MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering

International Nuclear Information System (INIS)

Adluru, Ganesh; Anderson, Jeffrey; Gur, Yaniv; Chen, Liyong; Feinberg, David; DiBella, Edward V. R.

2015-01-01

Purpose: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. Methods: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. Results: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. Conclusions: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions

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

Science.gov (United States)

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.

Optimization of SPECT-CT Hybrid Imaging Using Iterative Image Reconstruction for Low-Dose CT: A Phantom Study.

Directory of Open Access Journals (Sweden)

Oliver S Grosser

Full Text Available Hybrid imaging combines nuclear medicine imaging such as single photon emission computed tomography (SPECT or positron emission tomography (PET with computed tomography (CT. Through this hybrid design, scanned patients accumulate radiation exposure from both applications. Imaging modalities have been the subject of long-term optimization efforts, focusing on diagnostic applications. It was the aim of this study to investigate the influence of an iterative CT image reconstruction algorithm (ASIR on the image quality of the low-dose CT images.Examinations were performed with a SPECT-CT scanner with standardized CT and SPECT-phantom geometries and CT protocols with systematically reduced X-ray tube currents. Analyses included image quality with respect to photon flux. Results were compared to the standard FBP reconstructed images. The general impact of the CT-based attenuation maps used during SPECT reconstruction was examined for two SPECT phantoms. Using ASIR for image reconstructions, image noise was reduced compared to FBP reconstructions for the same X-ray tube current. The Hounsfield unit (HU values reconstructed by ASIR were correlated to the FBP HU values(R2 ≥ 0.88 and the contrast-to-noise ratio (CNR was improved by ASIR. However, for a phantom with increased attenuation, the HU values shifted for low X-ray tube currents I ≤ 60 mA (p ≤ 0.04. In addition, the shift of the HU values was observed within the attenuation corrected SPECT images for very low X-ray tube currents (I ≤ 20 mA, p ≤ 0.001.In general, the decrease in X-ray tube current up to 30 mA in combination with ASIR led to a reduction of CT-related radiation exposure without a significant decrease in image quality.

Time-of-flight PET image reconstruction using origin ensembles

Science.gov (United States)

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-01

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.

Improved visualization of collateral ligaments of the ankle: multiplanar reconstructions based on standard 2D turbo spin-echo MR images

International Nuclear Information System (INIS)

Duc, Sylvain R.; Mengiardi, Bernard; Pfirrmann, Christian W.A.; Hodler, Juerg; Zanetti, Marco

2007-01-01

The purpose of the study was to evaluate the visualization of the collateral ankle ligaments on multiplanar reconstructions (MPR) based on standard 2D turbo spin-echo images. Coronal and axial T2-weighted turbo spin-echo and MPR angled parallel to the course of the ligaments of 15 asymptomatic and 15 symptomatic ankles were separately analyzed by two musculoskeletal radiologists. Image quality was assessed in the asymptomatic ankles qualitatively. In the symptomatic ankles interobserver agreement and reader confidence was determined for each ligament. On MPR the tibionavicular and calcaneofibular ligaments were more commonly demonstrated on a single image than on standard MR images (reader 1: 13 versus 0, P=0.002; reader 2: 14 versus 1, P=0.001 and reader 1: 13 versus 2, P=0.001; reader 2: 14 versus 0, P<0.001). The tibionavicular ligament was considered to be better delineated on MPR by reader 1 (12 versus 3, P=0.031). In the symptomatic ankles, reader confidence was greater with MPR for all ligaments except for the tibiocalcanear ligament (both readers) and the anterior and posterior talofibular ligaments (for reader 2). Interobserver agreement was increased with MPR for the tibionavicular ligament. Multiplanar reconstructions of 2D turbo spin-echo images improve the visualization of the tibionavicular and calcaneofibular ligaments and strengthen diagnostic confidence for these ligaments. (orig.)

Reconstruction of a ring applicator using CT imaging: impact of the reconstruction method and applicator orientation

International Nuclear Information System (INIS)

Hellebust, Taran Paulsen; Tanderup, Kari; Bergstrand, Eva Stabell; Knutsen, Bjoern Helge; Roeislien, Jo; Olsen, Dag Rune

2007-01-01

The purpose of this study is to investigate whether the method of applicator reconstruction and/or the applicator orientation influence the dose calculation to points around the applicator for brachytherapy of cervical cancer with CT-based treatment planning. A phantom, containing a fixed ring applicator set and six lead pellets representing dose points, was used. The phantom was CT scanned with the ring applicator at four different angles related to the image plane. In each scan the applicator was reconstructed by three methods: (1) direct reconstruction in each image (DR) (2) reconstruction in multiplanar reconstructed images (MPR) and (3) library plans, using pre-defined applicator geometry (LIB). The doses to the lead pellets were calculated. The relative standard deviation (SD) for all reconstruction methods was less than 3.7% in the dose points. The relative SD for the LIB method was significantly lower (p < 0.05) than for the DR and MPR methods for all but two points. All applicator orientations had similar dose calculation reproducibility. Using library plans for applicator reconstruction gives the most reproducible dose calculation. However, with restrictive guidelines for applicator reconstruction the uncertainties for all methods are low compared to other factors influencing the accuracy of brachytherapy

Accelerated Compressed Sensing Based CT Image Reconstruction.

Science.gov (United States)

Hashemi, SayedMasoud; Beheshti, Soosan; Gill, Patrick R; Paul, Narinder S; Cobbold, Richard S C

2015-01-01

In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

Accelerated Compressed Sensing Based CT Image Reconstruction

Directory of Open Access Journals (Sweden)

SayedMasoud Hashemi

2015-01-01

Full Text Available In X-ray computed tomography (CT an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

Partial fourier and parallel MR image reconstruction with integrated gradient nonlinearity correction.

