001) were significantly lower with MBIR in comparison to those with FBP or ASIR. Intraluminal stent diameter was significantly higher with MBIR, as compared with ASIR or FBP (p < 0.001, p = 0.001). MBIR can reduce image noise and blooming artifact from the stent, leading to better in-stent assessment in patients with coronary artery stent.

An adaptive regularization parameter choice strategy for multispectral bioluminescence tomography

Energy Technology Data Exchange (ETDEWEB)

Feng Jinchao; Qin Chenghu; Jia Kebin; Han Dong; Liu Kai; Zhu Shouping; Yang Xin; Tian Jie [Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China); College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124 (China); Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China); Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China) and School of Life Sciences and Technology, Xidian University, Xi' an 710071 (China)

2011-11-15

Purpose: Bioluminescence tomography (BLT) provides an effective tool for monitoring physiological and pathological activities in vivo. However, the measured data in bioluminescence imaging are corrupted by noise. Therefore, regularization methods are commonly used to find a regularized solution. Nevertheless, for the quality of the reconstructed bioluminescent source obtained by regularization methods, the choice of the regularization parameters is crucial. To date, the selection of regularization parameters remains challenging. With regards to the above problems, the authors proposed a BLT reconstruction algorithm with an adaptive parameter choice rule. Methods: The proposed reconstruction algorithm uses a diffusion equation for modeling the bioluminescent photon transport. The diffusion equation is solved with a finite element method. Computed tomography (CT) images provide anatomical information regarding the geometry of the small animal and its internal organs. To reduce the ill-posedness of BLT, spectral information and the optimal permissible source region are employed. Then, the relationship between the unknown source distribution and multiview and multispectral boundary measurements is established based on the finite element method and the optimal permissible source region. Since the measured data are noisy, the BLT reconstruction is formulated as l{sub 2} data fidelity and a general regularization term. When choosing the regularization parameters for BLT, an efficient model function approach is proposed, which does not require knowledge of the noise level. This approach only requests the computation of the residual and regularized solution norm. With this knowledge, we construct the model function to approximate the objective function, and the regularization parameter is updated iteratively. Results: First, the micro-CT based mouse phantom was used for simulation verification. Simulation experiments were used to illustrate why multispectral data were used

Development of acoustic model-based iterative reconstruction technique for thick-concrete imaging

Science.gov (United States)

Almansouri, Hani; Clayton, Dwight; Kisner, Roger; Polsky, Yarom; Bouman, Charles; Santos-Villalobos, Hector

2016-02-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well's health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.1

Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Energy Technology Data Exchange (ETDEWEB)

Almansouri, Hani [Purdue University; Clayton, Dwight A [ORNL; Kisner, Roger A [ORNL; Polsky, Yarom [ORNL; Bouman, Charlie [Purdue University; Santos-Villalobos, Hector J [ORNL

2016-01-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well's health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.

Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Energy Technology Data Exchange (ETDEWEB)

Almansouri, Hani [Purdue University; Clayton, Dwight A [ORNL; Kisner, Roger A [ORNL; Polsky, Yarom [ORNL; Bouman, Charlie [Purdue University; Santos-Villalobos, Hector J [ORNL

2015-01-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well s health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.

Toward robust high resolution fluorescence tomography: a hybrid row-action edge preserving regularization

Science.gov (United States)

Behrooz, Ali; Zhou, Hao-Min; Eftekhar, Ali A.; Adibi, Ali

2011-02-01

Depth-resolved localization and quantification of fluorescence distribution in tissue, called Fluorescence Molecular Tomography (FMT), is highly ill-conditioned as depth information should be extracted from limited number of surface measurements. Inverse solvers resort to regularization algorithms that penalize Euclidean norm of the solution to overcome ill-posedness. While these regularization algorithms offer good accuracy, their smoothing effects result in continuous distributions which lack high-frequency edge-type features of the actual fluorescence distribution and hence limit the resolution offered by FMT. We propose an algorithm that penalizes the total variation (TV) norm of the solution to preserve sharp transitions and high-frequency components in the reconstructed fluorescence map while overcoming ill-posedness. The hybrid algorithm is composed of two levels: 1) An Algebraic Reconstruction Technique (ART), performed on FMT data for fast recovery of a smooth solution that serves as an initial guess for the iterative TV regularization, 2) A time marching TV regularization algorithm, inspired by the Rudin-Osher-Fatemi TV image restoration, performed on the initial guess to further enhance the resolution and accuracy of the reconstruction. The performance of the proposed method in resolving fluorescent tubes inserted in a liquid tissue phantom imaged by a non-contact CW trans-illumination FMT system is studied and compared to conventional regularization schemes. It is observed that the proposed method performs better in resolving fluorescence inclusions at higher depths.

A noise power spectrum study of a new model‐based iterative reconstruction system: Veo 3.0

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Li, Guang; Liu, Xinming; Dodge, Cristina T.; Jensen, Corey T.

2016-01-01

The purpose of this study was to evaluate performance of the third generation of model‐based iterative reconstruction (MBIR) system, Veo 3.0, based on noise power spectrum (NPS) analysis with various clinical presets over a wide range of clinically applicable dose levels. A CatPhan 600 surrounded by an oval, fat‐equivalent ring to mimic patient size/shape was scanned 10 times at each of six dose levels on a GE HD 750 scanner. NPS analysis was performed on images reconstructed with various Veo 3.0 preset combinations for comparisons of those images reconstructed using Veo 2.0, filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASiR). The new Target Thickness setting resulted in higher noise in thicker axial images. The new Texture Enhancement function achieved a more isotropic noise behavior with less image artifacts. Veo 3.0 provides additional reconstruction options designed to allow the user choice of balance between spatial resolution and image noise, relative to Veo 2.0. Veo 3.0 provides more user selectable options and in general improved isotropic noise behavior in comparison to Veo 2.0. The overall noise reduction performance of both versions of MBIR was improved in comparison to FBP and ASiR, especially at low‐dose levels. PACS number(s): 87.57.‐s, 87.57.Q‐, 87.57.C‐, 87.57.nf, 87.57.C‐, 87.57.cm PMID:27685118

Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms.

Science.gov (United States)

Liu, Li; Lin, Weikai; Jin, Mingwu

2015-01-01

In this paper, we propose two reconstruction algorithms for sparse-view X-ray computed tomography (CT). Treating the reconstruction problems as data fidelity constrained total variation (TV) minimization, both algorithms adapt the alternate two-stage strategy: projection onto convex sets (POCS) for data fidelity and non-negativity constraints and steepest descent for TV minimization. The novelty of this work is to determine iterative parameters automatically from data, thus avoiding tedious manual parameter tuning. In TV minimization, the step sizes of steepest descent are adaptively adjusted according to the difference from POCS update in either the projection domain or the image domain, while the step size of algebraic reconstruction technique (ART) in POCS is determined based on the data noise level. In addition, projection errors are used to compare with the error bound to decide whether to perform ART so as to reduce computational costs. The performance of the proposed methods is studied and evaluated using both simulated and physical phantom data. Our methods with automatic parameter tuning achieve similar, if not better, reconstruction performance compared to a representative two-stage algorithm. Copyright © 2014 Elsevier Ltd. All rights reserved.

Image quality of multiplanar reconstruction of pulmonary CT scans using adaptive statistical iterative reconstruction.

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Honda, O; Yanagawa, M; Inoue, A; Kikuyama, A; Yoshida, S; Sumikawa, H; Tobino, K; Koyama, M; Tomiyama, N

2011-04-01

We investigated the image quality of multiplanar reconstruction (MPR) using adaptive statistical iterative reconstruction (ASIR). Inflated and fixed lungs were scanned with a garnet detector CT in high-resolution mode (HR mode) or non-high-resolution (HR) mode, and MPR images were then reconstructed. Observers compared 15 MPR images of ASIR (40%) and ASIR (80%) with those of ASIR (0%), and assessed image quality using a visual five-point scale (1, definitely inferior; 5, definitely superior), with particular emphasis on normal pulmonary structures, artefacts, noise and overall image quality. The mean overall image quality scores in HR mode were 3.67 with ASIR (40%) and 4.97 with ASIR (80%). Those in non-HR mode were 3.27 with ASIR (40%) and 3.90 with ASIR (80%). The mean artefact scores in HR mode were 3.13 with ASIR (40%) and 3.63 with ASIR (80%), but those in non-HR mode were 2.87 with ASIR (40%) and 2.53 with ASIR (80%). The mean scores of the other parameters were greater than 3, whereas those in HR mode were higher than those in non-HR mode. There were significant differences between ASIR (40%) and ASIR (80%) in overall image quality (pASIR did not suppress the severe artefacts of contrast medium. In general, MPR image quality with ASIR (80%) was superior to that with ASIR (40%). However, there was an increased incidence of artefacts by ASIR when CT images were obtained in non-HR mode.

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography.

Science.gov (United States)

Precht, Helle; Thygesen, Jesper; Gerke, Oke; Egstrup, Kenneth; Waaler, Dag; Lambrechtsen, Jess

2016-12-01

Coronary computed tomography angiography (CCTA) requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR) techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure. To evaluate whether adaptive statistical iterative reconstruction (ASIR) enhances perceived image quality in CCTA compared to filtered back projection (FBP). Thirty patients underwent CCTA due to suspected coronary artery disease. Images were reconstructed using FBP, 30% ASIR, and 60% ASIR. Ninety image sets were evaluated by five observers using the subjective visual grading analysis (VGA) and assessed by proportional odds modeling. Objective quality assessment (contrast, noise, and the contrast-to-noise ratio [CNR]) was analyzed with linear mixed effects modeling on log-transformed data. The need for ethical approval was waived by the local ethics committee as the study only involved anonymously collected clinical data. VGA showed significant improvements in sharpness by comparing FBP with ASIR, resulting in odds ratios of 1.54 for 30% ASIR and 1.89 for 60% ASIR ( P  = 0.004). The objective measures showed significant differences between FBP and 60% ASIR ( P  < 0.0001) for noise, with an estimated ratio of 0.82, and for CNR, with an estimated ratio of 1.26. ASIR improved the subjective image quality of parameter sharpness and, objectively, reduced noise and increased CNR.

Iterative approach to self-adapting and altitude-dependent regularization for atmospheric profile retrievals.

Science.gov (United States)

Ridolfi, Marco; Sgheri, Luca

2011-12-19

In this paper we present the IVS (Iterative Variable Strength) method, an altitude-dependent, self-adapting Tikhonov regularization scheme for atmospheric profile retrievals. The method is based on a similar scheme we proposed in 2009. The new method does not need any specifically tuned minimization routine, hence it is more robust and faster. We test the self-consistency of the method using simulated observations of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). We then compare the new method with both our previous scheme and the scalar method currently implemented in the MIPAS on-line processor, using both synthetic and real atmospheric limb measurements. The IVS method shows very good performances.

Penalized maximum likelihood reconstruction for x-ray differential phase-contrast tomography

International Nuclear Information System (INIS)

Brendel, Bernhard; Teuffenbach, Maximilian von; Noël, Peter B.; Pfeiffer, Franz; Koehler, Thomas

2016-01-01

Purpose: The purpose of this work is to propose a cost function with regularization to iteratively reconstruct attenuation, phase, and scatter images simultaneously from differential phase contrast (DPC) acquisitions, without the need of phase retrieval, and examine its properties. Furthermore this reconstruction method is applied to an acquisition pattern that is suitable for a DPC tomographic system with continuously rotating gantry (sliding window acquisition), overcoming the severe smearing in noniterative reconstruction. Methods: We derive a penalized maximum likelihood reconstruction algorithm to directly reconstruct attenuation, phase, and scatter image from the measured detector values of a DPC acquisition. The proposed penalty comprises, for each of the three images, an independent smoothing prior. Image quality of the proposed reconstruction is compared to images generated with FBP and iterative reconstruction after phase retrieval. Furthermore, the influence between the priors is analyzed. Finally, the proposed reconstruction algorithm is applied to experimental sliding window data acquired at a synchrotron and results are compared to reconstructions based on phase retrieval. Results: The results show that the proposed algorithm significantly increases image quality in comparison to reconstructions based on phase retrieval. No significant mutual influence between the proposed independent priors could be observed. Further it could be illustrated that the iterative reconstruction of a sliding window acquisition results in images with substantially reduced smearing artifacts. Conclusions: Although the proposed cost function is inherently nonconvex, it can be used to reconstruct images with less aliasing artifacts and less streak artifacts than reconstruction methods based on phase retrieval. Furthermore, the proposed method can be used to reconstruct images of sliding window acquisitions with negligible smearing artifacts

The effect of iterative reconstruction on computed tomography assessment of emphysema, air trapping and airway dimensions

NARCIS (Netherlands)

Mets, Onno M.; Willemink, Martin J.; de Kort, Freek P. L.; Mol, Christian P.; Leiner, Tim; Oudkerk, Matthijs; Prokop, Mathias; de Jong, Pim A.

2012-01-01

To determine the influence of iterative reconstruction (IR) on quantitative computed tomography (CT) measurements of emphysema, air trapping, and airway wall and lumen dimensions, compared to filtered back-projection (FBP). Inspiratory and expiratory chest CTs of 75 patients (37 male, 38 female;

Determination of the optimal dose reduction level via iterative reconstruction using 640-slice volume chest CT in a pig model.

Directory of Open Access Journals (Sweden)

Xingli Liu

Full Text Available To determine the optimal dose reduction level of iterative reconstruction technique for paediatric chest CT in pig models.27 infant pigs underwent 640-slice volume chest CT with 80kVp and different mAs. Automatic exposure control technique was used, and the index of noise was set to SD10 (Group A, routine dose, SD12.5, SD15, SD17.5, SD20 (Groups from B to E to reduce dose respectively. Group A was reconstructed with filtered back projection (FBP, and Groups from B to E were reconstructed using iterative reconstruction (IR. Objective and subjective image quality (IQ among groups were compared to determine an optimal radiation reduction level.The noise and signal-to-noise ratio (SNR in Group D had no significant statistical difference from that in Group A (P = 1.0. The scores of subjective IQ in Group A were not significantly different from those in Group D (P>0.05. There were no obvious statistical differences in the objective and subjective index values among the subgroups (small, medium and large subgroups of Group D. The effective dose (ED of Group D was 58.9% lower than that of Group A (0.20±0.05mSv vs 0.48±0.10mSv, p <0.001.In infant pig chest CT, using iterative reconstruction can provide diagnostic image quality; furthermore, it can reduce the dosage by 58.9%.

Efficient methodologies for system matrix modelling in iterative image reconstruction for rotating high-resolution PET

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Ortuno, J E; Kontaxakis, G; Rubio, J L; Santos, A [Departamento de Ingenieria Electronica (DIE), Universidad Politecnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid (Spain); Guerra, P [Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid (Spain)], E-mail: juanen@die.upm.es

2010-04-07

A fully 3D iterative image reconstruction algorithm has been developed for high-resolution PET cameras composed of pixelated scintillator crystal arrays and rotating planar detectors, based on the ordered subsets approach. The associated system matrix is precalculated with Monte Carlo methods that incorporate physical effects not included in analytical models, such as positron range effects and interaction of the incident gammas with the scintillator material. Custom Monte Carlo methodologies have been developed and optimized for modelling of system matrices for fast iterative image reconstruction adapted to specific scanner geometries, without redundant calculations. According to the methodology proposed here, only one-eighth of the voxels within two central transaxial slices need to be modelled in detail. The rest of the system matrix elements can be obtained with the aid of axial symmetries and redundancies, as well as in-plane symmetries within transaxial slices. Sparse matrix techniques for the non-zero system matrix elements are employed, allowing for fast execution of the image reconstruction process. This 3D image reconstruction scheme has been compared in terms of image quality to a 2D fast implementation of the OSEM algorithm combined with Fourier rebinning approaches. This work confirms the superiority of fully 3D OSEM in terms of spatial resolution, contrast recovery and noise reduction as compared to conventional 2D approaches based on rebinning schemes. At the same time it demonstrates that fully 3D methodologies can be efficiently applied to the image reconstruction problem for high-resolution rotational PET cameras by applying accurate pre-calculated system models and taking advantage of the system's symmetries.

Development of an iterative reconstruction method to overcome 2D detector low resolution limitations in MLC leaf position error detection for 3D dose verification in IMRT

NARCIS (Netherlands)

Visser, Ruurd; J., Godart; Wauben, D.J.L.; Langendijk, J.; van 't Veld, A.A.; Korevaar, E.W.

2016-01-01

The objective of this study was to introduce a new iterative method to reconstruct multi leaf collimator (MLC) positions based on low resolution ionization detector array measurements and to evaluate its error detection performance. The iterative reconstruction method consists of a fluence model, a

Impact of the adaptive statistical iterative reconstruction technique on image quality in ultra-low-dose CT

International Nuclear Information System (INIS)

Xu, Yan; He, Wen; Chen, Hui; Hu, Zhihai; Li, Juan; Zhang, Tingting

2013-01-01

Aim: To evaluate the relationship between different noise indices (NIs) and radiation dose and to compare the effect of different reconstruction algorithm applications for ultra-low-dose chest computed tomography (CT) on image quality improvement and the accuracy of volumetric measurement of ground-glass opacity (GGO) nodules using a phantom study. Materials and methods: A 11 cm thick transverse phantom section with a chest wall, mediastinum, and 14 artificial GGO nodules with known volumes (919.93 ± 64.05 mm 3 ) was constructed. The phantom was scanned on a Discovery CT 750HD scanner with five different NIs (NIs = 20, 30, 40, 50, and 60). All data were reconstructed with a 0.625 mm section thickness using the filtered back-projection (FBP), 50% adaptive statistical iterative reconstruction (ASiR), and Veo model-base iterative reconstruction algorithms. Image noise was measured in six regions of interest (ROIs). Nodule volumes were measured using a commercial volumetric software package. The image quality and the volume measurement errors were analysed. Results: Image noise increased dramatically from 30.7 HU at NI 20 to 122.4 HU at NI 60, with FBP reconstruction. Conversely, Veo reconstruction effectively controlled the noise increase, with an increase from 9.97 HU at NI 20 to only 15.1 HU at NI 60. Image noise at NI 60 with Veo was even lower (50.8%) than that at NI 20 with FBP. The contrast-to-noise ratio (CNR) of Veo at NI 40 was similar to that of FBP at NI 20. All artificial GGO nodules were successfully identified and measured with an average relative volume measurement error with Veo at NI 60 of 4.24%, comparable to a value of 10.41% with FBP at NI 20. At NI 60, the radiation dose was only one-tenth that at NI 20. Conclusion: The Veo reconstruction algorithms very effectively reduced image noise compared with the conventional FBP reconstructions. Using ultra-low-dose CT scanning and Veo reconstruction, GGOs can be detected and quantified with an acceptable

Feasibility of low-dose CT with model-based iterative image reconstruction in follow-up of patients with testicular cancer

International Nuclear Information System (INIS)

Murphy, Kevin P.; Crush, Lee; O’Neill, Siobhan B.; Foody, James; Breen, Micheál; Brady, Adrian; Kelly, Paul J.; Power, Derek G.; Sweeney, Paul; Bye, Jackie; O’Connor, Owen J.; Maher, Michael M.; O’Regan, Kevin N.

2016-01-01

•Radiologists should endeavour to minimise radiation exposure to patients with testicular cancer.•Iterative reconstruction algorithms permit CT imaging at lower radiation doses.•Image quality for reduced-dose CT–MBIR is at least comparable to conventional dose.•No loss of diagnostic accuracy apparent with reduced-dose CT–MBIR. Radiologists should endeavour to minimise radiation exposure to patients with testicular cancer. Iterative reconstruction algorithms permit CT imaging at lower radiation doses. Image quality for reduced-dose CT–MBIR is at least comparable to conventional dose. No loss of diagnostic accuracy apparent with reduced-dose CT–MBIR. We examine the performance of pure model-based iterative reconstruction with reduced-dose CT in follow-up of patients with early-stage testicular cancer. Sixteen patients (mean age 35.6 ± 7.4 years) with stage I or II testicular cancer underwent conventional dose (CD) and low-dose (LD) CT acquisition during CT surveillance. LD data was reconstructed with model-based iterative reconstruction (LD–MBIR). Datasets were objectively and subjectively analysed at 8 anatomical levels. Two blinded clinical reads were compared to gold-standard assessment for diagnostic accuracy. Mean radiation dose reduction of 67.1% was recorded. Mean dose measurements for LD–MBIR were: thorax – 66 ± 11 mGy cm (DLP), 1.0 ± 0.2 mSv (ED), 2.0 ± 0.4 mGy (SSDE); abdominopelvic – 128 ± 38 mGy cm (DLP), 1.9 ± 0.6 mSv (ED), 3.0 ± 0.6 mGy (SSDE). Objective noise and signal-to-noise ratio values were comparable between the CD and LD–MBIR images. LD–MBIR images were superior (p < 0.001) with regard to subjective noise, streak artefact, 2-plane contrast resolution, 2-plane spatial resolution and diagnostic acceptability. All patients were correctly categorised as positive, indeterminate or negative for metastatic disease by 2 readers on LD–MBIR and CD datasets. MBIR facilitated a 67% reduction in radiation dose whilst

The impact of iterative reconstruction in low-dose computed tomography on the evaluation of diffuse interstitial lung disease

Energy Technology Data Exchange (ETDEWEB)

Lim, Hyun Ju; Chung, Myung Jin; Shin, Kyung Eun; Hwang, Hye Sun; Lee, Kyung Soo [Dept. of Radiology nd Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

2016-11-15

To evaluate the impact of iterative reconstruction (IR) on the assessment of diffuse interstitial lung disease (DILD) using CT. An American College of Radiology (ACR) phantom (module 4 to assess spatial resolution) was scanned with 10-100 effective mAs at 120 kVp. The images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), with blending ratios of 0%, 30%, 70% and 100%, and model-based iterative reconstruction (MBIR), and their spatial resolution was objectively assessed by the line pair structure method. The patient study was based on retrospective interpretation of prospectively acquired data, and it was approved by the institutional review board. Chest CT scans of 23 patients (mean age 64 years) were performed at 120 kVp using 1) standard dose protocol applying 142-275 mA with dose modulation (high-resolution computed tomography [HRCT]) and 2) low-dose protocol applying 20 mA (low dose CT, LDCT). HRCT images were reconstructed with FBP, and LDCT images were reconstructed using FBP, ASIR, and MBIR. Matching images were randomized and independently reviewed by chest radiologists. Subjective assessment of disease presence and radiological diagnosis was made on a 10-point scale. In addition, semi-quantitative results were compared for the extent of abnormalities estimated to the nearest 5% of parenchymal involvement. In the phantom study, ASIR was comparable to FBP in terms of spatial resolution. However, for MBIR, the spatial resolution was greatly decreased under 10 mA. In the patient study, the detection of the presence of disease was not significantly different. The values for area under the curve for detection of DILD by HRCT, FBP, ASIR, and MBIR were as follows: 0.978, 0.979, 0.972, and 0.963. LDCT images reconstructed with FBP, ASIR, and MBIR tended to underestimate reticular or honeycombing opacities (-2.8%, -4.1%, and -5.3%, respectively) and overestimate ground glass opacities (+4.6%, +8.9%, and

Technical Note: FreeCT_ICD: An Open Source Implementation of a Model-Based Iterative Reconstruction Method using Coordinate Descent Optimization for CT Imaging Investigations.

Science.gov (United States)

Hoffman, John M; Noo, Frédéric; Young, Stefano; Hsieh, Scott S; McNitt-Gray, Michael

2018-06-01

To facilitate investigations into the impacts of acquisition and reconstruction parameters on quantitative imaging, radiomics and CAD using CT imaging, we previously released an open source implementation of a conventional weighted filtered backprojection reconstruction called FreeCT_wFBP. Our purpose was to extend that work by providing an open-source implementation of a model-based iterative reconstruction method using coordinate descent optimization, called FreeCT_ICD. Model-based iterative reconstruction offers the potential for substantial radiation dose reduction, but can impose substantial computational processing and storage requirements. FreeCT_ICD is an open source implementation of a model-based iterative reconstruction method that provides a reasonable tradeoff between these requirements. This was accomplished by adapting a previously proposed method that allows the system matrix to be stored with a reasonable memory requirement. The method amounts to describing the attenuation coefficient using rotating slices that follow the helical geometry. In the initially-proposed version, the rotating slices are themselves described using blobs. We have replaced this description by a unique model that relies on tri-linear interpolation together with the principles of Joseph's method. This model offers an improvement in memory requirement while still allowing highly accurate reconstruction for conventional CT geometries. The system matrix is stored column-wise and combined with an iterative coordinate descent (ICD) optimization. The result is FreeCT_ICD, which is a reconstruction program developed on the Linux platform using C++ libraries and the open source GNU GPL v2.0 license. The software is capable of reconstructing raw projection data of helical CT scans. In this work, the software has been described and evaluated by reconstructing datasets exported from a clinical scanner which consisted of an ACR accreditation phantom dataset and a clinical pediatric

Image Reconstruction. Chapter 13

Energy Technology Data Exchange (ETDEWEB)

Nuyts, J. [Department of Nuclear Medicine and Medical Imaging Research Center, Katholieke Universiteit Leuven, Leuven (Belgium); Matej, S. [Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA (United States)

2014-12-15

This chapter discusses how 2‑D or 3‑D images of tracer distribution can be reconstructed from a series of so-called projection images acquired with a gamma camera or a positron emission tomography (PET) system [13.1]. This is often called an ‘inverse problem’. The reconstruction is the inverse of the acquisition. The reconstruction is called an inverse problem because making software to compute the true tracer distribution from the acquired data turns out to be more difficult than the ‘forward’ direction, i.e. making software to simulate the acquisition. There are basically two approaches to image reconstruction: analytical reconstruction and iterative reconstruction. The analytical approach is based on mathematical inversion, yielding efficient, non-iterative reconstruction algorithms. In the iterative approach, the reconstruction problem is reduced to computing a finite number of image values from a finite number of measurements. That simplification enables the use of iterative instead of mathematical inversion. Iterative inversion tends to require more computer power, but it can cope with more complex (and hopefully more accurate) models of the acquisition process.

Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data

Directory of Open Access Journals (Sweden)

Jaewook Jung

2017-03-01

Full Text Available With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL combined with Hypothesize and Test (HAT. The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International

Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data.

Science.gov (United States)

Jung, Jaewook; Jwa, Yoonseok; Sohn, Gunho

2017-03-19

With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS) data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP) technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International Society for

Implementation of non-linear filters for iterative penalized maximum likelihood image reconstruction

International Nuclear Information System (INIS)

Liang, Z.; Gilland, D.; Jaszczak, R.; Coleman, R.

1990-01-01

In this paper, the authors report on the implementation of six edge-preserving, noise-smoothing, non-linear filters applied in image space for iterative penalized maximum-likelihood (ML) SPECT image reconstruction. The non-linear smoothing filters implemented were the median filter, the E 6 filter, the sigma filter, the edge-line filter, the gradient-inverse filter, and the 3-point edge filter with gradient-inverse filter, and the 3-point edge filter with gradient-inverse weight. A 3 x 3 window was used for all these filters. The best image obtained, by viewing the profiles through the image in terms of noise-smoothing, edge-sharpening, and contrast, was the one smoothed with the 3-point edge filter. The computation time for the smoothing was less than 1% of one iteration, and the memory space for the smoothing was negligible. These images were compared with the results obtained using Bayesian analysis

Renal Cyst Pseudoenhancement: Intraindividual Comparison Between Virtual Monochromatic Spectral Images and Conventional Polychromatic 120-kVp Images Obtained During the Same CT Examination and Comparisons Among Images Reconstructed Using Filtered Back Projection, Adaptive Statistical Iterative Reconstruction, and Model-Based Iterative Reconstruction

Science.gov (United States)

Yamada, Yoshitake; Yamada, Minoru; Sugisawa, Koichi; Akita, Hirotaka; Shiomi, Eisuke; Abe, Takayuki; Okuda, Shigeo; Jinzaki, Masahiro

2015-01-01

Abstract The purpose of this study was to compare renal cyst pseudoenhancement between virtual monochromatic spectral (VMS) and conventional polychromatic 120-kVp images obtained during the same abdominal computed tomography (CT) examination and among images reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Our institutional review board approved this prospective study; each participant provided written informed consent. Thirty-one patients (19 men, 12 women; age range, 59–85 years; mean age, 73.2 ± 5.5 years) with renal cysts underwent unenhanced 120-kVp CT followed by sequential fast kVp-switching dual-energy (80/140 kVp) and 120-kVp abdominal enhanced CT in the nephrographic phase over a 10-cm scan length with a random acquisition order and 4.5-second intervals. Fifty-one renal cysts (maximal diameter, 18.0 ± 14.7 mm [range, 4–61 mm]) were identified. The CT attenuation values of the cysts as well as of the kidneys were measured on the unenhanced images, enhanced VMS images (at 70 keV) reconstructed using FBP and ASIR from dual-energy data, and enhanced 120-kVp images reconstructed using FBP, ASIR, and MBIR. The results were analyzed using the mixed-effects model and paired t test with Bonferroni correction. The attenuation increases (pseudoenhancement) of the renal cysts on the VMS images reconstructed using FBP/ASIR (least square mean, 5.0/6.0 Hounsfield units [HU]; 95% confidence interval, 2.6–7.4/3.6–8.4 HU) were significantly lower than those on the conventional 120-kVp images reconstructed using FBP/ASIR/MBIR (least square mean, 12.1/12.8/11.8 HU; 95% confidence interval, 9.8–14.5/10.4–15.1/9.4–14.2 HU) (all P < .001); on the other hand, the CT attenuation values of the kidneys on the VMS images were comparable to those on the 120-kVp images. Regardless of the reconstruction algorithm, 70-keV VMS images showed

WE-FG-207B-03: Multi-Energy CT Reconstruction with Spatial Spectral Nonlocal Means Regularization

Energy Technology Data Exchange (ETDEWEB)

Li, B [University of Texas Southwestern Medical Center, Dallas, TX (United States); Southern Medical University, Guangzhou, Guangdong (China); Shen, C; Ouyang, L; Yang, M; Jiang, S; Jia, X [University of Texas Southwestern Medical Center, Dallas, TX (United States); Zhou, L [Southern Medical University, Guangzhou, Guangdong (China)

2016-06-15

Purpose: Multi-energy computed tomography (MECT) is an emerging application in medical imaging due to its ability of material differentiation and potential for molecular imaging. In MECT, image correlations at different spatial and channels exist. It is desirable to incorporate these correlations in reconstruction to improve image quality. For this purpose, this study proposes a MECT reconstruction technique that employes spatial spectral non-local means (ssNLM) regularization. Methods: We consider a kVp-switching scanning method in which source energy is rapidly switched during data acquisition. For each energy channel, this yields projection data acquired at a number of angles, whereas projection angles among channels are different. We formulate the reconstruction task as an optimziation problem. A least square term enfores data fidelity. A ssNLM term is used as regularization to encourage similarities among image patches at different spatial locations and channels. When comparing image patches at different channels, intensity difference were corrected by a transformation estimated via histogram equalization during the reconstruction process. Results: We tested our method in a simulation study with a NCAT phantom and an experimental study with a Gammex phantom. For comparison purpose, we also performed reconstructions using conjugate-gradient least square (CGLS) method and conventional NLM method that only considers spatial correlation in an image. ssNLM is able to better suppress streak artifacts. The streaks are along different projection directions in images at different channels. ssNLM discourages this dissimilarity and hence removes them. True image structures are preserved in this process. Measurements in regions of interests yield 1.1 to 3.2 and 1.5 to 1.8 times higher contrast to noise ratio than the NLM approach. Improvements over CGLS is even more profound due to lack of regularization in the CGLS method and hence amplified noise. Conclusion: The

Regularization design for high-quality cone-beam CT of intracranial hemorrhage using statistical reconstruction

Science.gov (United States)

Dang, H.; Stayman, J. W.; Xu, J.; Sisniega, A.; Zbijewski, W.; Wang, X.; Foos, D. H.; Aygun, N.; Koliatsos, V. E.; Siewerdsen, J. H.