Science.gov (United States)

Tao, Shengzhen; Trzasko, Joshua D; Shu, Yunhong; Weavers, Paul T; Huston, John; Gray, Erin M; Bernstein, Matt A

2016-06-01

To describe how integrated gradient nonlinearity (GNL) correction can be used within noniterative partial Fourier (homodyne) and parallel (SENSE and GRAPPA) MR image reconstruction strategies, and demonstrate that performing GNL correction during, rather than after, these routines mitigates the image blurring and resolution loss caused by postreconstruction image domain based GNL correction. Starting from partial Fourier and parallel magnetic resonance imaging signal models that explicitly account for GNL, noniterative image reconstruction strategies for each accelerated acquisition technique are derived under the same core mathematical assumptions as their standard counterparts. A series of phantom and in vivo experiments on retrospectively undersampled data were performed to investigate the spatial resolution benefit of integrated GNL correction over conventional postreconstruction correction. Phantom and in vivo results demonstrate that the integrated GNL correction reduces the image blurring introduced by the conventional GNL correction, while still correcting GNL-induced coarse-scale geometrical distortion. Images generated from undersampled data using the proposed integrated GNL strategies offer superior depiction of fine image detail, for example, phantom resolution inserts and anatomical tissue boundaries. Noniterative partial Fourier and parallel imaging reconstruction methods with integrated GNL correction reduce the resolution loss that occurs during conventional postreconstruction GNL correction while preserving the computational efficiency of standard reconstruction techniques. Magn Reson Med 75:2534-2544, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Overview of image reconstruction

International Nuclear Information System (INIS)

Marr, R.B.

1980-04-01

Image reconstruction (or computerized tomography, etc.) is any process whereby a function, f, on R/sup n/ is estimated from empirical data pertaining to its integrals, ∫f(x) dx, for some collection of hyperplanes of dimension k < n. The paper begins with background information on how image reconstruction problems have arisen in practice, and describes some of the application areas of past or current interest; these include radioastronomy, optics, radiology and nuclear medicine, electron microscopy, acoustical imaging, geophysical tomography, nondestructive testing, and NMR zeugmatography. Then the various reconstruction algorithms are discussed in five classes: summation, or simple back-projection; convolution, or filtered back-projection; Fourier and other functional transforms; orthogonal function series expansion; and iterative methods. Certain more technical mathematical aspects of image reconstruction are considered from the standpoint of uniqueness, consistency, and stability of solution. The paper concludes by presenting certain open problems. 73 references

Fast implementations of reconstruction-based scatter compensation in fully 3D SPECT image reconstruction

International Nuclear Information System (INIS)

Kadrmas, Dan J.; Karimi, Seemeen S.; Frey, Eric C.; Tsui, Benjamin M.W.

1998-01-01

Accurate scatter compensation in SPECT can be performed by modelling the scatter response function during the reconstruction process. This method is called reconstruction-based scatter compensation (RBSC). It has been shown that RBSC has a number of advantages over other methods of compensating for scatter, but using RBSC for fully 3D compensation has resulted in prohibitively long reconstruction times. In this work we propose two new methods that can be used in conjunction with existing methods to achieve marked reductions in RBSC reconstruction times. The first method, coarse-grid scatter modelling, significantly accelerates the scatter model by exploiting the fact that scatter is dominated by low-frequency information. The second method, intermittent RBSC, further accelerates the reconstruction process by limiting the number of iterations during which scatter is modelled. The fast implementations were evaluated using a Monte Carlo simulated experiment of the 3D MCAT phantom with 99m Tc tracer, and also using experimentally acquired data with 201 Tl tracer. Results indicated that these fast methods can reconstruct, with fully 3D compensation, images very similar to those obtained using standard RBSC methods, and in reconstruction times that are an order of magnitude shorter. Using these methods, fully 3D iterative reconstruction with RBSC can be performed well within the realm of clinically realistic times (under 10 minutes for 64x64x24 image reconstruction). (author)

A two-step Hilbert transform method for 2D image reconstruction

International Nuclear Information System (INIS)

Noo, Frederic; Clackdoyle, Rolf; Pack, Jed D

2004-01-01

The paper describes a new accurate two-dimensional (2D) image reconstruction method consisting of two steps. In the first step, the backprojected image is formed after taking the derivative of the parallel projection data. In the second step, a Hilbert filtering is applied along certain lines in the differentiated backprojection (DBP) image. Formulae for performing the DBP step in fan-beam geometry are also presented. The advantage of this two-step Hilbert transform approach is that in certain situations, regions of interest (ROIs) can be reconstructed from truncated projection data. Simulation results are presented that illustrate very similar reconstructed image quality using the new method compared to standard filtered backprojection, and that show the capability to correctly handle truncated projections. In particular, a simulation is presented of a wide patient whose projections are truncated laterally yet for which highly accurate ROI reconstruction is obtained

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.

Image reconstruction in k-space from MR data encoded with ambiguous gradient fields.

Science.gov (United States)

Schultz, Gerrit; Gallichan, Daniel; Weber, Hans; Witschey, Walter R T; Honal, Matthias; Hennig, Jürgen; Zaitsev, Maxim

2015-02-01

In this work, the limits of image reconstruction in k-space are explored when non-bijective gradient fields are used for spatial encoding. The image space analogy between parallel imaging and imaging with non-bijective encoding fields is partially broken in k-space. As a consequence, it is hypothesized and proven that ambiguities can only be resolved partially in k-space, and not completely as is the case in image space. Image-space and k-space based reconstruction algorithms for multi-channel radiofrequency data acquisitions are programmed and tested using numerical simulations as well as in vivo measurement data. The hypothesis is verified based on an analysis of reconstructed images. It is found that non-bijective gradient fields have the effect that densely sampled autocalibration data, used for k-space reconstruction, provide less information than a separate scan of the receiver coil sensitivity maps, used for image space reconstruction. Consequently, in k-space only the undersampling artifact can be unfolded, whereas in image space, it is also possible to resolve aliasing that is caused by the non-bijectivity of the gradient fields. For standard imaging, reconstruction in image space and in k-space is nearly equivalent, whereas there is a fundamental difference with practical consequences for the selection of image reconstruction algorithms when non-bijective encoding fields are involved. © 2014 Wiley Periodicals, Inc.

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.

Comparison of clinical and physics scoring of PET images when image reconstruction parameters are varied

International Nuclear Information System (INIS)

Walsh, C.; Johnston, C.; Sheehy, N.; Reilly, G. O.

2013-01-01

In this study the quantitative and qualitative image quality (IQ) measurements with clinical judgement of IQ in positron emission tomography (PET) were compared. The limitations of IQ metrics and the proposed criteria of acceptability for PET scanners are discussed. Phantom and patient images were reconstructed using seven different iterative reconstruction protocols. For each reconstructed set of images, IQ was scored based both on the visual analysis and on the quantitative metrics. The quantitative physics metrics did not rank the reconstruction protocols in the same order as the clinicians' scoring of perceived IQ (R s = -0.54). Better agreement was achieved when comparing the clinical perception of IQ to the physicist's visual assessment of IQ in the phantom images (R s = +0.59). The closest agreement was seen between the quantitative physics metrics and the measurement of the standard uptake values (SUVs) in small tumours (R s = +0.92). Given the disparity between the clinical perception of IQ and the physics metrics a cautious approach to use of IQ measurements for determining suspension levels is warranted. (authors)

Clinical evaluation of PET image reconstruction using a spatial resolution model

DEFF Research Database (Denmark)

Andersen, Flemming Littrup; Klausen, Thomas Levin; Loft, Annika

2013-01-01

PURPOSE: PET image resolution is variable across the measured field-of-view and described by the point spread function (PSF). When accounting for the PSF during PET image reconstruction image resolution is improved and partial volume effects are reduced. Here, we evaluate the effect of PSF......-based reconstruction on lesion quantification in routine clinical whole-body (WB) PET/CT imaging. MATERIALS AND METHODS: 41 oncology patients were referred for a WB-PET/CT examination (Biograph 40 TruePoint). Emission data were acquired at 2.5min/bed at 1hpi of 400 MBq [18F]-FDG. Attenuation-corrected PET images were...... reconstructed on 336×336-matrices using: (R1) standard AW-OSEM (4 iter, 8 subsets, 4mm Gaussian) and (R2) AW-OSEM with PSF (3 iter, 21 subsets, 2mm). Blinded and randomised reading of R1- and R2-PET images was performed. Individual lesions were located and counted independently on both sets of images...

MR image reconstruction via guided filter.

Science.gov (United States)

Huang, Heyan; Yang, Hang; Wang, Kang

2018-04-01

Magnetic resonance imaging (MRI) reconstruction from the smallest possible set of Fourier samples has been a difficult problem in medical imaging field. In our paper, we present a new approach based on a guided filter for efficient MRI recovery algorithm. The guided filter is an edge-preserving smoothing operator and has better behaviors near edges than the bilateral filter. Our reconstruction method is consist of two steps. First, we propose two cost functions which could be computed efficiently and thus obtain two different images. Second, the guided filter is used with these two obtained images for efficient edge-preserving filtering, and one image is used as the guidance image, the other one is used as a filtered image in the guided filter. In our reconstruction algorithm, we can obtain more details by introducing guided filter. We compare our reconstruction algorithm with some competitive MRI reconstruction techniques in terms of PSNR and visual quality. Simulation results are given to show the performance of our new method.

Medical image reconstruction. A conceptual tutorial

International Nuclear Information System (INIS)

Zeng, Gengsheng Lawrence

2010-01-01

''Medical Image Reconstruction: A Conceptual Tutorial'' introduces the classical and modern image reconstruction technologies, such as two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. This book presents both analytical and iterative methods of these technologies and their applications in X-ray CT (computed tomography), SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging). Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich's cone-beam filtered backprojection algorithm, and reconstruction with highly undersampled data with l 0 -minimization are also included. (orig.)

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.

An efficient algorithm for reconstruction of spect images in the presence of spatially varying attenuation

International Nuclear Information System (INIS)

Zeeberg, B.R.; Bacharach, S.; Carson, R.; Green, M.V.; Larson, S.M.; Soucaille, J.F.

1985-01-01

An algorithm is presented which permits the reconstruction of SPECT images in the presence of spatially varying attenuation. The algorithm considers the spatially variant attenuation as a perturbation of the constant attenuation case and computes a reconstructed image and a correction image to estimate the effects of this perturbation. The corrected image will be computed from these two images and is of comparable quality both visually and quantitatively to those simulated for zero or constant attenuation taken as standard reference images. In addition, the algorithm is time efficient, in that the time required is approximately 2.5 times that for a standard convolution-back projection algorithm

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

Robust sparse image reconstruction of radio interferometric observations with PURIFY

Science.gov (United States)

Pratley, Luke; McEwen, Jason D.; d'Avezac, Mayeul; Carrillo, Rafael E.; Onose, Alexandru; Wiaux, Yves

2018-01-01

Next-generation radio interferometers, such as the Square Kilometre Array, will revolutionize our understanding of the Universe through their unprecedented sensitivity and resolution. However, to realize these goals significant challenges in image and data processing need to be overcome. The standard methods in radio interferometry for reconstructing images, such as CLEAN, have served the community well over the last few decades and have survived largely because they are pragmatic. However, they produce reconstructed interferometric images that are limited in quality and scalability for big data. In this work, we apply and evaluate alternative interferometric reconstruction methods that make use of state-of-the-art sparse image reconstruction algorithms motivated by compressive sensing, which have been implemented in the PURIFY software package. In particular, we implement and apply the proximal alternating direction method of multipliers algorithm presented in a recent article. First, we assess the impact of the interpolation kernel used to perform gridding and degridding on sparse image reconstruction. We find that the Kaiser-Bessel interpolation kernel performs as well as prolate spheroidal wave functions while providing a computational saving and an analytic form. Secondly, we apply PURIFY to real interferometric observations from the Very Large Array and the Australia Telescope Compact Array and find that images recovered by PURIFY are of higher quality than those recovered by CLEAN. Thirdly, we discuss how PURIFY reconstructions exhibit additional advantages over those recovered by CLEAN. The latest version of PURIFY, with developments presented in this work, is made publicly available.

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

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

Energy Technology Data Exchange (ETDEWEB)

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.)