2016-03-01

Intracranial hemorrhage (ICH) is associated with pathologies such as hemorrhagic stroke and traumatic brain injury. Multi-detector CT is the current front-line imaging modality for detecting ICH (fresh blood contrast 40-80 HU, down to 1 mm). Flat-panel detector (FPD) cone-beam CT (CBCT) offers a potential alternative with a smaller scanner footprint, greater portability, and lower cost potentially well suited to deployment at the point of care outside standard diagnostic radiology and emergency room settings. Previous studies have suggested reliable detection of ICH down to 3 mm in CBCT using high-fidelity artifact correction and penalized weighted least-squared (PWLS) image reconstruction with a post-artifact-correction noise model. However, ICH reconstructed by traditional image regularization exhibits nonuniform spatial resolution and noise due to interaction between the statistical weights and regularization, which potentially degrades the detectability of ICH. In this work, we propose three regularization methods designed to overcome these challenges. The first two compute spatially varying certainty for uniform spatial resolution and noise, respectively. The third computes spatially varying regularization strength to achieve uniform "detectability," combining both spatial resolution and noise in a manner analogous to a delta-function detection task. Experiments were conducted on a CBCT test-bench, and image quality was evaluated for simulated ICH in different regions of an anthropomorphic head. The first two methods improved the uniformity in spatial resolution and noise compared to traditional regularization. The third exhibited the highest uniformity in detectability among all methods and best overall image quality. The proposed regularization provides a valuable means to achieve uniform image quality in CBCT of ICH and is being incorporated in a CBCT prototype for ICH imaging.

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

International Nuclear Information System (INIS)

Bai, T; Yan, H; Shi, F; Jia, X; Jiang, Steve B.; Lou, Y; Xu, Q; Mou, X

2014-01-01

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

Energy Technology Data Exchange (ETDEWEB)

Bai, T [Xi' an Jiaotong University, Xi' an (China); UT Southwestern Medical Center, Dallas, TX (United States); Yan, H; Shi, F; Jia, X; Jiang, Steve B. [UT Southwestern Medical Center, Dallas, TX (United States); Lou, Y [University of California Irvine, Irvine, CA (United States); Xu, Q; Mou, X [Xi' an Jiaotong University, Xi' an (China)

2014-06-15

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography

Directory of Open Access Journals (Sweden)

Helle Precht

2016-12-01

Full Text Available Background Coronary computed tomography angiography (CCTA requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure. Purpose To evaluate whether adaptive statistical iterative reconstruction (ASIR enhances perceived image quality in CCTA compared to filtered back projection (FBP. Material and Methods Thirty patients underwent CCTA due to suspected coronary artery disease. Images were reconstructed using FBP, 30% ASIR, and 60% ASIR. Ninety image sets were evaluated by five observers using the subjective visual grading analysis (VGA and assessed by proportional odds modeling. Objective quality assessment (contrast, noise, and the contrast-to-noise ratio [CNR] was analyzed with linear mixed effects modeling on log-transformed data. The need for ethical approval was waived by the local ethics committee as the study only involved anonymously collected clinical data. Results VGA showed significant improvements in sharpness by comparing FBP with ASIR, resulting in odds ratios of 1.54 for 30% ASIR and 1.89 for 60% ASIR (P = 0.004. The objective measures showed significant differences between FBP and 60% ASIR (P < 0.0001 for noise, with an estimated ratio of 0.82, and for CNR, with an estimated ratio of 1.26. Conclusion ASIR improved the subjective image quality of parameter sharpness and, objectively, reduced noise and increased CNR.

Minimal residual cone-beam reconstruction with attenuation correction in SPECT

International Nuclear Information System (INIS)

La, Valerie; Grangeat, Pierre

1998-01-01

This paper presents an iterative method based on the minimal residual algorithm for tomographic attenuation compensated reconstruction from attenuated cone-beam projections given the attenuation distribution. Unlike conjugate-gradient based reconstruction techniques, the proposed minimal residual based algorithm solves directly a quasisymmetric linear system, which is a preconditioned system. Thus it avoids the use of normal equations, which improves the convergence rate. Two main contributions are introduced. First, a regularization method is derived for quasisymmetric problems, based on a Tikhonov-Phillips regularization applied to the factorization of the symmetric part of the system matrix. This regularization is made spatially adaptive to avoid smoothing the region of interest. Second, our existing reconstruction algorithm for attenuation correction in parallel-beam geometry is extended to cone-beam geometry. A circular orbit is considered. Two preconditioning operators are proposed: the first one is Grangeat's inversion formula and the second one is Feldkamp's inversion formula. Experimental results obtained on simulated data are presented and the shadow zone effect on attenuated data is illustrated. (author)

Computed tomography of the chest with model-based iterative reconstruction using a radiation exposure similar to chest X-ray examination: preliminary observations

Energy Technology Data Exchange (ETDEWEB)

Neroladaki, Angeliki; Botsikas, Diomidis; Boudabbous, Sana; Becker, Christoph D.; Montet, Xavier [Geneva University Hospital, Department of Radiology, Geneva 4 (Switzerland)

2013-02-15

The purpose of this study was to assess the diagnostic image quality of ultra-low-dose chest computed tomography (ULD-CT) obtained with a radiation dose comparable to chest radiography and reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) in comparison with standard dose diagnostic CT (SDD-CT) or low-dose diagnostic CT (LDD-CT) reconstructed with FBP alone. Unenhanced chest CT images of 42 patients acquired with ULD-CT were compared with images obtained with SDD-CT or LDD-CT in the same examination. Noise measurements and image quality, based on conspicuity of chest lesions on all CT data sets were assessed on a five-point scale. The radiation dose of ULD-CT was 0.16 {+-} 0.006 mSv compared with 11.2 {+-} 2.7 mSv for SDD-CT (P < 0.0001) and 2.7 {+-} 0.9 mSv for LDD-CT. Image quality of ULD-CT increased significantly when using MBIR compared with FBP or ASIR (P < 0.001). ULD-CT reconstructed with MBIR enabled to detect as many non-calcified pulmonary nodules as seen on SDD-CT or LDD-CT. However, image quality of ULD-CT was clearly inferior for characterisation of ground glass opacities or emphysema. Model-based iterative reconstruction allows detection of pulmonary nodules with ULD-CT with radiation exposure in the range of a posterior to anterior (PA) and lateral chest X-ray. (orig.)

Adaptive algebraic reconstruction technique

International Nuclear Information System (INIS)

Lu Wenkai; Yin Fangfang

2004-01-01

Algebraic reconstruction techniques (ART) are iterative procedures for reconstructing objects from their projections. It is proven that ART can be computationally efficient by carefully arranging the order in which the collected data are accessed during the reconstruction procedure and adaptively adjusting the relaxation parameters. In this paper, an adaptive algebraic reconstruction technique (AART), which adopts the same projection access scheme in multilevel scheme algebraic reconstruction technique (MLS-ART), is proposed. By introducing adaptive adjustment of the relaxation parameters during the reconstruction procedure, one-iteration AART can produce reconstructions with better quality, in comparison with one-iteration MLS-ART. Furthermore, AART outperforms MLS-ART with improved computational efficiency

Ultralow dose computed tomography attenuation correction for pediatric PET CT using adaptive statistical iterative reconstruction

International Nuclear Information System (INIS)

Brady, Samuel L.; Shulkin, Barry L.

2015-01-01

Purpose: To develop ultralow dose computed tomography (CT) attenuation correction (CTAC) acquisition protocols for pediatric positron emission tomography CT (PET CT). Methods: A GE Discovery 690 PET CT hybrid scanner was used to investigate the change to quantitative PET and CT measurements when operated at ultralow doses (10–35 mA s). CT quantitation: noise, low-contrast resolution, and CT numbers for 11 tissue substitutes were analyzed in-phantom. CT quantitation was analyzed to a reduction of 90% volume computed tomography dose index (0.39/3.64; mGy) from baseline. To minimize noise infiltration, 100% adaptive statistical iterative reconstruction (ASiR) was used for CT reconstruction. PET images were reconstructed with the lower-dose CTAC iterations and analyzed for: maximum body weight standardized uptake value (SUV bw ) of various diameter targets (range 8–37 mm), background uniformity, and spatial resolution. Radiation dose and CTAC noise magnitude were compared for 140 patient examinations (76 post-ASiR implementation) to determine relative dose reduction and noise control. Results: CT numbers were constant to within 10% from the nondose reduced CTAC image for 90% dose reduction. No change in SUV bw , background percent uniformity, or spatial resolution for PET images reconstructed with CTAC protocols was found down to 90% dose reduction. Patient population effective dose analysis demonstrated relative CTAC dose reductions between 62% and 86% (3.2/8.3–0.9/6.2). Noise magnitude in dose-reduced patient images increased but was not statistically different from predose-reduced patient images. Conclusions: Using ASiR allowed for aggressive reduction in CT dose with no change in PET reconstructed images while maintaining sufficient image quality for colocalization of hybrid CT anatomy and PET radioisotope uptake

Ultralow dose computed tomography attenuation correction for pediatric PET CT using adaptive statistical iterative reconstruction

Energy Technology Data Exchange (ETDEWEB)

Brady, Samuel L., E-mail: samuel.brady@stjude.org [Division of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105 (United States); Shulkin, Barry L. [Nuclear Medicine and Department of Radiological Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105 (United States)

2015-02-15

Purpose: To develop ultralow dose computed tomography (CT) attenuation correction (CTAC) acquisition protocols for pediatric positron emission tomography CT (PET CT). Methods: A GE Discovery 690 PET CT hybrid scanner was used to investigate the change to quantitative PET and CT measurements when operated at ultralow doses (10–35 mA s). CT quantitation: noise, low-contrast resolution, and CT numbers for 11 tissue substitutes were analyzed in-phantom. CT quantitation was analyzed to a reduction of 90% volume computed tomography dose index (0.39/3.64; mGy) from baseline. To minimize noise infiltration, 100% adaptive statistical iterative reconstruction (ASiR) was used for CT reconstruction. PET images were reconstructed with the lower-dose CTAC iterations and analyzed for: maximum body weight standardized uptake value (SUV{sub bw}) of various diameter targets (range 8–37 mm), background uniformity, and spatial resolution. Radiation dose and CTAC noise magnitude were compared for 140 patient examinations (76 post-ASiR implementation) to determine relative dose reduction and noise control. Results: CT numbers were constant to within 10% from the nondose reduced CTAC image for 90% dose reduction. No change in SUV{sub bw}, background percent uniformity, or spatial resolution for PET images reconstructed with CTAC protocols was found down to 90% dose reduction. Patient population effective dose analysis demonstrated relative CTAC dose reductions between 62% and 86% (3.2/8.3–0.9/6.2). Noise magnitude in dose-reduced patient images increased but was not statistically different from predose-reduced patient images. Conclusions: Using ASiR allowed for aggressive reduction in CT dose with no change in PET reconstructed images while maintaining sufficient image quality for colocalization of hybrid CT anatomy and PET radioisotope uptake.

Image quality at low tube voltage (70 kV) and sinogram-affirmed iterative reconstruction for computed tomography in infants with congenital heart disease

International Nuclear Information System (INIS)

Nakagawa, Motoo; Ozawa, Yoshiyuki; Sakurai, Keita; Shimohira, Masashi; Shibamoto, Yuta; Ohashi, Kazuya; Asano, Miki; Yamaguchi, Sachiko

2015-01-01

Lower tube voltage has advantages for CT angiography, such as improved contrast To evaluate the image quality of low-voltage (70 kV) CT for congenital heart disease and the ability of sinogram-affirmed iterative reconstruction to improve image quality. Forty-six children with congenital heart disease (median age: 109 days) were examined using dual-source CT. Scans were performed at 80 kV and 70 kV in 21 and 25 children, respectively. A nonionic iodinated contrast medium (300 mg I/ml) was used for the 80-kV protocol. The contrast medium was diluted to 75% (225 mgI/mL) with saline for the 70-kV protocol. Image noise was measured in the two protocols for each group by extracting the standard deviations of a region of interest placed on the descending aorta. We then determined whether sinogram-affirmed iterative reconstruction reduced the image noise at 70 kV. There was more noise at 70 kV than at 80 kV (29 ± 12 vs 20 ± 4.8; P < 0.01). Sinogram-affirmed iterative reconstruction with grade 4 strength settings improved the noise (20 ± 5.9; P < 0.01) for the 70-kV group. Sinogram-affirmed iterative reconstruction improved the image quality of CT in congenital heart disease. (orig.)

Image quality at low tube voltage (70 kV) and sinogram-affirmed iterative reconstruction for computed tomography in infants with congenital heart disease

Energy Technology Data Exchange (ETDEWEB)

Nakagawa, Motoo; Ozawa, Yoshiyuki; Sakurai, Keita; Shimohira, Masashi; Shibamoto, Yuta [Nagoya City University Graduate School of Medical Sciences, Department of Radiology, Nagoya (Japan); Ohashi, Kazuya [Nagoya City University Hospital, Division of Central Radiology, Nagoya (Japan); Asano, Miki [Nagoya City University Graduate School of Medical Sciences, Department of Cardiovascular Surgery, Nagoya (Japan); Yamaguchi, Sachiko [Nagoya City University Graduate School of Medical Sciences, Department of Pediatrics and Neonatology, Nagoya (Japan)

2015-09-15

Lower tube voltage has advantages for CT angiography, such as improved contrast To evaluate the image quality of low-voltage (70 kV) CT for congenital heart disease and the ability of sinogram-affirmed iterative reconstruction to improve image quality. Forty-six children with congenital heart disease (median age: 109 days) were examined using dual-source CT. Scans were performed at 80 kV and 70 kV in 21 and 25 children, respectively. A nonionic iodinated contrast medium (300 mg I/ml) was used for the 80-kV protocol. The contrast medium was diluted to 75% (225 mgI/mL) with saline for the 70-kV protocol. Image noise was measured in the two protocols for each group by extracting the standard deviations of a region of interest placed on the descending aorta. We then determined whether sinogram-affirmed iterative reconstruction reduced the image noise at 70 kV. There was more noise at 70 kV than at 80 kV (29 ± 12 vs 20 ± 4.8; P < 0.01). Sinogram-affirmed iterative reconstruction with grade 4 strength settings improved the noise (20 ± 5.9; P < 0.01) for the 70-kV group. Sinogram-affirmed iterative reconstruction improved the image quality of CT in congenital heart disease. (orig.)

Bayesian image reconstruction for improving detection performance of muon tomography.

Science.gov (United States)

Wang, Guobao; Schultz, Larry J; Qi, Jinyi

2009-05-01

Muon tomography is a novel technology that is being developed for detecting high-Z materials in vehicles or cargo containers. Maximum likelihood methods have been developed for reconstructing the scattering density image from muon measurements. However, the instability of maximum likelihood estimation often results in noisy images and low detectability of high-Z targets. In this paper, we propose using regularization to improve the image quality of muon tomography. We formulate the muon reconstruction problem in a Bayesian framework by introducing a prior distribution on scattering density images. An iterative shrinkage algorithm is derived to maximize the log posterior distribution. At each iteration, the algorithm obtains the maximum a posteriori update by shrinking an unregularized maximum likelihood update. Inverse quadratic shrinkage functions are derived for generalized Laplacian priors and inverse cubic shrinkage functions are derived for generalized Gaussian priors. Receiver operating characteristic studies using simulated data demonstrate that the Bayesian reconstruction can greatly improve the detection performance of muon tomography.

An Lq–Lp optimization framework for image reconstruction of electrical resistance tomography

International Nuclear Information System (INIS)

Zhao, Jia; Xu, Yanbin; Dong, Feng

2014-01-01

Image reconstruction in electrical resistance tomography (ERT) is an ill-posed and nonlinear problem, which is easily affected by measurement noise. The regularization method with L 2 constraint term or L 1 constraint term is often used to solve the inverse problem of ERT. It shows that the reconstruction method with L 2 regularization puts smoothness to obtain stability in the image reconstruction process, which is blurry at the interface of different conductivities. The regularization method with L 1 norm is powerful at dealing with the over-smoothing effects, which is beneficial in obtaining a sharp transaction in conductivity distribution. To find the reason for these effects, an L q –L p optimization framework (1 ⩽ q ⩽ 2, 1 ⩽ p ⩽ 2) for the image reconstruction of ERT is presented in this paper. The L q –L p optimization framework is solved based on an approximation handling with Gauss–Newton iteration algorithm. The optimization framework is tested for image reconstruction of ERT with different models and the effects of the L p regularization term on the quality of the reconstructed images are discussed with both simulation and experiment. By comparing the reconstructed results with different p in the regularization term, it is found that a large penalty is implemented on small data in the solution when p is small and a lesser penalty is implemented on small data in the solution when p is larger. It also makes the reconstructed images smoother and more easily affected by noise when p is larger. (paper)

Submillisievert coronary calcium quantification using model-based iterative reconstruction: A within-patient analysis

Energy Technology Data Exchange (ETDEWEB)

Harder, Annemarie M. den, E-mail: a.m.denharder@umcutrecht.nl [Department of Radiology, University Medical Center Utrecht, Utrecht (Netherlands); Wolterink, Jelmer M. [Image Sciences Institute, University Medical Center Utrecht, Utrecht (Netherlands); Willemink, Martin J.; Schilham, Arnold M.R.; Jong, Pim A. de [Department of Radiology, University Medical Center Utrecht, Utrecht (Netherlands); Budde, Ricardo P.J. [Department of Radiology, Erasmus Medical Center, Rotterdam (Netherlands); Nathoe, Hendrik M. [Department of Cardiology, University Medical Center Utrecht, Utrecht (Netherlands); Išgum, Ivana [Image Sciences Institute, University Medical Center Utrecht, Utrecht (Netherlands); Leiner, Tim [Department of Radiology, University Medical Center Utrecht, Utrecht (Netherlands)

2016-11-15

Highlights: • Iterative reconstruction (IR) allows for low dose coronary calcium scoring (CCS). • Radiation dose can be safely reduced to 0.4 mSv with hybrid and model-based IR. • FBP is not feasible at these dose levels due to excessive noise. - Abstract: Purpose: To determine the effect of model-based iterative reconstruction (IR) on coronary calcium quantification using different submillisievert CT acquisition protocols. Methods: Twenty-eight patients received a clinically indicated non contrast-enhanced cardiac CT. After the routine dose acquisition, low-dose acquisitions were performed with 60%, 40% and 20% of the routine dose mAs. Images were reconstructed with filtered back projection (FBP), hybrid IR (HIR) and model-based IR (MIR) and Agatston scores, calcium volumes and calcium mass scores were determined. Results: Effective dose was 0.9, 0.5, 0.4 and 0.2 mSv, respectively. At 0.5 and 0.4 mSv, differences in Agatston scores with both HIR and MIR compared to FBP at routine dose were small (−0.1 to −2.9%), while at 0.2 mSv, differences in Agatston scores of −12.6 to −14.6% occurred. Reclassification of risk category at reduced dose levels was more frequent with MIR (21–25%) than with HIR (18%). Conclusions: Radiation dose for coronary calcium scoring can be safely reduced to 0.4 mSv using both HIR and MIR, while FBP is not feasible at these dose levels due to excessive noise. Further dose reduction can lead to an underestimation in Agatston score and subsequent reclassification to lower risk categories. Mass scores were unaffected by dose reductions.

MO-FG-204-04: How Iterative Reconstruction Algorithms Affect the NPS of CT Images

International Nuclear Information System (INIS)

Li, G; Liu, X; Dodge, C; Jensen, C; Rong, J

2015-01-01

Purpose: To evaluate how the third generation model based iterative reconstruction (MBIR) compares with filtered back-projection (FBP), adaptive statistical iterative reconstruction (ASiR), and the second generation MBIR based on noise power spectrum (NPS) analysis over a wide range of clinically applicable dose levels. Methods: The Catphan 600 CTP515 module, surrounded by an oval, fat-equivalent ring to mimic patient size/shape, was scanned on a GE HD750 CT scanner at 1, 2, 3, 6, 12 and 19mGy CTDIvol levels with typical patient scan parameters: 120kVp, 0.8s, 40mm beam width, large SFOV, 0.984 pitch and reconstructed thickness 2.5mm (VEO3.0: Abd/Pelvis with Texture and NR05). At each CTDIvol level, 10 repeated scans were acquired for achieving sufficient data sampling. The images were reconstructed using Standard kernel with FBP; 20%, 40% and 70% ASiR; and two versions of MBIR (VEO2.0 and 3.0). For evaluating the effect of the ROI spatial location to the Result of NPS, 4 ROI groups were categorized based on their distances from the center of the phantom. Results: VEO3.0 performed inferiorly comparing to VEO2.0 over all dose levels. On the other hand, at low dose levels (less than 3 mGy), it clearly outperformed ASiR and FBP, in NPS values. Therefore, the lower the dose level, the relative performance of MBIR improves. However, the shapes of the NPS show substantial differences in horizontal and vertical sampling dimensions. These differences may determine the characteristics of the noise/texture features in images, and hence, play an important role in image interpretation. Conclusion: The third generation MBIR did not improve over the second generation MBIR in term of NPS analysis. The overall performance of both versions of MBIR improved as compared to other reconstruction algorithms when dose was reduced. The shapes of the NPS curves provided additional value for future characterization of the image noise/texture features

MO-FG-204-04: How Iterative Reconstruction Algorithms Affect the NPS of CT Images

Energy Technology Data Exchange (ETDEWEB)

Li, G; Liu, X; Dodge, C; Jensen, C; Rong, J [UT MD Anderson Cancer Center, Houston, TX (United States)

2015-06-15

Purpose: To evaluate how the third generation model based iterative reconstruction (MBIR) compares with filtered back-projection (FBP), adaptive statistical iterative reconstruction (ASiR), and the second generation MBIR based on noise power spectrum (NPS) analysis over a wide range of clinically applicable dose levels. Methods: The Catphan 600 CTP515 module, surrounded by an oval, fat-equivalent ring to mimic patient size/shape, was scanned on a GE HD750 CT scanner at 1, 2, 3, 6, 12 and 19mGy CTDIvol levels with typical patient scan parameters: 120kVp, 0.8s, 40mm beam width, large SFOV, 0.984 pitch and reconstructed thickness 2.5mm (VEO3.0: Abd/Pelvis with Texture and NR05). At each CTDIvol level, 10 repeated scans were acquired for achieving sufficient data sampling. The images were reconstructed using Standard kernel with FBP; 20%, 40% and 70% ASiR; and two versions of MBIR (VEO2.0 and 3.0). For evaluating the effect of the ROI spatial location to the Result of NPS, 4 ROI groups were categorized based on their distances from the center of the phantom. Results: VEO3.0 performed inferiorly comparing to VEO2.0 over all dose levels. On the other hand, at low dose levels (less than 3 mGy), it clearly outperformed ASiR and FBP, in NPS values. Therefore, the lower the dose level, the relative performance of MBIR improves. However, the shapes of the NPS show substantial differences in horizontal and vertical sampling dimensions. These differences may determine the characteristics of the noise/texture features in images, and hence, play an important role in image interpretation. Conclusion: The third generation MBIR did not improve over the second generation MBIR in term of NPS analysis. The overall performance of both versions of MBIR improved as compared to other reconstruction algorithms when dose was reduced. The shapes of the NPS curves provided additional value for future characterization of the image noise/texture features.

Upgrade to iterative image reconstruction (IR) in abdominal MDCT imaging. A clinical study for detailed parameter optimization beyond vendor recommendations using the adaptive statistical iterative reconstruction environment (ASIR)

International Nuclear Information System (INIS)

Mueck, F.G.; Koerner, M.; Scherr, M.K.; Geyer, L.L.; Deak, Z.; Linsenmaier, U.; Reiser, M.; Wirth, S.

2012-01-01

To compare the image quality of dose-reduced 64-row abdominal CT reconstructed at different levels of adaptive statistical iterative reconstruction (ASIR) to full-dose baseline examinations reconstructed with filtered back-projection (FBP) in a clinical setting and upgrade situation. Abdominal baseline examinations (noise index NI = 29; LightSpeed VCT XT, GE) were intra-individually compared to follow-up studies on a CT with an ASIR option (NI = 43; Discovery HD750, GE), n = 42. Standard-kernel images were calculated with ASIR blendings of 0 - 100 % in slice and volume mode, respectively. Three experienced radiologists compared the image quality of these 567 sets to their corresponding full-dose baseline examination (-2: diagnostically inferior, -1: inferior, 0: equal, +1: superior, +2: diagnostically superior). Furthermore, a phantom was scanned. Statistical analysis used the Wilcoxon - the Mann-Whitney U-test and the intra-class correlation (ICC). The mean CTDIvol decreased from 19.7 ± 5.5 to 12.2 ± 4.7 mGy (p 0.10). Volume mode performed 73 % slower than slice mode (p < 0.01). After the system upgrade, the vendor recommendation of ASIR 50 % in slice mode allowed for a dose reduction of 38 % in abdominal CT with comparable image quality and time expenditure. However, there is still further dose reduction potential for more complex reconstruction settings. (orig.)

Efficient operator splitting algorithm for joint sparsity-regularized SPIRiT-based parallel MR imaging reconstruction.

Science.gov (United States)

Duan, Jizhong; Liu, Yu; Jing, Peiguang

2018-02-01

Self-consistent parallel imaging (SPIRiT) is an auto-calibrating model for the reconstruction of parallel magnetic resonance imaging, which can be formulated as a regularized SPIRiT problem. The Projection Over Convex Sets (POCS) method was used to solve the formulated regularized SPIRiT problem. However, the quality of the reconstructed image still needs to be improved. Though methods such as NonLinear Conjugate Gradients (NLCG) can achieve higher spatial resolution, these methods always demand very complex computation and converge slowly. In this paper, we propose a new algorithm to solve the formulated Cartesian SPIRiT problem with the JTV and JL1 regularization terms. The proposed algorithm uses the operator splitting (OS) technique to decompose the problem into a gradient problem and a denoising problem with two regularization terms, which is solved by our proposed split Bregman based denoising algorithm, and adopts the Barzilai and Borwein method to update step size. Simulation experiments on two in vivo data sets demonstrate that the proposed algorithm is 1.3 times faster than ADMM for datasets with 8 channels. Especially, our proposal is 2 times faster than ADMM for the dataset with 32 channels. Copyright © 2017 Elsevier Inc. All rights reserved.

Cardiac CT for planning redo cardiac surgery: effect of knowledge-based iterative model reconstruction on image quality

International Nuclear Information System (INIS)

Oda, Seitaro; Weissman, Gaby; Weigold, W. Guy; Vembar, Mani

2015-01-01

The purpose of this study was to investigate the effects of knowledge-based iterative model reconstruction (IMR) on image quality in cardiac CT performed for the planning of redo cardiac surgery by comparing IMR images with images reconstructed with filtered back-projection (FBP) and hybrid iterative reconstruction (HIR). We studied 31 patients (23 men, 8 women; mean age 65.1 ± 16.5 years) referred for redo cardiac surgery who underwent cardiac CT. Paired image sets were created using three types of reconstruction: FBP, HIR, and IMR. Quantitative parameters including CT attenuation, image noise, and contrast-to-noise ratio (CNR) of each cardiovascular structure were calculated. The visual image quality - graininess, streak artefact, margin sharpness of each cardiovascular structure, and overall image quality - was scored on a five-point scale. The mean image noise of FBP, HIR, and IMR images was 58.3 ± 26.7, 36.0 ± 12.5, and 14.2 ± 5.5 HU, respectively; there were significant differences in all comparison combinations among the three methods. The CNR of IMR images was better than that of FBP and HIR images in all evaluated structures. The visual scores were significantly higher for IMR than for the other images in all evaluated parameters. IMR can provide significantly improved qualitative and quantitative image quality at in cardiac CT for planning of reoperative cardiac surgery. (orig.)

Influence of dose reduction and iterative reconstruction on CT calcium scores : a multi-manufacturer dynamic phantom study

NARCIS (Netherlands)

van der Werf, N R; Willemink, M J; Willems, T P; Greuter, M J W; Leiner, T

To evaluate the influence of dose reduction in combination with iterative reconstruction (IR) on coronary calcium scores (CCS) in a dynamic phantom on state-of-the-art CT systems from different manufacturers. Calcified inserts in an anthropomorphic chest phantom were translated at 20 mm/s

Algebraic reconstruction techniques for spectral reconstruction in diffuse optical tomography

International Nuclear Information System (INIS)

Brendel, Bernhard; Ziegler, Ronny; Nielsen, Tim

2008-01-01

Reconstruction in diffuse optical tomography (DOT) necessitates solving the diffusion equation, which is nonlinear with respect to the parameters that have to be reconstructed. Currently applied solving methods are based on the linearization of the equation. For spectral three-dimensional reconstruction, the emerging equation system is too large for direct inversion, but the application of iterative methods is feasible. Computational effort and speed of convergence of these iterative methods are crucial since they determine the computation time of the reconstruction. In this paper, the iterative methods algebraic reconstruction technique (ART) and conjugated gradients (CGs) as well as a new modified ART method are investigated for spectral DOT reconstruction. The aim of the modified ART scheme is to speed up the convergence by considering the specific conditions of spectral reconstruction. As a result, it converges much faster to favorable results than conventional ART and CG methods

Emergency assessment of patients with acute abdominal pain using low-dose CT with iterative reconstruction: a comparative study

Energy Technology Data Exchange (ETDEWEB)

Poletti, Pierre-Alexandre; Becker, Minerva; Becker, Christoph D.; Zaidi, Habib; Platon, Alexandra [University Hospital of Geneva, Department of Radiology, Geneva (Switzerland); Halfon Poletti, Alice; Rutschmann, Olivier T. [University Hospital of Geneva, Department of Community, Primary Care and Emergency Medicine, Geneva (Switzerland); Perneger, Thomas [University Hospital of Geneva, Division of Clinical Epidemiology, Geneva (Switzerland)

2017-08-15

To determine if radiation dose delivered by contrast-enhanced CT (CECT) for acute abdominal pain can be reduced to the dose administered in abdominal radiography (<2.5 mSv) using low-dose CT (LDCT) with iterative reconstruction algorithms. One hundred and fifty-one consecutive patients requiring CECT for acute abdominal pain were included, and their body mass index (BMI) was calculated. CECT was immediately followed by LDCT. LDCT series was processed using 1) 40% iterative reconstruction algorithm blended with filtered back projection (LDCT-IR-FBP) and 2) model-based iterative reconstruction algorithm (LDCT-MBIR). LDCT-IR-FBP and LDCT-MBIR images were reviewed independently by two board-certified radiologists (Raters 1 and 2). Abdominal pathology was revealed on CECT in 120 (79%) patients. In those with BMI <30, accuracies for correct diagnosis by Rater 1 with LDCT-IR-FBP and LDCT-MBIR, when compared to CECT, were 95.4% (104/109) and 99% (108/109), respectively, and 92.7% (101/109) and 100% (109/109) for Rater 2. In patients with BMI ≥30, accuracies with LDCT-IR-FBP and LDCT-MBIR were 88.1% (37/42) and 90.5% (38/42) for Rater 1 and 78.6% (33/42) and 92.9% (39/42) for Rater 2. The radiation dose delivered by CT to non-obese patients with acute abdominal pain can be safely reduced to levels close to standard radiography using LDCT-MBIR. (orig.)