SU-E-J-218: Evaluation of CT Images Created Using a New Metal Artifact Reduction Reconstruction Algorithm for Radiation Therapy Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Niemkiewicz, J; Palmiotti, A; Miner, M; Stunja, L; Bergene, J [Lehigh Valley Health Network, Allentown, PA (United States)

2014-06-01

Purpose: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU values were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation

SU-E-J-218: Evaluation of CT Images Created Using a New Metal Artifact Reduction Reconstruction Algorithm for Radiation Therapy Treatment Planning

International Nuclear Information System (INIS)

Niemkiewicz, J; Palmiotti, A; Miner, M; Stunja, L; Bergene, J

2014-01-01

Purpose: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU values were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation

3.5D dynamic PET image reconstruction incorporating kinetics-based clusters

International Nuclear Information System (INIS)

Lu Lijun; Chen Wufan; Karakatsanis, Nicolas A; Rahmim, Arman; Tang Jing

2012-01-01

Standard 3D dynamic positron emission tomographic (PET) imaging consists of independent image reconstructions of individual frames followed by application of appropriate kinetic model to the time activity curves at the voxel or region-of-interest (ROI). The emerging field of 4D PET reconstruction, by contrast, seeks to move beyond this scheme and incorporate information from multiple frames within the image reconstruction task. Here we propose a novel reconstruction framework aiming to enhance quantitative accuracy of parametric images via introduction of priors based on voxel kinetics, as generated via clustering of preliminary reconstructed dynamic images to define clustered neighborhoods of voxels with similar kinetics. This is then followed by straightforward maximum a posteriori (MAP) 3D PET reconstruction as applied to individual frames; and as such the method is labeled ‘3.5D’ image reconstruction. The use of cluster-based priors has the advantage of further enhancing quantitative performance in dynamic PET imaging, because: (a) there are typically more voxels in clusters than in conventional local neighborhoods, and (b) neighboring voxels with distinct kinetics are less likely to be clustered together. Using realistic simulated 11 C-raclopride dynamic PET data, the quantitative performance of the proposed method was investigated. Parametric distribution-volume (DV) and DV ratio (DVR) images were estimated from dynamic image reconstructions using (a) maximum-likelihood expectation maximization (MLEM), and MAP reconstructions using (b) the quadratic prior (QP-MAP), (c) the Green prior (GP-MAP) and (d, e) two proposed cluster-based priors (CP-U-MAP and CP-W-MAP), followed by graphical modeling, and were qualitatively and quantitatively compared for 11 ROIs. Overall, the proposed dynamic PET reconstruction methodology resulted in substantial visual as well as quantitative accuracy improvements (in terms of noise versus bias performance) for parametric DV

Motion-map constrained image reconstruction (MCIR): Application to four-dimensional cone-beam computed tomography

Energy Technology Data Exchange (ETDEWEB)

Park, Justin C. [Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92093 and Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093 (United States); Kim, Jin Sung [Department of Radiation Oncology, Samsung Medical Center, Seoul 135-710 (Korea, Republic of); Park, Sung Ho [Department of Medical Physics, Asan Medical Center, College of Medicine, University of Ulsan, Seoul 138-736 (Korea, Republic of); Liu, Zhaowei [Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093 (United States); Song, Bongyong; Song, William Y. [Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92093 (United States)

2013-12-15

Purpose: Utilization of respiratory correlated four-dimensional cone-beam computed tomography (4DCBCT) has enabled verification of internal target motion and volume immediately prior to treatment. However, with current standard CBCT scan, 4DCBCT poses challenge for reconstruction due to the fact that multiple phase binning leads to insufficient number of projection data to reconstruct and thus cause streaking artifacts. The purpose of this study is to develop a novel 4DCBCT reconstruction algorithm framework called motion-map constrained image reconstruction (MCIR), that allows reconstruction of high quality and high phase resolution 4DCBCT images with no more than the imaging dose as well as projections used in a standard free breathing 3DCBCT (FB-3DCBCT) scan.Methods: The unknown 4DCBCT volume at each phase was mathematically modeled as a combination of FB-3DCBCT and phase-specific update vector which has an associated motion-map matrix. The motion-map matrix, which is the key innovation of the MCIR algorithm, was defined as the matrix that distinguishes voxels that are moving from stationary ones. This 4DCBCT model was then reconstructed with compressed sensing (CS) reconstruction framework such that the voxels with high motion would be aggressively updated by the phase-wise sorted projections and the voxels with less motion would be minimally updated to preserve the FB-3DCBCT. To evaluate the performance of our proposed MCIR algorithm, we evaluated both numerical phantoms and a lung cancer patient. The results were then compared with the (1) clinical FB-3DCBCT reconstructed using the FDK, (2) 4DCBCT reconstructed using the FDK, and (3) 4DCBCT reconstructed using the well-known prior image constrained compressed sensing (PICCS).Results: Examination of the MCIR algorithm showed that high phase-resolved 4DCBCT with sets of up to 20 phases using a typical FB-3DCBCT scan could be reconstructed without compromising the image quality. Moreover, in comparison with

Motion-map constrained image reconstruction (MCIR): Application to four-dimensional cone-beam computed tomography

International Nuclear Information System (INIS)

Park, Justin C.; Kim, Jin Sung; Park, Sung Ho; Liu, Zhaowei; Song, Bongyong; Song, William Y.

2013-01-01

Purpose: Utilization of respiratory correlated four-dimensional cone-beam computed tomography (4DCBCT) has enabled verification of internal target motion and volume immediately prior to treatment. However, with current standard CBCT scan, 4DCBCT poses challenge for reconstruction due to the fact that multiple phase binning leads to insufficient number of projection data to reconstruct and thus cause streaking artifacts. The purpose of this study is to develop a novel 4DCBCT reconstruction algorithm framework called motion-map constrained image reconstruction (MCIR), that allows reconstruction of high quality and high phase resolution 4DCBCT images with no more than the imaging dose as well as projections used in a standard free breathing 3DCBCT (FB-3DCBCT) scan.Methods: The unknown 4DCBCT volume at each phase was mathematically modeled as a combination of FB-3DCBCT and phase-specific update vector which has an associated motion-map matrix. The motion-map matrix, which is the key innovation of the MCIR algorithm, was defined as the matrix that distinguishes voxels that are moving from stationary ones. This 4DCBCT model was then reconstructed with compressed sensing (CS) reconstruction framework such that the voxels with high motion would be aggressively updated by the phase-wise sorted projections and the voxels with less motion would be minimally updated to preserve the FB-3DCBCT. To evaluate the performance of our proposed MCIR algorithm, we evaluated both numerical phantoms and a lung cancer patient. The results were then compared with the (1) clinical FB-3DCBCT reconstructed using the FDK, (2) 4DCBCT reconstructed using the FDK, and (3) 4DCBCT reconstructed using the well-known prior image constrained compressed sensing (PICCS).Results: Examination of the MCIR algorithm showed that high phase-resolved 4DCBCT with sets of up to 20 phases using a typical FB-3DCBCT scan could be reconstructed without compromising the image quality. Moreover, in comparison with

Tomographic image reconstruction using training images

DEFF Research Database (Denmark)

Soltani, Sara; Andersen, Martin Skovgaard; Hansen, Per Christian

2017-01-01

We describe and examine an algorithm for tomographic image reconstruction where prior knowledge about the solution is available in the form of training images. We first construct a non-negative dictionary based on prototype elements from the training images; this problem is formulated within...

Image reconstruction in computerized tomography using the convolution method

International Nuclear Information System (INIS)

Oliveira Rebelo, A.M. de.

1984-03-01

In the present work an algoritin was derived, using the analytical convolution method (filtered back-projection) for two-dimensional or three-dimensional image reconstruction in computerized tomography applied to non-destructive testing and to the medical use. This mathematical model is based on the analytical Fourier transform method for image reconstruction. This model consists of a discontinuous system formed by an NxN array of cells (pixels). The attenuation in the object under study of a colimated gamma ray beam has been determined for various positions and incidence angles (projections) in terms of the interaction of the beam with the intercepted pixels. The contribution of each pixel to beam attenuation was determined using the weight function W ij which was used for simulated tests. Simulated tests using standard objects with attenuation coefficients in the range of 0,2 to 0,7 cm -1 were carried out using cell arrays of up to 25x25. One application was carried out in the medical area simulating image reconstruction of an arm phantom with attenuation coefficients in the range of 0,2 to 0,5 cm -1 using cell arrays of 41x41. The simulated results show that, in objects with a great number of interfaces and great variations of attenuation coefficients at these interfaces, a good reconstruction is obtained with the number of projections equal to the reconstruction matrix dimension. A good reconstruction is otherwise obtained with fewer projections. (author) [pt

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

Method for position emission mammography image reconstruction

Science.gov (United States)

Smith, Mark Frederick

2004-10-12

An image reconstruction method comprising accepting coincidence datat from either a data file or in real time from a pair of detector heads, culling event data that is outside a desired energy range, optionally saving the desired data for each detector position or for each pair of detector pixels on the two detector heads, and then reconstructing the image either by backprojection image reconstruction or by iterative image reconstruction. In the backprojection image reconstruction mode, rays are traced between centers of lines of response (LOR's), counts are then either allocated by nearest pixel interpolation or allocated by an overlap method and then corrected for geometric effects and attenuation and the data file updated. If the iterative image reconstruction option is selected, one implementation is to compute a grid Siddon retracing, and to perform maximum likelihood expectation maiximization (MLEM) computed by either: a) tracing parallel rays between subpixels on opposite detector heads; or b) tracing rays between randomized endpoint locations on opposite detector heads.

Use of an object model in three dimensional image reconstruction. Application in medical imaging

International Nuclear Information System (INIS)

Delageniere-Guillot, S.

1993-02-01

Threedimensional image reconstruction from projections corresponds to a set of techniques which give information on the inner structure of the studied object. These techniques are mainly used in medical imaging or in non destructive evaluation. Image reconstruction is an ill-posed problem. So the inversion has to be regularized. This thesis deals with the introduction of a priori information within the reconstruction algorithm. The knowledge is introduced through an object model. The proposed scheme is applied to the medical domain for cone beam geometry. We address two specific problems. First, we study the reconstruction of high contrast objects. This can be applied to bony morphology (bone/soft tissue) or to angiography (vascular structures opacified by injection of contrast agent). With noisy projections, the filtering steps of standard methods tend to smooth the natural transitions of the investigated object. In order to regularize the reconstruction but to keep contrast, we introduce a model of classes which involves the Markov random fields theory. We develop a reconstruction scheme: analytic reconstruction-reprojection. Then, we address the case of an object changing during the acquisition. This can be applied to angiography when the contrast agent is moving through the vascular tree. The problem is then stated as a dynamic reconstruction. We define an evolution AR model and we use an algebraic reconstruction method. We represent the object at a particular moment as an intermediary state between the state of the object at the beginning and at the end of the acquisition. We test both methods on simulated and real data, and we prove how the use of an a priori model can improve the results. (author)

A novel data processing technique for image reconstruction of penumbral imaging

Science.gov (United States)

Xie, Hongwei; Li, Hongyun; Xu, Zeping; Song, Guzhou; Zhang, Faqiang; Zhou, Lin

2011-06-01

CT image reconstruction technique was applied to the data processing of the penumbral imaging. Compared with other traditional processing techniques for penumbral coded pinhole image such as Wiener, Lucy-Richardson and blind technique, this approach is brand new. In this method, the coded aperture processing method was used for the first time independent to the point spread function of the image diagnostic system. In this way, the technical obstacles was overcome in the traditional coded pinhole image processing caused by the uncertainty of point spread function of the image diagnostic system. Then based on the theoretical study, the simulation of penumbral imaging and image reconstruction was carried out to provide fairly good results. While in the visible light experiment, the point source of light was used to irradiate a 5mm×5mm object after diffuse scattering and volume scattering. The penumbral imaging was made with aperture size of ~20mm. Finally, the CT image reconstruction technique was used for image reconstruction to provide a fairly good reconstruction result.