Degree Associated Edge Reconstruction Number of Graphs with Regular Pruned Graph

Directory of Open Access Journals (Sweden)

P. Anusha Devi

2015-10-01

Full Text Available An ecard of a graph $G$ is a subgraph formed by deleting an edge. A da-ecard specifies the degree of the deleted edge along with the ecard. The degree associated edge reconstruction number of a graph $G,~dern(G,$ is the minimum number of da-ecards that uniquely determines $G.$  The adversary degree associated edge reconstruction number of a graph $G, adern(G,$ is the minimum number $k$ such that every collection of $k$ da-ecards of $G$ uniquely determines $G.$ The maximal subgraph without end vertices of a graph $G$ which is not a tree is the pruned graph of $G.$ It is shown that $dern$ of complete multipartite graphs and some connected graphs with regular pruned graph is $1$ or $2.$ We also determine $dern$ and $adern$ of corona product of standard graphs.

Adaptive multiresolution method for MAP reconstruction in electron tomography

Energy Technology Data Exchange (ETDEWEB)

Acar, Erman, E-mail: erman.acar@tut.fi [Department of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere (Finland); BioMediTech, Tampere University of Technology, Biokatu 10, 33520 Tampere (Finland); Peltonen, Sari; Ruotsalainen, Ulla [Department of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere (Finland); BioMediTech, Tampere University of Technology, Biokatu 10, 33520 Tampere (Finland)

2016-11-15

3D image reconstruction with electron tomography holds problems due to the severely limited range of projection angles and low signal to noise ratio of the acquired projection images. The maximum a posteriori (MAP) reconstruction methods have been successful in compensating for the missing information and suppressing noise with their intrinsic regularization techniques. There are two major problems in MAP reconstruction methods: (1) selection of the regularization parameter that controls the balance between the data fidelity and the prior information, and (2) long computation time. One aim of this study is to provide an adaptive solution to the regularization parameter selection problem without having additional knowledge about the imaging environment and the sample. The other aim is to realize the reconstruction using sequences of resolution levels to shorten the computation time. The reconstructions were analyzed in terms of accuracy and computational efficiency using a simulated biological phantom and publically available experimental datasets of electron tomography. The numerical and visual evaluations of the experiments show that the adaptive multiresolution method can provide more accurate results than the weighted back projection (WBP), simultaneous iterative reconstruction technique (SIRT), and sequential MAP expectation maximization (sMAPEM) method. The method is superior to sMAPEM also in terms of computation time and usability since it can reconstruct 3D images significantly faster without requiring any parameter to be set by the user. - Highlights: • An adaptive multiresolution reconstruction method is introduced for electron tomography. • The method provides more accurate results than the conventional reconstruction methods. • The missing wedge and noise problems can be compensated by the method efficiently.

Iterative reconstruction of SiPM light response functions in a square-shaped compact gamma camera

Science.gov (United States)

Morozov, A.; Alves, F.; Marcos, J.; Martins, R.; Pereira, L.; Solovov, V.; Chepel, V.

2017-05-01

Compact gamma cameras with a square-shaped monolithic scintillator crystal and an array of silicon photomultipliers (SiPMs) are actively being developed for applications in areas such as small animal imaging, cancer diagnostics and radiotracer guided surgery. Statistical methods of position reconstruction, which are potentially superior to the traditional centroid method, require accurate knowledge of the spatial response of each photomultiplier. Using both Monte Carlo simulations and experimental data obtained with a camera prototype, we show that the spatial response of all photomultipliers (light response functions) can be parameterized with axially symmetric functions obtained iteratively from flood field irradiation data. The study was performed with a camera prototype equipped with a 30  ×  30  ×  2 mm3 LYSO crystal and an 8  ×  8 array of SiPMs for 140 keV gamma rays. The simulations demonstrate that the images, reconstructed with the maximum likelihood method using the response obtained with the iterative approach, exhibit only minor distortions: the average difference between the reconstructed and the true positions in X and Y directions does not exceed 0.2 mm in the central area of 22  ×  22 mm2 and 0.4 mm at the periphery of the camera. A similar level of image distortions is shown experimentally with the camera prototype.

Ultralow dose dentomaxillofacial CT imaging and iterative reconstruction techniques: variability of Hounsfield units and contrast-to-noise ratio

Science.gov (United States)

Bischel, Alexander; Stratis, Andreas; Kakar, Apoorv; Bosmans, Hilde; Jacobs, Reinhilde; Gassner, Eva-Maria; Puelacher, Wolfgang; Pauwels, Ruben

2016-01-01

Objective: The aim of this study was to evaluate whether application of ultralow dose protocols and iterative reconstruction technology (IRT) influence quantitative Hounsfield units (HUs) and contrast-to-noise ratio (CNR) in dentomaxillofacial CT imaging. Methods: A phantom with inserts of five types of materials was scanned using protocols for (a) a clinical reference for navigated surgery (CT dose index volume 36.58 mGy), (b) low-dose sinus imaging (18.28 mGy) and (c) four ultralow dose imaging (4.14, 2.63, 0.99 and 0.53 mGy). All images were reconstructed using: (i) filtered back projection (FBP); (ii) IRT: adaptive statistical iterative reconstruction-50 (ASIR-50), ASIR-100 and model-based iterative reconstruction (MBIR); and (iii) standard (std) and bone kernel. Mean HU, CNR and average HU error after recalibration were determined. Each combination of protocols was compared using Friedman analysis of variance, followed by Dunn's multiple comparison test. Results: Pearson's sample correlation coefficients were all >0.99. Ultralow dose protocols using FBP showed errors of up to 273 HU. Std kernels had less HU variability than bone kernels. MBIR reduced the error value for the lowest dose protocol to 138 HU and retained the highest relative CNR. ASIR could not demonstrate significant advantages over FBP. Conclusions: Considering a potential dose reduction as low as 1.5% of a std protocol, ultralow dose protocols and IRT should be further tested for clinical dentomaxillofacial CT imaging. Advances in knowledge: HU as a surrogate for bone density may vary significantly in CT ultralow dose imaging. However, use of std kernels and MBIR technology reduce HU error values and may retain the highest CNR. PMID:26859336

Iterative reconstruction with boundary detection for carbon ion computed tomography

Science.gov (United States)

Shrestha, Deepak; Qin, Nan; Zhang, You; Kalantari, Faraz; Niu, Shanzhou; Jia, Xun; Pompos, Arnold; Jiang, Steve; Wang, Jing

2018-03-01

In heavy ion radiation therapy, improving the accuracy in range prediction of the ions inside the patient’s body has become essential. Accurate localization of the Bragg peak provides greater conformity of the tumor while sparing healthy tissues. We investigated the use of carbon ions directly for computed tomography (carbon CT) to create the relative stopping power map of a patient’s body. The Geant4 toolkit was used to perform a Monte Carlo simulation of the carbon ion trajectories, to study their lateral and angular deflections and the most likely paths, using a water phantom. Geant4 was used to create carbonCT projections of a contrast and spatial resolution phantom, with a cone beam of 430 MeV/u carbon ions. The contrast phantom consisted of cranial bone, lung material, and PMMA inserts while the spatial resolution phantom contained bone and lung material inserts with line pair (lp) densities ranging from 1.67 lp cm-1 through 5 lp cm-1. First, the positions of each carbon ion on the rear and front trackers were used for an approximate reconstruction of the phantom. The phantom boundary was extracted from this approximate reconstruction, by using the position as well as angle information from the four tracking detectors, resulting in the entry and exit locations of the individual ions on the phantom surface. Subsequent reconstruction was performed by the iterative algebraic reconstruction technique coupled with total variation minimization (ART-TV) assuming straight line trajectories for the ions inside the phantom. The influence of number of projections was studied with reconstruction from five different sets of projections: 15, 30, 45, 60 and 90. Additionally, the effect of number of ions on the image quality was investigated by reducing the number of ions/projection while keeping the total number of projections at 60. An estimation of carbon ion range using the carbonCT image resulted in improved range prediction compared to the range calculated using a

Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: A phantom study.

Science.gov (United States)

Kim, Hyungjin; Park, Chang Min; Song, Yong Sub; Lee, Sang Min; Goo, Jin Mo

2014-05-01

To evaluate the influence of radiation dose settings and reconstruction algorithms on the measurement accuracy and reproducibility of semi-automated pulmonary nodule volumetry. CT scans were performed on a chest phantom containing various nodules (10 and 12mm; +100, -630 and -800HU) at 120kVp with tube current-time settings of 10, 20, 50, and 100mAs. Each CT was reconstructed using filtered back projection (FBP), iDose(4) and iterative model reconstruction (IMR). Semi-automated volumetry was performed by two radiologists using commercial volumetry software for nodules at each CT dataset. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. The absolute percentage measurement errors and differences were then calculated for volume and mass. The influence of radiation dose and reconstruction algorithm on measurement accuracy, reproducibility and objective image quality metrics was analyzed using generalized estimating equations. Measurement accuracy and reproducibility of nodule volume and mass were not significantly associated with CT radiation dose settings or reconstruction algorithms (p>0.05). Objective image quality metrics of CT images were superior in IMR than in FBP or iDose(4) at all radiation dose settings (pvolumetry can be applied to low- or ultralow-dose chest CT with usage of a novel iterative reconstruction algorithm without losing measurement accuracy and reproducibility. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Adaptive statistical iterative reconstruction reduces patient radiation dose in neuroradiology CT studies

Energy Technology Data Exchange (ETDEWEB)

Komlosi, Peter; Zhang, Yanrong; Leiva-Salinas, Carlos; Ornan, David; Grady, Deborah [University of Virginia, Department of Radiology and Medical Imaging, Division of Neuroradiology, PO Box 800170, Charlottesville, VA (United States); Patrie, James T.; Xin, Wenjun [University of Virginia, Department of Public Health Sciences, Charlottesville, VA (United States); Wintermark, Max [University of Virginia, Department of Radiology and Medical Imaging, Division of Neuroradiology, PO Box 800170, Charlottesville, VA (United States); Centre Hospitalier Universitaire Vaudois, Department of Radiology, Lausanne (Switzerland)

2014-03-15

Adaptive statistical iterative reconstruction (ASIR) can decrease image noise, thereby generating CT images of comparable diagnostic quality with less radiation. The purpose of this study is to quantify the effect of systematic use of ASIR versus filtered back projection (FBP) for neuroradiology CT protocols on patients' radiation dose and image quality. We evaluated the effect of ASIR on six types of neuroradiologic CT studies: adult and pediatric unenhanced head CT, adult cervical spine CT, adult cervical and intracranial CT angiography, adult soft tissue neck CT with contrast, and adult lumbar spine CT. For each type of CT study, two groups of 100 consecutive studies were retrospectively reviewed: 100 studies performed with FBP and 100 studies performed with ASIR/FBP blending factor of 40 %/60 % with appropriate noise indices. The weighted volume CT dose index (CTDI{sub vol}), dose-length product (DLP) and noise were recorded. Each study was also reviewed for image quality by two reviewers. Continuous and categorical variables were compared by t test and free permutation test, respectively. For adult unenhanced brain CT, CT cervical myelography, cervical and intracranial CT angiography and lumbar spine CT both CTDI{sub vol} and DLP were lowered by up to 10.9 % (p < 0.001), 17.9 % (p = 0.005), 20.9 % (p < 0.001), and 21.7 % (p = 0.001), respectively, by using ASIR compared with FBP alone. Image quality and noise were similar for both FBP and ASIR. We recommend routine use of iterative reconstruction for neuroradiology CT examinations because this approach affords a significant dose reduction while preserving image quality. (orig.)

Adaptive statistical iterative reconstruction reduces patient radiation dose in neuroradiology CT studies

International Nuclear Information System (INIS)

Komlosi, Peter; Zhang, Yanrong; Leiva-Salinas, Carlos; Ornan, David; Grady, Deborah; Patrie, James T.; Xin, Wenjun; Wintermark, Max

2014-01-01

Adaptive statistical iterative reconstruction (ASIR) can decrease image noise, thereby generating CT images of comparable diagnostic quality with less radiation. The purpose of this study is to quantify the effect of systematic use of ASIR versus filtered back projection (FBP) for neuroradiology CT protocols on patients' radiation dose and image quality. We evaluated the effect of ASIR on six types of neuroradiologic CT studies: adult and pediatric unenhanced head CT, adult cervical spine CT, adult cervical and intracranial CT angiography, adult soft tissue neck CT with contrast, and adult lumbar spine CT. For each type of CT study, two groups of 100 consecutive studies were retrospectively reviewed: 100 studies performed with FBP and 100 studies performed with ASIR/FBP blending factor of 40 %/60 % with appropriate noise indices. The weighted volume CT dose index (CTDI vol ), dose-length product (DLP) and noise were recorded. Each study was also reviewed for image quality by two reviewers. Continuous and categorical variables were compared by t test and free permutation test, respectively. For adult unenhanced brain CT, CT cervical myelography, cervical and intracranial CT angiography and lumbar spine CT both CTDI vol and DLP were lowered by up to 10.9 % (p < 0.001), 17.9 % (p = 0.005), 20.9 % (p < 0.001), and 21.7 % (p = 0.001), respectively, by using ASIR compared with FBP alone. Image quality and noise were similar for both FBP and ASIR. We recommend routine use of iterative reconstruction for neuroradiology CT examinations because this approach affords a significant dose reduction while preserving image quality. (orig.)

Acoustical source reconstruction from non-synchronous sequential measurements by Fast Iterative Shrinkage Thresholding Algorithm

Science.gov (United States)

Yu, Liang; Antoni, Jerome; Leclere, Quentin; Jiang, Weikang

2017-11-01

Acoustical source reconstruction is a typical inverse problem, whose minimum frequency of reconstruction hinges on the size of the array and maximum frequency depends on the spacing distance between the microphones. For the sake of enlarging the frequency of reconstruction and reducing the cost of an acquisition system, Cyclic Projection (CP), a method of sequential measurements without reference, was recently investigated (JSV,2016,372:31-49). In this paper, the Propagation based Fast Iterative Shrinkage Thresholding Algorithm (Propagation-FISTA) is introduced, which improves CP in two aspects: (1) the number of acoustic sources is no longer needed and the only making assumption is that of a "weakly sparse" eigenvalue spectrum; (2) the construction of the spatial basis is much easier and adaptive to practical scenarios of acoustical measurements benefiting from the introduction of propagation based spatial basis. The proposed Propagation-FISTA is first investigated with different simulations and experimental setups and is next illustrated with an industrial case.

Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA).

Science.gov (United States)

Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A

2015-02-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.

Iterative Multiview Side Information for Enhanced Reconstruction in Distributed Video Coding

Directory of Open Access Journals (Sweden)

2009-03-01

Full Text Available Distributed video coding (DVC is a new paradigm for video compression based on the information theoretical results of Slepian and Wolf (SW and Wyner and Ziv (WZ. DVC entails low-complexity encoders as well as separate encoding of correlated video sources. This is particularly attractive for multiview camera systems in video surveillance and camera sensor network applications, where low complexity is required at the encoder. In addition, the separate encoding of the sources implies no communication between the cameras in a practical scenario. This is an advantage since communication is time and power consuming and requires complex networking. In this work, different intercamera estimation techniques for side information (SI generation are explored and compared in terms of estimating quality, complexity, and rate distortion (RD performance. Further, a technique called iterative multiview side information (IMSI is introduced, where the final SI is used in an iterative reconstruction process. The simulation results show that IMSI significantly improves the RD performance for video with significant motion and activity. Furthermore, DVC outperforms AVC/H.264 Intra for video with average and low motion but it is still inferior to the Inter No Motion and Inter Motion modes.

Iterative image reconstruction algorithms in coronary CT angiography improve the detection of lipid-core plaque - a comparison with histology

International Nuclear Information System (INIS)

Puchner, Stefan B.; Ferencik, Maros; Maurovich-Horvat, Pal; Nakano, Masataka; Otsuka, Fumiyuki; Virmani, Renu; Kauczor, Hans-Ulrich; Hoffmann, Udo; Schlett, Christopher L.

2015-01-01

To evaluate whether iterative reconstruction algorithms improve the diagnostic accuracy of coronary CT angiography (CCTA) for detection of lipid-core plaque (LCP) compared to histology. CCTA and histological data were acquired from three ex vivo hearts. CCTA images were reconstructed using filtered back projection (FBP), adaptive-statistical (ASIR) and model-based (MBIR) iterative algorithms. Vessel cross-sections were co-registered between FBP/ASIR/MBIR and histology. Plaque area 2 : 5.78 ± 2.29 vs. 3.39 ± 1.68 FBP; 5.92 ± 1.87 vs. 3.43 ± 1.62 ASIR; 6.40 ± 1.55 vs. 3.49 ± 1.50 MBIR; all p < 0.0001). AUC for detecting LCP was 0.803/0.850/0.903 for FBP/ASIR/MBIR and was significantly higher for MBIR compared to FBP (p = 0.01). MBIR increased sensitivity for detection of LCP by CCTA. Plaque area <60 HU in CCTA was associated with LCP in histology regardless of the reconstruction algorithm. However, MBIR demonstrated higher accuracy for detecting LCP, which may improve vulnerable plaque detection by CCTA. (orig.)

Effect of radiation dose reduction and iterative reconstruction on computer-aided detection of pulmonary nodules : Intra-individual comparison

NARCIS (Netherlands)

Den Harder, Annemarie M.; Willemink, Martin J.; Van Hamersvelt, Robbert W.; Vonken, Evert-Jan P A; Milles, Julien; Schilham, Arnold M R; Lammers, Jan Willem; De Jong, Pim A.; Leiner, Tim; Budde, Ricardo P J

2016-01-01

Objective To evaluate the effect of radiation dose reduction and iterative reconstruction (IR) on the performance of computer-aided detection (CAD) for pulmonary nodules. Methods In this prospective study twenty-five patients were included who were scanned for pulmonary nodule follow-up. Image

Systematic Error in Lung Nodule Volumetry : Effect of Iterative Reconstruction Versus Filtered Back Projection at Different CT Parameters

NARCIS (Netherlands)

Willemink, Martin J.; Leiner, Tim; Budde, Ricardo P. J.; de Kort, Freek P. L.; Vliegenthart, Rozemarijn; van Ooijen, Peter M. A.; Oudkerk, Matthijs; de Jong, Pim A.

2012-01-01

OBJECTIVE. Iterative reconstruction potentially can reduce radiation dose compared with filtered back projection (FBP) for chest CT. This is especially important for repeated CT scanning, as is the case in patients with indeterminate lung nodules. It is currently unknown whether absolute nodule

Performance evaluation of iterative reconstruction algorithms for achieving CT radiation dose reduction — a phantom study

Science.gov (United States)

Dodge, Cristina T.; Tamm, Eric P.; Cody, Dianna D.; Liu, Xinming; Jensen, Corey T.; Wei, Wei; Kundra, Vikas

2016-01-01

The purpose of this study was to characterize image quality and dose performance with GE CT iterative reconstruction techniques, adaptive statistical iterative reconstruction (ASiR), and model‐based iterative reconstruction (MBIR), over a range of typical to low‐dose intervals using the Catphan 600 and the anthropomorphic Kyoto Kagaku abdomen phantoms. The scope of the project was to quantitatively describe the advantages and limitations of these approaches. The Catphan 600 phantom, supplemented with a fat‐equivalent oval ring, was scanned using a GE Discovery HD750 scanner at 120 kVp, 0.8 s rotation time, and pitch factors of 0.516, 0.984, and 1.375. The mA was selected for each pitch factor to achieve CTDIvol values of 24, 18, 12, 6, 3, 2, and 1 mGy. Images were reconstructed at 2.5 mm thickness with filtered back‐projection (FBP); 20%, 40%, and 70% ASiR; and MBIR. The potential for dose reduction and low‐contrast detectability were evaluated from noise and contrast‐to‐noise ratio (CNR) measurements in the CTP 404 module of the Catphan. Hounsfield units (HUs) of several materials were evaluated from the cylinder inserts in the CTP 404 module, and the modulation transfer function (MTF) was calculated from the air insert. The results were confirmed in the anthropomorphic Kyoto Kagaku abdomen phantom at 6, 3, 2, and 1 mGy. MBIR reduced noise levels five‐fold and increased CNR by a factor of five compared to FBP below 6 mGy CTDIvol, resulting in a substantial improvement in image quality. Compared to ASiR and FBP, HU in images reconstructed with MBIR were consistently lower, and this discrepancy was reversed by higher pitch factors in some materials. MBIR improved the conspicuity of the high‐contrast spatial resolution bar pattern, and MTF quantification confirmed the superior spatial resolution performance of MBIR versus FBP and ASiR at higher dose levels. While ASiR and FBP were relatively insensitive to changes in dose and pitch, the spatial

Improving image quality in Electrical Impedance Tomography (EIT using Projection Error Propagation-based Regularization (PEPR technique: A simulation study

Directory of Open Access Journals (Sweden)

Tushar Kanti Bera

2011-03-01

Full Text Available A Projection Error Propagation-based Regularization (PEPR method is proposed and the reconstructed image quality is improved in Electrical Impedance Tomography (EIT. A projection error is produced due to the misfit of the calculated and measured data in the reconstruction process. The variation of the projection error is integrated with response matrix in each iterations and the reconstruction is carried out in EIDORS. The PEPR method is studied with the simulated boundary data for different inhomogeneity geometries. Simulated results demonstrate that the PEPR technique improves image reconstruction precision in EIDORS and hence it can be successfully implemented to increase the reconstruction accuracy in EIT.>doi:10.5617/jeb.158 J Electr Bioimp, vol. 2, pp. 2-12, 2011

Electron paramagnetic resonance image reconstruction with total variation and curvelets regularization

Science.gov (United States)

Durand, Sylvain; Frapart, Yves-Michel; Kerebel, Maud

2017-11-01

Spatial electron paramagnetic resonance imaging (EPRI) is a recent method to localize and characterize free radicals in vivo or in vitro, leading to applications in material and biomedical sciences. To improve the quality of the reconstruction obtained by EPRI, a variational method is proposed to inverse the image formation model. It is based on a least-square data-fidelity term and the total variation and Besov seminorm for the regularization term. To fully comprehend the Besov seminorm, an implementation using the curvelet transform and the L 1 norm enforcing the sparsity is proposed. It allows our model to reconstruct both image where acquisition information are missing and image with details in textured areas, thus opening possibilities to reduce acquisition times. To implement the minimization problem using the algorithm developed by Chambolle and Pock, a thorough analysis of the direct model is undertaken and the latter is inverted while avoiding the use of filtered backprojection (FBP) and of non-uniform Fourier transform. Numerical experiments are carried out on simulated data, where the proposed model outperforms both visually and quantitatively the classical model using deconvolution and FBP. Improved reconstructions on real data, acquired on an irradiated distal phalanx, were successfully obtained.

Effects of pure and hybrid iterative reconstruction algorithms on high-resolution computed tomography in the evaluation of interstitial lung disease.

Science.gov (United States)

Katsura, Masaki; Sato, Jiro; Akahane, Masaaki; Mise, Yoko; Sumida, Kaoru; Abe, Osamu

2017-08-01

To compare image quality characteristics of high-resolution computed tomography (HRCT) in the evaluation of interstitial lung disease using three different reconstruction methods: model-based iterative reconstruction (MBIR), adaptive statistical iterative reconstruction (ASIR), and filtered back projection (FBP). Eighty-nine consecutive patients with interstitial lung disease underwent standard-of-care chest CT with 64-row multi-detector CT. HRCT images were reconstructed in 0.625-mm contiguous axial slices using FBP, ASIR, and MBIR. Two radiologists independently assessed the images in a blinded manner for subjective image noise, streak artifacts, and visualization of normal and pathologic structures. Objective image noise was measured in the lung parenchyma. Spatial resolution was assessed by measuring the modulation transfer function (MTF). MBIR offered significantly lower objective image noise (22.24±4.53, PASIR (39.76±7.41) and FBP (51.91±9.71). MTF (spatial resolution) was increased using MBIR compared with ASIR and FBP. MBIR showed improvements in visualization of normal and pathologic structures over ASIR and FBP, while ASIR was rated quite similarly to FBP. MBIR significantly improved subjective image noise (PASIR and FBP. Copyright © 2017 Elsevier B.V. All rights reserved.

Iterative Runge–Kutta-type methods for nonlinear ill-posed problems

International Nuclear Information System (INIS)

Böckmann, C; Pornsawad, P

2008-01-01

We present a regularization method for solving nonlinear ill-posed problems by applying the family of Runge–Kutta methods to an initial value problem, in particular, to the asymptotical regularization method. We prove that the developed iterative regularization method converges to a solution under certain conditions and with a general stopping rule. Some particular iterative regularization methods are numerically implemented. Numerical results of the examples show that the developed Runge–Kutta-type regularization methods yield stable solutions and that particular implicit methods are very efficient in saving iteration steps

Accelerated perturbation-resilient block-iterative projection methods with application to image reconstruction.

Science.gov (United States)

Nikazad, T; Davidi, R; Herman, G T

2012-03-01

We study the convergence of a class of accelerated perturbation-resilient block-iterative projection methods for solving systems of linear equations. We prove convergence to a fixed point of an operator even in the presence of summable perturbations of the iterates, irrespective of the consistency of the linear system. For a consistent system, the limit point is a solution of the system. In the inconsistent case, the symmetric version of our method converges to a weighted least squares solution. Perturbation resilience is utilized to approximate the minimum of a convex functional subject to the equations. A main contribution, as compared to previously published approaches to achieving similar aims, is a more than an order of magnitude speed-up, as demonstrated by applying the methods to problems of image reconstruction from projections. In addition, the accelerated algorithms are illustrated to be better, in a strict sense provided by the method of statistical hypothesis testing, than their unaccelerated versions for the task of detecting small tumors in the brain from X-ray CT projection data.

The use of adaptive statistical iterative reconstruction in pediatric head CT: a feasibility study.

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Vorona, G A; Zuccoli, G; Sutcavage, T; Clayton, B L; Ceschin, R C; Panigrahy, A

2013-01-01

Iterative reconstruction techniques facilitate CT dose reduction; though to our knowledge, no group has explored using iterative reconstruction with pediatric head CT. Our purpose was to perform a feasibility study to assess the use of ASIR in a small group of pediatric patients undergoing head CT. An Alderson-Rando head phantom was scanned at decreasing 10% mA intervals relative to our standard protocol, and each study was then reconstructed at 10% ASIR intervals. An intracranial region of interest was consistently placed to estimate noise. Our ventriculoperitoneal shunt CT protocol was subsequently modified, and patients were scanned at 20% ASIR with approximately 20% mA reductions. ASIR studies were anonymously compared with older non-ASIR studies from the same patients by 2 attending pediatric neuroradiologists for diagnostic utility, sharpness, noise, and artifacts. The phantom study demonstrated similar noise at 100% mA/0% ASIR (3.9) and 80% mA/20% ASIR (3.7). Twelve pediatric patients were scanned at reduced dose at 20% ASIR. The average CTDI(vol) and DLP values of the 20% ASIR studies were 22.4 mGy and 338.4 mGy-cm, and for the non-ASIR studies, they were 28.8 mGy and 444.5 mGy-cm, representing statistically significant decreases in the CTDI(vol) (22.1%, P = .00007) and DLP (23.9%, P = .0005) values. There were no significant differences between the ASIR studies and non-ASIR studies with respect to diagnostic acceptability, sharpness, noise, or artifacts. Our findings suggest that 20% ASIR can provide approximately 22% dose reduction in pediatric head CT without affecting image quality.