High-speed reconstruction of compressed images

Science.gov (United States)

Cox, Jerome R., Jr.; Moore, Stephen M.

1990-07-01

A compression scheme is described that allows high-definition radiological images with greater than 8-bit intensity resolution to be represented by 8-bit pixels. Reconstruction of the images with their original intensity resolution can be carried out by means of a pipeline architecture suitable for compact, high-speed implementation. A reconstruction system is described that can be fabricated according to this approach and placed between an 8-bit display buffer and the display's video system thereby allowing contrast control of images at video rates. Results for 50 CR chest images are described showing that error-free reconstruction of the original 10-bit CR images can be achieved.

MO-DE-207A-09: Low-Dose CT Image Reconstruction Via Learning From Different Patient Normal-Dose Images

Energy Technology Data Exchange (ETDEWEB)

Han, H; Xing, L [Stanford University, Palo Alto, CA (United States); Liang, Z [Stony Brook University, Stony Brook, NY (United States)

2016-06-15

Purpose: To investigate a novel low-dose CT (LdCT) image reconstruction strategy for lung CT imaging in radiation therapy. Methods: The proposed approach consists of four steps: (1) use the traditional filtered back-projection (FBP) method to reconstruct the LdCT image; (2) calculate structure similarity (SSIM) index between the FBP-reconstructed LdCT image and a set of normal-dose CT (NdCT) images, and select the NdCT image with the highest SSIM as the learning source; (3) segment the NdCT source image into lung and outside tissue regions via simple thresholding, and adopt multiple linear regression to learn high-order Markov random field (MRF) pattern for each tissue region in the NdCT source image; (4) segment the FBP-reconstructed LdCT image into lung and outside regions as well, and apply the learnt MRF prior in each tissue region for statistical iterative reconstruction of the LdCT image following the penalized weighted least squares (PWLS) framework. Quantitative evaluation of the reconstructed images was based on the signal-to-noise ratio (SNR), local binary pattern (LBP) and histogram of oriented gradients (HOG) metrics. Results: It was observed that lung and outside tissue regions have different MRF patterns predicted from the NdCT. Visual inspection showed that our method obviously outperformed the traditional FBP method. Comparing with the region-smoothing PWLS method, our method has, in average, 13% increase in SNR, 15% decrease in LBP difference, and 12% decrease in HOG difference from reference standard for all regions of interest, which indicated the superior performance of the proposed method in terms of image resolution and texture preservation. Conclusion: We proposed a novel LdCT image reconstruction method by learning similar image characteristics from a set of NdCT images, and the to-be-learnt NdCT image does not need to be scans from the same subject. This approach is particularly important for enhancing image quality in radiation therapy.

MO-DE-207A-09: Low-Dose CT Image Reconstruction Via Learning From Different Patient Normal-Dose Images

International Nuclear Information System (INIS)

Han, H; Xing, L; Liang, Z

2016-01-01

Purpose: To investigate a novel low-dose CT (LdCT) image reconstruction strategy for lung CT imaging in radiation therapy. Methods: The proposed approach consists of four steps: (1) use the traditional filtered back-projection (FBP) method to reconstruct the LdCT image; (2) calculate structure similarity (SSIM) index between the FBP-reconstructed LdCT image and a set of normal-dose CT (NdCT) images, and select the NdCT image with the highest SSIM as the learning source; (3) segment the NdCT source image into lung and outside tissue regions via simple thresholding, and adopt multiple linear regression to learn high-order Markov random field (MRF) pattern for each tissue region in the NdCT source image; (4) segment the FBP-reconstructed LdCT image into lung and outside regions as well, and apply the learnt MRF prior in each tissue region for statistical iterative reconstruction of the LdCT image following the penalized weighted least squares (PWLS) framework. Quantitative evaluation of the reconstructed images was based on the signal-to-noise ratio (SNR), local binary pattern (LBP) and histogram of oriented gradients (HOG) metrics. Results: It was observed that lung and outside tissue regions have different MRF patterns predicted from the NdCT. Visual inspection showed that our method obviously outperformed the traditional FBP method. Comparing with the region-smoothing PWLS method, our method has, in average, 13% increase in SNR, 15% decrease in LBP difference, and 12% decrease in HOG difference from reference standard for all regions of interest, which indicated the superior performance of the proposed method in terms of image resolution and texture preservation. Conclusion: We proposed a novel LdCT image reconstruction method by learning similar image characteristics from a set of NdCT images, and the to-be-learnt NdCT image does not need to be scans from the same subject. This approach is particularly important for enhancing image quality in radiation therapy.

A low-count reconstruction algorithm for Compton-based prompt gamma imaging

Science.gov (United States)

Huang, Hsuan-Ming; Liu, Chih-Chieh; Jan, Meei-Ling; Lee, Ming-Wei

2018-04-01

The Compton camera is an imaging device which has been proposed to detect prompt gammas (PGs) produced by proton–nuclear interactions within tissue during proton beam irradiation. Compton-based PG imaging has been developed to verify proton ranges because PG rays, particularly characteristic ones, have strong correlations with the distribution of the proton dose. However, accurate image reconstruction from characteristic PGs is challenging because the detector efficiency and resolution are generally low. Our previous study showed that point spread functions can be incorporated into the reconstruction process to improve image resolution. In this study, we proposed a low-count reconstruction algorithm to improve the image quality of a characteristic PG emission by pooling information from other characteristic PG emissions. PGs were simulated from a proton beam irradiated on a water phantom, and a two-stage Compton camera was used for PG detection. The results show that the image quality of the reconstructed characteristic PG emission is improved with our proposed method in contrast to the standard reconstruction method using events from only one characteristic PG emission. For the 4.44 MeV PG rays, both methods can be used to predict the positions of the peak and the distal falloff with a mean accuracy of 2 mm. Moreover, only the proposed method can improve the estimated positions of the peak and the distal falloff of 5.25 MeV PG rays, and a mean accuracy of 2 mm can be reached.

Comparison of different reconstruction algorithms for three-dimensional ultrasound imaging in a neurosurgical setting.

Science.gov (United States)

Miller, D; Lippert, C; Vollmer, F; Bozinov, O; Benes, L; Schulte, D M; Sure, U

2012-09-01

Freehand three-dimensional ultrasound imaging (3D-US) is increasingly used in image-guided surgery. During image acquisition, a set of B-scans is acquired that is distributed in a non-parallel manner over the area of interest. Reconstructing these images into a regular array allows 3D visualization. However, the reconstruction process may introduce artefacts and may therefore reduce image quality. The aim of the study is to compare different algorithms with respect to image quality and diagnostic value for image guidance in neurosurgery. 3D-US data sets were acquired during surgery of various intracerebral lesions using an integrated ultrasound-navigation device. They were stored for post-hoc evaluation. Five different reconstruction algorithms, a standard multiplanar reconstruction with interpolation (MPR), a pixel nearest neighbour method (PNN), a voxel nearest neighbour method (VNN) and two voxel based distance-weighted algorithms (VNN2 and DW) were tested with respect to image quality and artefact formation. The capability of the algorithm to fill gaps within the sample volume was investigated and a clinical evaluation with respect to the diagnostic value of the reconstructed images was performed. MPR was significantly worse than the other algorithms in filling gaps. In an image subtraction test, VNN2 and DW reliably reconstructed images even if large amounts of data were missing. However, the quality of the reconstruction improved, if data acquisition was performed in a structured manner. When evaluating the diagnostic value of reconstructed axial, sagittal and coronal views, VNN2 and DW were judged to be significantly better than MPR and VNN. VNN2 and DW could be identified as robust algorithms that generate reconstructed US images with a high diagnostic value. These algorithms improve the utility and reliability of 3D-US imaging during intraoperative navigation. Copyright © 2012 John Wiley & Sons, Ltd.

3-D image reconstruction in radiology

International Nuclear Information System (INIS)

Grangeat, P.

1999-01-01

In this course, we present highlights on fully 3-D image reconstruction algorithms used in 3-D X-ray Computed Tomography (3-D-CT) and 3-D Rotational Radiography (3-D-RR). We first consider the case of spiral CT with a one-row detector. Starting from the 2-D fan-beam inversion formula for a circular trajectory, we introduce spiral CT 3-D image reconstruction algorithm using axial interpolation for each transverse slice. In order to improve the X-ray detection efficiency and to speed the acquisition process, the future is to use multi-row detectors associated with small angle cone-beam geometry. The generalization of the 2-D fan-beam image reconstruction algorithm to cone beam defined direct inversion formula referred as Feldkamp's algorithm for a circular trajectory and Wang's algorithm for a spiral trajectory. However, large area detectors does exist such as Radiological Image Intensifiers or in a near future solid state detectors. To get a larger zoom effect, it defines a cone-beam geometry associated with a large aperture angle. For this case, we introduce indirect image reconstruction algorithm by plane re-binning in the Radon domain. We will present some results from a prototype MORPHOMETER device using the RADON reconstruction software. Lastly, we consider the special case of 3-D Rotational Digital Subtraction Angiography with a restricted number of views. We introduce constraint optimization algorithm using quadratic, entropic or half-quadratic constraints. Generalized ART (Algebraic Reconstruction Technique) iterative reconstruction algorithm can be derived from the Bregman algorithm. We present reconstructed vascular trees from a prototype MORPHOMETER device. (author)

Blockwise conjugate gradient methods for image reconstruction in volumetric CT.

Science.gov (United States)

Qiu, W; Titley-Peloquin, D; Soleimani, M

2012-11-01

Cone beam computed tomography (CBCT) enables volumetric image reconstruction from 2D projection data and plays an important role in image guided radiation therapy (IGRT). Filtered back projection is still the most frequently used algorithm in applications. The algorithm discretizes the scanning process (forward projection) into a system of linear equations, which must then be solved to recover images from measured projection data. The conjugate gradients (CG) algorithm and its variants can be used to solve (possibly regularized) linear systems of equations Ax=b and linear least squares problems minx∥b-Ax∥2, especially when the matrix A is very large and sparse. Their applications can be found in a general CT context, but in tomography problems (e.g. CBCT reconstruction) they have not widely been used. Hence, CBCT reconstruction using the CG-type algorithm LSQR was implemented and studied in this paper. In CBCT reconstruction, the main computational challenge is that the matrix A usually is very large, and storing it in full requires an amount of memory well beyond the reach of commodity computers. Because of these memory capacity constraints, only a small fraction of the weighting matrix A is typically used, leading to a poor reconstruction. In this paper, to overcome this difficulty, the matrix A is partitioned and stored blockwise, and blockwise matrix-vector multiplications are implemented within LSQR. This implementation allows us to use the full weighting matrix A for CBCT reconstruction without further enhancing computer standards. Tikhonov regularization can also be implemented in this fashion, and can produce significant improvement in the reconstructed images. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Tomographic image reconstruction using Artificial Neural Networks

International Nuclear Information System (INIS)

Paschalis, P.; Giokaris, N.D.; Karabarbounis, A.; Loudos, G.K.; Maintas, D.; Papanicolas, C.N.; Spanoudaki, V.; Tsoumpas, Ch.; Stiliaris, E.

2004-01-01

A new image reconstruction technique based on the usage of an Artificial Neural Network (ANN) is presented. The most crucial factor in designing such a reconstruction system is the network architecture and the number of the input projections needed to reconstruct the image. Although the training phase requires a large amount of input samples and a considerable CPU time, the trained network is characterized by simplicity and quick response. The performance of this ANN is tested using several image patterns. It is intended to be used together with a phantom rotating table and the γ-camera of IASA for SPECT image reconstruction

Model-Based Reconstructive Elasticity Imaging Using Ultrasound

Directory of Open Access Journals (Sweden)

Salavat R. Aglyamov

2007-01-01

Full Text Available Elasticity imaging is a reconstructive imaging technique where tissue motion in response to mechanical excitation is measured using modern imaging systems, and the estimated displacements are then used to reconstruct the spatial distribution of Young's modulus. Here we present an ultrasound elasticity imaging method that utilizes the model-based technique for Young's modulus reconstruction. Based on the geometry of the imaged object, only one axial component of the strain tensor is used. The numerical implementation of the method is highly efficient because the reconstruction is based on an analytic solution of the forward elastic problem. The model-based approach is illustrated using two potential clinical applications: differentiation of liver hemangioma and staging of deep venous thrombosis. Overall, these studies demonstrate that model-based reconstructive elasticity imaging can be used in applications where the geometry of the object and the surrounding tissue is somewhat known and certain assumptions about the pathology can be made.