Intensity-based bayesian framework for image reconstruction from sparse projection data

International Nuclear Information System (INIS)

Rashed, E.A.; Kudo, Hiroyuki

2009-01-01

This paper presents a Bayesian framework for iterative image reconstruction from projection data measured over a limited number of views. The classical Nyquist sampling rule yields the minimum number of projection views required for accurate reconstruction. However, challenges exist in many medical and industrial imaging applications in which the projection data is undersampled. Classical analytical reconstruction methods such as filtered backprojection (FBP) are not a good choice for use in such cases because the data undersampling in the angular range introduces aliasing and streak artifacts that degrade lesion detectability. In this paper, we propose a Bayesian framework for maximum likelihood-expectation maximization (ML-EM)-based iterative reconstruction methods that incorporates a priori knowledge obtained from expected intensity information. The proposed framework is based on the fact that, in tomographic imaging, it is often possible to expect a set of intensity values of the reconstructed object with relatively high accuracy. The image reconstruction cost function is modified to include the l 1 norm distance to the a priori known information. The proposed method has the potential to regularize the solution to reduce artifacts without missing lesions that cannot be expected from the a priori information. Numerical studies showed a significant improvement in image quality and lesion detectability under the condition of highly undersampled projection data. (author)

Clinical correlative evaluation of an iterative method for reconstruction of brain SPECT images

International Nuclear Information System (INIS)

Nobili, Flavio; Vitali, Paolo; Calvini, Piero; Bollati, Francesca; Girtler, Nicola; Delmonte, Marta; Mariani, Giuliano; Rodriguez, Guido

2001-01-01

Background: Brain SPECT and PET investigations have showed discrepancies in Alzheimer's disease (AD) when considering data deriving from deeply located structures, such as the mesial temporal lobe. These discrepancies could be due to a variety of factors, including substantial differences in gamma-cameras and underlying technology. Mesial temporal structures are deeply located within the brain and the commonly used Filtered Back-Projection (FBP) technique does not fully take into account either the physical parameters of gamma-cameras or geometry of collimators. In order to overcome these limitations, alternative reconstruction methods have been proposed, such as the iterative method of the Conjugate Gradients with modified matrix (CG). However, the clinical applications of these methods have so far been only anecdotal. The present study was planned to compare perfusional SPECT data as derived from the conventional FBP method and from the iterative CG method, which takes into account the geometrical and physical characteristics of the gamma-camera, by a correlative approach with neuropsychology. Methods: Correlations were compared between perfusion of the hippocampal region, as achieved by both the FBP and the CG reconstruction methods, and a short-memory test (Selective Reminding Test, SRT), specifically addressing one of its function. A brain-dedicated camera (CERASPECT) was used for SPECT studies with 99m Tc-hexamethylpropylene-amine-oxime in 23 consecutive patients (mean age: 74.2±6.5) with mild (Mini-Mental Status Examination score ≥15, mean 20.3±3), probable AD. Counts from a hippocampal region in each hemisphere were referred to the average thalamic counts. Results: Hippocampal perfusion significantly correlated with the MMSE score with similar statistical significance (p<0.01) between the two reconstruction methods. Correlation between hippocampal perfusion and the SRT score was better with the CG method (r=0.50 for both hemispheres, p<0.01) than with

Clinical correlative evaluation of an iterative method for reconstruction of brain SPECT images

Energy Technology Data Exchange (ETDEWEB)

Nobili, Flavio E-mail: fnobili@smartino.ge.it; Vitali, Paolo; Calvini, Piero; Bollati, Francesca; Girtler, Nicola; Delmonte, Marta; Mariani, Giuliano; Rodriguez, Guido

2001-08-01

Background: Brain SPECT and PET investigations have showed discrepancies in Alzheimer's disease (AD) when considering data deriving from deeply located structures, such as the mesial temporal lobe. These discrepancies could be due to a variety of factors, including substantial differences in gamma-cameras and underlying technology. Mesial temporal structures are deeply located within the brain and the commonly used Filtered Back-Projection (FBP) technique does not fully take into account either the physical parameters of gamma-cameras or geometry of collimators. In order to overcome these limitations, alternative reconstruction methods have been proposed, such as the iterative method of the Conjugate Gradients with modified matrix (CG). However, the clinical applications of these methods have so far been only anecdotal. The present study was planned to compare perfusional SPECT data as derived from the conventional FBP method and from the iterative CG method, which takes into account the geometrical and physical characteristics of the gamma-camera, by a correlative approach with neuropsychology. Methods: Correlations were compared between perfusion of the hippocampal region, as achieved by both the FBP and the CG reconstruction methods, and a short-memory test (Selective Reminding Test, SRT), specifically addressing one of its function. A brain-dedicated camera (CERASPECT) was used for SPECT studies with {sup 99m}Tc-hexamethylpropylene-amine-oxime in 23 consecutive patients (mean age: 74.2{+-}6.5) with mild (Mini-Mental Status Examination score {>=}15, mean 20.3{+-}3), probable AD. Counts from a hippocampal region in each hemisphere were referred to the average thalamic counts. Results: Hippocampal perfusion significantly correlated with the MMSE score with similar statistical significance (p<0.01) between the two reconstruction methods. Correlation between hippocampal perfusion and the SRT score was better with the CG method (r=0.50 for both hemispheres, p<0

Direct Reconstruction of CT-based Attenuation Correction Images for PET with Cluster-Based Penalties

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Kim, Soo Mee; Alessio, Adam M.; De Man, Bruno; Asma, Evren; Kinahan, Paul E.

2015-01-01

Extremely low-dose CT acquisitions for the purpose of PET attenuation correction will have a high level of noise and biasing artifacts due to factors such as photon starvation. This work explores a priori knowledge appropriate for CT iterative image reconstruction for PET attenuation correction. We investigate the maximum a posteriori (MAP) framework with cluster-based, multinomial priors for the direct reconstruction of the PET attenuation map. The objective function for direct iterative attenuation map reconstruction was modeled as a Poisson log-likelihood with prior terms consisting of quadratic (Q) and mixture (M) distributions. The attenuation map is assumed to have values in 4 clusters: air+background, lung, soft tissue, and bone. Under this assumption, the MP was a mixture probability density function consisting of one exponential and three Gaussian distributions. The relative proportion of each cluster was jointly estimated during each voxel update of direct iterative coordinate decent (dICD) method. Noise-free data were generated from NCAT phantom and Poisson noise was added. Reconstruction with FBP (ramp filter) was performed on the noise-free (ground truth) and noisy data. For the noisy data, dICD reconstruction was performed with the combination of different prior strength parameters (β and γ) of Q- and M-penalties. The combined quadratic and mixture penalties reduces the RMSE by 18.7% compared to post-smoothed iterative reconstruction and only 0.7% compared to quadratic alone. For direct PET attenuation map reconstruction from ultra-low dose CT acquisitions, the combination of quadratic and mixture priors offers regularization of both variance and bias and is a potential method to derive attenuation maps with negligible patient dose. However, the small improvement in quantitative accuracy relative to the substantial increase in algorithm complexity does not currently justify the use of mixture-based PET attenuation priors for reconstruction of CT

High resolution x-ray CMT: Reconstruction methods

Energy Technology Data Exchange (ETDEWEB)

Brown, J.K.

1997-02-01

This paper qualitatively discusses the primary characteristics of methods for reconstructing tomographic images from a set of projections. These reconstruction methods can be categorized as either {open_quotes}analytic{close_quotes} or {open_quotes}iterative{close_quotes} techniques. Analytic algorithms are derived from the formal inversion of equations describing the imaging process, while iterative algorithms incorporate a model of the imaging process and provide a mechanism to iteratively improve image estimates. Analytic reconstruction algorithms are typically computationally more efficient than iterative methods; however, analytic algorithms are available for a relatively limited set of imaging geometries and situations. Thus, the framework of iterative reconstruction methods is better suited for high accuracy, tomographic reconstruction codes.

Improved iterative image reconstruction algorithm for the exterior problem of computed tomography

International Nuclear Information System (INIS)

Guo, Yumeng; Zeng, Li

2017-01-01

In industrial applications that are limited by the angle of a fan-beam and the length of a detector, the exterior problem of computed tomography (CT) uses only the projection data that correspond to the external annulus of the objects to reconstruct an image. Because the reconstructions are not affected by the projection data that correspond to the interior of the objects, the exterior problem is widely applied to detect cracks in the outer wall of large-sized objects, such as in-service pipelines. However, image reconstruction in the exterior problem is still a challenging problem due to truncated projection data and beam-hardening, both of which can lead to distortions and artifacts. Thus, developing an effective algorithm and adopting a scanning trajectory suited for the exterior problem may be valuable. In this study, an improved iterative algorithm that combines total variation minimization (TVM) with a region scalable fitting (RSF) model was developed for a unilateral off-centered scanning trajectory and can be utilized to inspect large-sized objects for defects. Experiments involving simulated phantoms and real projection data were conducted to validate the practicality of our algorithm. Furthermore, comparative experiments show that our algorithm outperforms others in suppressing the artifacts caused by truncated projection data and beam-hardening.

Improved iterative image reconstruction algorithm for the exterior problem of computed tomography

Energy Technology Data Exchange (ETDEWEB)

Guo, Yumeng [Chongqing University, College of Mathematics and Statistics, Chongqing 401331 (China); Chongqing University, ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing 400044 (China); Zeng, Li, E-mail: drlizeng@cqu.edu.cn [Chongqing University, College of Mathematics and Statistics, Chongqing 401331 (China); Chongqing University, ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing 400044 (China)

2017-01-11

In industrial applications that are limited by the angle of a fan-beam and the length of a detector, the exterior problem of computed tomography (CT) uses only the projection data that correspond to the external annulus of the objects to reconstruct an image. Because the reconstructions are not affected by the projection data that correspond to the interior of the objects, the exterior problem is widely applied to detect cracks in the outer wall of large-sized objects, such as in-service pipelines. However, image reconstruction in the exterior problem is still a challenging problem due to truncated projection data and beam-hardening, both of which can lead to distortions and artifacts. Thus, developing an effective algorithm and adopting a scanning trajectory suited for the exterior problem may be valuable. In this study, an improved iterative algorithm that combines total variation minimization (TVM) with a region scalable fitting (RSF) model was developed for a unilateral off-centered scanning trajectory and can be utilized to inspect large-sized objects for defects. Experiments involving simulated phantoms and real projection data were conducted to validate the practicality of our algorithm. Furthermore, comparative experiments show that our algorithm outperforms others in suppressing the artifacts caused by truncated projection data and beam-hardening.

Comparison of methods for suppressing edge and aliasing artefacts in iterative x-ray CT reconstruction

International Nuclear Information System (INIS)

Zbijewski, Wojciech; Beekman, Freek J

2006-01-01

X-ray CT images obtained with iterative reconstruction (IR) can be hampered by the so-called edge and aliasing artefacts, which appear as interference patterns and severe overshoots in the areas of sharp intensity transitions. Previously, we have demonstrated that these artefacts are caused by discretization errors during the projection simulation step in IR. Although these errors are inherent to IR, they can be adequately suppressed by reconstruction on an image grid that is finer than that typically used for analytical methods such as filtered back-projection. Two other methods that may prevent edge artefacts are: (i) smoothing the projections prior to reconstruction or (ii) using an image representation different from voxels; spherically symmetric Kaiser-Bessel functions are a frequently employed example of such a representation. In this paper, we compare reconstruction on a fine grid with the two above-mentioned alternative strategies for edge artefact reduction. We show that the use of a fine grid results in a more adequate suppression of artefacts than the smoothing of projections or using the Kaiser-Bessel image representation

Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms

International Nuclear Information System (INIS)

Sidky, Emil Y.; Pan Xiaochuan; Reiser, Ingrid S.; Nishikawa, Robert M.; Moore, Richard H.; Kopans, Daniel B.

2009-01-01

Purpose: The authors develop a practical, iterative algorithm for image-reconstruction in undersampled tomographic systems, such as digital breast tomosynthesis (DBT). Methods: The algorithm controls image regularity by minimizing the image total p variation (TpV), a function that reduces to the total variation when p=1.0 or the image roughness when p=2.0. Constraints on the image, such as image positivity and estimated projection-data tolerance, are enforced by projection onto convex sets. The fact that the tomographic system is undersampled translates to the mathematical property that many widely varied resultant volumes may correspond to a given data tolerance. Thus the application of image regularity serves two purposes: (1) Reduction in the number of resultant volumes out of those allowed by fixing the data tolerance, finding the minimum image TpV for fixed data tolerance, and (2) traditional regularization, sacrificing data fidelity for higher image regularity. The present algorithm allows for this dual role of image regularity in undersampled tomography. Results: The proposed image-reconstruction algorithm is applied to three clinical DBT data sets. The DBT cases include one with microcalcifications and two with masses. Conclusions: Results indicate that there may be a substantial advantage in using the present image-reconstruction algorithm for microcalcification imaging.

Model-based Iterative Reconstruction: Effect on Patient Radiation Dose and Image Quality in Pediatric Body CT

Science.gov (United States)

Dillman, Jonathan R.; Goodsitt, Mitchell M.; Christodoulou, Emmanuel G.; Keshavarzi, Nahid; Strouse, Peter J.

2014-01-01

Purpose To retrospectively compare image quality and radiation dose between a reduced-dose computed tomographic (CT) protocol that uses model-based iterative reconstruction (MBIR) and a standard-dose CT protocol that uses 30% adaptive statistical iterative reconstruction (ASIR) with filtered back projection. Materials and Methods Institutional review board approval was obtained. Clinical CT images of the chest, abdomen, and pelvis obtained with a reduced-dose protocol were identified. Images were reconstructed with two algorithms: MBIR and 100% ASIR. All subjects had undergone standard-dose CT within the prior year, and the images were reconstructed with 30% ASIR. Reduced- and standard-dose images were evaluated objectively and subjectively. Reduced-dose images were evaluated for lesion detectability. Spatial resolution was assessed in a phantom. Radiation dose was estimated by using volumetric CT dose index (CTDIvol) and calculated size-specific dose estimates (SSDE). A combination of descriptive statistics, analysis of variance, and t tests was used for statistical analysis. Results In the 25 patients who underwent the reduced-dose protocol, mean decrease in CTDIvol was 46% (range, 19%–65%) and mean decrease in SSDE was 44% (range, 19%–64%). Reduced-dose MBIR images had less noise (P > .004). Spatial resolution was superior for reduced-dose MBIR images. Reduced-dose MBIR images were equivalent to standard-dose images for lungs and soft tissues (P > .05) but were inferior for bones (P = .004). Reduced-dose 100% ASIR images were inferior for soft tissues (P ASIR. Conclusion CT performed with a reduced-dose protocol and MBIR is feasible in the pediatric population, and it maintains diagnostic quality. © RSNA, 2013 Online supplemental material is available for this article. PMID:24091359

Low-rank matrix approximation with manifold regularization.

Science.gov (United States)

Zhang, Zhenyue; Zhao, Keke

2013-07-01

This paper proposes a new model of low-rank matrix factorization that incorporates manifold regularization to the matrix factorization. Superior to the graph-regularized nonnegative matrix factorization, this new regularization model has globally optimal and closed-form solutions. A direct algorithm (for data with small number of points) and an alternate iterative algorithm with inexact inner iteration (for large scale data) are proposed to solve the new model. A convergence analysis establishes the global convergence of the iterative algorithm. The efficiency and precision of the algorithm are demonstrated numerically through applications to six real-world datasets on clustering and classification. Performance comparison with existing algorithms shows the effectiveness of the proposed method for low-rank factorization in general.

Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: A phantom study

International Nuclear Information System (INIS)

Kim, Hyungjin; Park, Chang Min; Song, Yong Sub; Lee, Sang Min; Goo, Jin Mo

2014-01-01

Purpose: To evaluate the influence of radiation dose settings and reconstruction algorithms on the measurement accuracy and reproducibility of semi-automated pulmonary nodule volumetry. Materials and methods: CT scans were performed on a chest phantom containing various nodules (10 and 12 mm; +100, −630 and −800 HU) at 120 kVp with tube current–time settings of 10, 20, 50, and 100 mAs. Each CT was reconstructed using filtered back projection (FBP), iDose 4 and iterative model reconstruction (IMR). Semi-automated volumetry was performed by two radiologists using commercial volumetry software for nodules at each CT dataset. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. The absolute percentage measurement errors and differences were then calculated for volume and mass. The influence of radiation dose and reconstruction algorithm on measurement accuracy, reproducibility and objective image quality metrics was analyzed using generalized estimating equations. Results: Measurement accuracy and reproducibility of nodule volume and mass were not significantly associated with CT radiation dose settings or reconstruction algorithms (p > 0.05). Objective image quality metrics of CT images were superior in IMR than in FBP or iDose 4 at all radiation dose settings (p < 0.05). Conclusion: Semi-automated nodule volumetry can be applied to low- or ultralow-dose chest CT with usage of a novel iterative reconstruction algorithm without losing measurement accuracy and reproducibility

Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: A phantom study

Energy Technology Data Exchange (ETDEWEB)

Kim, Hyungjin, E-mail: khj.snuh@gmail.com [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Park, Chang Min, E-mail: cmpark@radiol.snu.ac.kr [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Cancer Research Institute, Seoul National University, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Song, Yong Sub, E-mail: terasong@gmail.com [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Lee, Sang Min, E-mail: sangmin.lee.md@gmail.com [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Goo, Jin Mo, E-mail: jmgoo@plaza.snu.ac.kr [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Cancer Research Institute, Seoul National University, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of)

2014-05-15

Purpose: To evaluate the influence of radiation dose settings and reconstruction algorithms on the measurement accuracy and reproducibility of semi-automated pulmonary nodule volumetry. Materials and methods: CT scans were performed on a chest phantom containing various nodules (10 and 12 mm; +100, −630 and −800 HU) at 120 kVp with tube current–time settings of 10, 20, 50, and 100 mAs. Each CT was reconstructed using filtered back projection (FBP), iDose{sup 4} and iterative model reconstruction (IMR). Semi-automated volumetry was performed by two radiologists using commercial volumetry software for nodules at each CT dataset. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. The absolute percentage measurement errors and differences were then calculated for volume and mass. The influence of radiation dose and reconstruction algorithm on measurement accuracy, reproducibility and objective image quality metrics was analyzed using generalized estimating equations. Results: Measurement accuracy and reproducibility of nodule volume and mass were not significantly associated with CT radiation dose settings or reconstruction algorithms (p > 0.05). Objective image quality metrics of CT images were superior in IMR than in FBP or iDose{sup 4} at all radiation dose settings (p < 0.05). Conclusion: Semi-automated nodule volumetry can be applied to low- or ultralow-dose chest CT with usage of a novel iterative reconstruction algorithm without losing measurement accuracy and reproducibility.

Upgrade to iterative image reconstruction (IR) in abdominal MDCT imaging. A clinical study for detailed parameter optimization beyond vendor recommendations using the adaptive statistical iterative reconstruction environment (ASIR)

Energy Technology Data Exchange (ETDEWEB)

Mueck, F.G.; Koerner, M.; Scherr, M.K.; Geyer, L.L.; Deak, Z.; Linsenmaier, U.; Reiser, M.; Wirth, S. [Ludwig-Maximilians-Univ. Muenchen (Germany). Inst. fuer Klinische Radiologie

2012-03-15

To compare the image quality of dose-reduced 64-row abdominal CT reconstructed at different levels of adaptive statistical iterative reconstruction (ASIR) to full-dose baseline examinations reconstructed with filtered back-projection (FBP) in a clinical setting and upgrade situation. Abdominal baseline examinations (noise index NI = 29; LightSpeed VCT XT, GE) were intra-individually compared to follow-up studies on a CT with an ASIR option (NI = 43; Discovery HD750, GE), n = 42. Standard-kernel images were calculated with ASIR blendings of 0 - 100 % in slice and volume mode, respectively. Three experienced radiologists compared the image quality of these 567 sets to their corresponding full-dose baseline examination (-2: diagnostically inferior, -1: inferior, 0: equal, +1: superior, +2: diagnostically superior). Furthermore, a phantom was scanned. Statistical analysis used the Wilcoxon - the Mann-Whitney U-test and the intra-class correlation (ICC). The mean CTDIvol decreased from 19.7 {+-} 5.5 to 12.2 {+-} 4.7 mGy (p < 0.001). The ICC was 0.861. The total image quality of the dose-reduced ASIR studies was comparable to the baseline at ASIR 50 % in slice (p = 0.18) and ASIR 50 - 100 % in volume mode (p > 0.10). Volume mode performed 73 % slower than slice mode (p < 0.01). After the system upgrade, the vendor recommendation of ASIR 50 % in slice mode allowed for a dose reduction of 38 % in abdominal CT with comparable image quality and time expenditure. However, there is still further dose reduction potential for more complex reconstruction settings. (orig.)

Parallelization of the model-based iterative reconstruction algorithm DIRA

International Nuclear Information System (INIS)

Oertenberg, A.; Sandborg, M.; Alm Carlsson, G.; Malusek, A.; Magnusson, M.

2016-01-01

New paradigms for parallel programming have been devised to simplify software development on multi-core processors and many-core graphical processing units (GPU). Despite their obvious benefits, the parallelization of existing computer programs is not an easy task. In this work, the use of the Open Multiprocessing (OpenMP) and Open Computing Language (OpenCL) frameworks is considered for the parallelization of the model-based iterative reconstruction algorithm DIRA with the aim to significantly shorten the code's execution time. Selected routines were parallelized using OpenMP and OpenCL libraries; some routines were converted from MATLAB to C and optimised. Parallelization of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a 16-core computer. Parallelization with OpenCL was more difficult owing to differences between the central processing unit and GPU architectures. The resulting speedup was substantially lower than the theoretical peak performance of the GPU; the cause was explained. (authors)

Whole-body CT for lymphoma staging: Feasibility of halving radiation dose and risk by iterative image reconstruction

Energy Technology Data Exchange (ETDEWEB)

Meyer, M., E-mail: mathias.meyer@medma.uni-heidelberg.de [Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Klein, S.A., E-mail: stefan.klein@umm.de [Department of Hematology and Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Brix, G., E-mail: gbrix@bfs.de [Department of Medical and Occupational Radiation Protection, Federal Office for Radiation Protection, Ingolstädter Landstraße 1, D-85764 Neuherberg (Germany); Fink, C., E-mail: Christian.Fink@medma.uni-heidelberg.de [Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Pilz, L., E-mail: lothar.pilz@medma.uni-heidelberg.de [Department of Biostatistics, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Jafarov, H., E-mail: Hashim.Jafarov@umm.de [Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Hofmann, W.K., E-mail: w.k.hofmann@umm.de [Department of Hematology and Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Schoenberg, S.O., E-mail: Stefan.Schoenberg@umm.de [Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); and others

2014-02-15

Objectives: Patients with lymphoma are at higher-risk of secondary malignancies mainly due to effects of cancer therapy as well as frequent radiological surveillance. We thus aimed to investigate the objective and subjective image quality as well as radiation exposure and risk of full-dose standard (FDS), full-dose iterative (FDI), and half-dose iterative (HDI) image reconstruction in patients with lymphoma. Material and methods: In 100 lymphoma patients, contrast-enhanced whole-body staging was performed on a dual-source CT. To acquire full-dose and half-dose CT data simultaneously, the total current-time product was equally distributed on both tubes operating at 120 kV. HDI reconstructions were calculated by using only data from one tube. Quantitative image quality was assessed by measuring image noise in different tissues of the neck, thorax, and abdomen. Overall diagnostic image quality was assessed using a 5-point Likert scale. Radiation doses and risks were estimated for a male and female reference person. Results: For all anatomical regions apart from the lungs image noise was significantly lower and the overall subjective image quality significantly better when using FDI and HDI instead of FDS reconstruction (p < 0.05). For the half-dose protocol, the risk to develop a radiation-induced cancer was estimated to be less than 0.11/0.19% for an adult male/female. Conclusions: Image quality of FDI and more importantly of HDI is superior to FDS reconstruction, thus enabling to halve radiation dose and risk to lymphoma patients.

Radiation dose reduction in cerebral CT perfusion imaging using iterative reconstruction

International Nuclear Information System (INIS)

Niesten, Joris M.; Schaaf, Irene C. van der; Riordan, Alan J.; Jong, Hugo W.A.M. de; Eijspaart, Daniel; Smit, Ewoud J.; Mali, Willem P.T.M.; Velthuis, Birgitta K.; Horsch, Alexander D.

2014-01-01

To investigate whether iterative reconstruction (IR) in cerebral CT perfusion (CTP) allows for 50 % dose reduction while maintaining image quality (IQ). A total of 48 CTP examinations were reconstructed into a standard dose (150 mAs) with filtered back projection (FBP) and half-dose (75 mAs) with two strengths of IR (middle and high). Objective IQ (quantitative perfusion values, contrast-to-noise ratio (CNR), penumbra, infarct area and penumbra/infarct (P/I) index) and subjective IQ (diagnostic IQ on a four-point Likert scale and overall IQ binomial) were compared among the reconstructions. Half-dose CTP with high IR level had, compared with standard dose with FBP, similar objective (grey matter cerebral blood volume (CBV) 4.4 versus 4.3 mL/100 g, CNR 1.59 versus 1.64 and P/I index 0.74 versus 0.73, respectively) and subjective diagnostic IQ (mean Likert scale 1.42 versus 1.49, respectively). The overall IQ in half-dose with high IR level was scored lower in 26-31 %. Half-dose with FBP and with the middle IR level were inferior to standard dose with FBP. With the use of IR in CTP imaging it is possible to examine patients with a half dose without significantly altering the objective and diagnostic IQ. The standard dose with FBP is still preferable in terms of subjective overall IQ in about one quarter of patients. (orig.)

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations.

Science.gov (United States)

Pogue, Brian W; Song, Xiaomei; Tosteson, Tor D; McBride, Troy O; Jiang, Shudong; Paulsen, Keith D

2002-07-01

Near-infrared (NIR) diffuse tomography is an emerging method for imaging the interior of tissues to quantify concentrations of hemoglobin and exogenous chromophores non-invasively in vivo. It often exploits an optical diffusion model-based image reconstruction algorithm to estimate spatial property values from measurements of the light flux at the surface of the tissue. In this study, mean-squared error (MSE) over the image is used to evaluate methods for regularizing the ill-posed inverse image reconstruction problem in NIR tomography. Estimates of image bias and image standard deviation were calculated based upon 100 repeated reconstructions of a test image with randomly distributed noise added to the light flux measurements. It was observed that the bias error dominates at high regularization parameter values while variance dominates as the algorithm is allowed to approach the optimal solution. This optimum does not necessarily correspond to the minimum projection error solution, but typically requires further iteration with a decreasing regularization parameter to reach the lowest image error. Increasing measurement noise causes a need to constrain the minimum regularization parameter to higher values in order to achieve a minimum in the overall image MSE.

Complex amplitude reconstruction by iterative amplitude-phase retrieval algorithm with reference

Science.gov (United States)

Shen, Cheng; Guo, Cheng; Tan, Jiubin; Liu, Shutian; Liu, Zhengjun

2018-06-01

Multi-image iterative phase retrieval methods have been successfully applied in plenty of research fields due to their simple but efficient implementation. However, there is a mismatch between the measurement of the first long imaging distance and the sequential interval. In this paper, an amplitude-phase retrieval algorithm with reference is put forward without additional measurements or priori knowledge. It gets rid of measuring the first imaging distance. With a designed update formula, it significantly raises the convergence speed and the reconstruction fidelity, especially in phase retrieval. Its superiority over the original amplitude-phase retrieval (APR) method is validated by numerical analysis and experiments. Furthermore, it provides a conceptual design of a compact holographic image sensor, which can achieve numerical refocusing easily.

Adaptive wavelet tight frame construction for accelerating MRI reconstruction

Directory of Open Access Journals (Sweden)

Genjiao Zhou

2017-09-01

Full Text Available The sparsity regularization approach, which assumes that the image of interest is likely to have sparse representation in some transform domain, has been an active research area in image processing and medical image reconstruction. Although various sparsifying transforms have been used in medical image reconstruction such as wavelet, contourlet, and total variation (TV etc., the efficiency of these transforms typically rely on the special structure of the underlying image. A better way to address this issue is to develop an overcomplete dictionary from the input data in order to get a better sparsifying transform for the underlying image. However, the general overcomplete dictionaries do not satisfy the so-called perfect reconstruction property which ensures that the given signal can be perfectly represented by its canonical coefficients in a manner similar to orthonormal bases, resulting in time consuming in the iterative image reconstruction. This work is to develop an adaptive wavelet tight frame method for magnetic resonance image reconstruction. The proposed scheme incorporates the adaptive wavelet tight frame approach into the magnetic resonance image reconstruction by solving a l0-regularized minimization problem. Numerical results show that the proposed approach provides significant time savings as compared to the over-complete dictionary based methods with comparable performance in terms of both peak signal-to-noise ratio and subjective visual quality.

Low dose dynamic CT myocardial perfusion imaging using a statistical iterative reconstruction method

Energy Technology Data Exchange (ETDEWEB)

Tao, Yinghua [Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705 (United States); Chen, Guang-Hong [Department of Medical Physics and Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin 53705 (United States); Hacker, Timothy A.; Raval, Amish N. [Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin 53792 (United States); Van Lysel, Michael S.; Speidel, Michael A., E-mail: speidel@wisc.edu [Department of Medical Physics and Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin 53705 (United States)

2014-07-15

Purpose: Dynamic CT myocardial perfusion imaging has the potential to provide both functional and anatomical information regarding coronary artery stenosis. However, radiation dose can be potentially high due to repeated scanning of the same region. The purpose of this study is to investigate the use of statistical iterative reconstruction to improve parametric maps of myocardial perfusion derived from a low tube current dynamic CT acquisition. Methods: Four pigs underwent high (500 mA) and low (25 mA) dose dynamic CT myocardial perfusion scans with and without coronary occlusion. To delineate the affected myocardial territory, an N-13 ammonia PET perfusion scan was performed for each animal in each occlusion state. Filtered backprojection (FBP) reconstruction was first applied to all CT data sets. Then, a statistical iterative reconstruction (SIR) method was applied to data sets acquired at low dose. Image voxel noise was matched between the low dose SIR and high dose FBP reconstructions. CT perfusion maps were compared among the low dose FBP, low dose SIR and high dose FBP reconstructions. Numerical simulations of a dynamic CT scan at high and low dose (20:1 ratio) were performed to quantitatively evaluate SIR and FBP performance in terms of flow map accuracy, precision, dose efficiency, and spatial resolution. Results: Forin vivo studies, the 500 mA FBP maps gave −88.4%, −96.0%, −76.7%, and −65.8% flow change in the occluded anterior region compared to the open-coronary scans (four animals). The percent changes in the 25 mA SIR maps were in good agreement, measuring −94.7%, −81.6%, −84.0%, and −72.2%. The 25 mA FBP maps gave unreliable flow measurements due to streaks caused by photon starvation (percent changes of +137.4%, +71.0%, −11.8%, and −3.5%). Agreement between 25 mA SIR and 500 mA FBP global flow was −9.7%, 8.8%, −3.1%, and 26.4%. The average variability of flow measurements in a nonoccluded region was 16.3%, 24.1%, and 937

Low dose dynamic CT myocardial perfusion imaging using a statistical iterative reconstruction method

International Nuclear Information System (INIS)

Tao, Yinghua; Chen, Guang-Hong; Hacker, Timothy A.; Raval, Amish N.; Van Lysel, Michael S.; Speidel, Michael A.

2014-01-01

Purpose: Dynamic CT myocardial perfusion imaging has the potential to provide both functional and anatomical information regarding coronary artery stenosis. However, radiation dose can be potentially high due to repeated scanning of the same region. The purpose of this study is to investigate the use of statistical iterative reconstruction to improve parametric maps of myocardial perfusion derived from a low tube current dynamic CT acquisition. Methods: Four pigs underwent high (500 mA) and low (25 mA) dose dynamic CT myocardial perfusion scans with and without coronary occlusion. To delineate the affected myocardial territory, an N-13 ammonia PET perfusion scan was performed for each animal in each occlusion state. Filtered backprojection (FBP) reconstruction was first applied to all CT data sets. Then, a statistical iterative reconstruction (SIR) method was applied to data sets acquired at low dose. Image voxel noise was matched between the low dose SIR and high dose FBP reconstructions. CT perfusion maps were compared among the low dose FBP, low dose SIR and high dose FBP reconstructions. Numerical simulations of a dynamic CT scan at high and low dose (20:1 ratio) were performed to quantitatively evaluate SIR and FBP performance in terms of flow map accuracy, precision, dose efficiency, and spatial resolution. Results: Forin vivo studies, the 500 mA FBP maps gave −88.4%, −96.0%, −76.7%, and −65.8% flow change in the occluded anterior region compared to the open-coronary scans (four animals). The percent changes in the 25 mA SIR maps were in good agreement, measuring −94.7%, −81.6%, −84.0%, and −72.2%. The 25 mA FBP maps gave unreliable flow measurements due to streaks caused by photon starvation (percent changes of +137.4%, +71.0%, −11.8%, and −3.5%). Agreement between 25 mA SIR and 500 mA FBP global flow was −9.7%, 8.8%, −3.1%, and 26.4%. The average variability of flow measurements in a nonoccluded region was 16.3%, 24.1%, and 937