Fast image reconstruction for Compton camera using stochastic origin ensemble approach.

Science.gov (United States)

Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

2011-01-01

Compton camera has been proposed as a potential imaging tool in astronomy, industry, homeland security, and medical diagnostics. Due to the inherent geometrical complexity of Compton camera data, image reconstruction of distributed sources can be ineffective and/or time-consuming when using standard techniques such as filtered backprojection or maximum likelihood-expectation maximization (ML-EM). In this article, the authors demonstrate a fast reconstruction of Compton camera data using a novel stochastic origin ensembles (SOE) approach based on Markov chains. During image reconstruction, the origins of the measured events are randomly assigned to locations on conical surfaces, which are the Compton camera analogs of lines-of-responses in PET. Therefore, the image is defined as an ensemble of origin locations of all possible event origins. During the course of reconstruction, the origins of events are stochastically moved and the acceptance of the new event origin is determined by the predefined acceptance probability, which is proportional to the change in event density. For example, if the event density at the new location is higher than in the previous location, the new position is always accepted. After several iterations, the reconstructed distribution of origins converges to a quasistationary state which can be voxelized and displayed. Comparison with the list-mode ML-EM reveals that the postfiltered SOE algorithm has similar performance in terms of image quality while clearly outperforming ML-EM in relation to reconstruction time. In this study, the authors have implemented and tested a new image reconstruction algorithm for the Compton camera based on the stochastic origin ensembles with Markov chains. The algorithm uses list-mode data, is parallelizable, and can be used for any Compton camera geometry. SOE algorithm clearly outperforms list-mode ML-EM for simple Compton camera geometry in terms of reconstruction time. The difference in computational time

Surface topography characterization using 3D stereoscopic reconstruction of SEM images

Science.gov (United States)

Vedantha Krishna, Amogh; Flys, Olena; Reddy, Vijeth V.; Rosén, B. G.

2018-06-01

A major drawback of the optical microscope is its limitation to resolve finer details. Many microscopes have been developed to overcome the limitations set by the diffraction of visible light. The scanning electron microscope (SEM) is one such alternative: it uses electrons for imaging, which have much smaller wavelength than photons. As a result high magnification with superior image resolution can be achieved. However, SEM generates 2D images which provide limited data for surface measurements and analysis. Often many research areas require the knowledge of 3D structures as they contribute to a comprehensive understanding of microstructure by allowing effective measurements and qualitative visualization of the samples under study. For this reason, stereo photogrammetry technique is employed to convert SEM images into 3D measurable data. This paper aims to utilize a stereoscopic reconstruction technique as a reliable method for characterization of surface topography. Reconstructed results from SEM images are compared with coherence scanning interferometer (CSI) results obtained by measuring a roughness reference standard sample. This paper presents a method to select the most robust/consistent surface texture parameters that are insensitive to the uncertainties involved in the reconstruction technique itself. Results from the two-stereoscopic reconstruction algorithms are also documented in this paper.

3D Reconstruction and Standardization of the Rat Vibrissal Cortex for Precise Registration of Single Neuron Morphology

Science.gov (United States)

Egger, Robert; Narayanan, Rajeevan T.; Helmstaedter, Moritz; de Kock, Christiaan P. J.; Oberlaender, Marcel

2012-01-01

The three-dimensional (3D) structure of neural circuits is commonly studied by reconstructing individual or small groups of neurons in separate preparations. Investigation of structural organization principles or quantification of dendritic and axonal innervation thus requires integration of many reconstructed morphologies into a common reference frame. Here we present a standardized 3D model of the rat vibrissal cortex and introduce an automated registration tool that allows for precise placement of single neuron reconstructions. We (1) developed an automated image processing pipeline to reconstruct 3D anatomical landmarks, i.e., the barrels in Layer 4, the pia and white matter surfaces and the blood vessel pattern from high-resolution images, (2) quantified these landmarks in 12 different rats, (3) generated an average 3D model of the vibrissal cortex and (4) used rigid transformations and stepwise linear scaling to register 94 neuron morphologies, reconstructed from in vivo stainings, to the standardized cortex model. We find that anatomical landmarks vary substantially across the vibrissal cortex within an individual rat. In contrast, the 3D layout of the entire vibrissal cortex remains remarkably preserved across animals. This allows for precise registration of individual neuron reconstructions with approximately 30 µm accuracy. Our approach could be used to reconstruct and standardize other anatomically defined brain areas and may ultimately lead to a precise digital reference atlas of the rat brain. PMID:23284282

Reconstructed Image Spatial Resolution of Multiple Coincidences Compton Imager

Science.gov (United States)

Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

2010-02-01

We study the multiple coincidences Compton imager (MCCI) which is based on a simultaneous acquisition of several photons emitted in cascade from a single nuclear decay. Theoretically, this technique should provide a major improvement in localization of a single radioactive source as compared to a standard Compton camera. In this work, we investigated the performance and limitations of MCCI using Monte Carlo computer simulations. Spatial resolutions of the reconstructed point source have been studied as a function of the MCCI parameters, including geometrical dimensions and detector characteristics such as materials, energy and spatial resolutions.

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

Science.gov (United States)

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

Wavelet/scalar quantization compression standard for fingerprint images

Energy Technology Data Exchange (ETDEWEB)

Brislawn, C.M.

1996-06-12

US Federal Bureau of Investigation (FBI) has recently formulated a national standard for digitization and compression of gray-scale fingerprint images. Fingerprints are scanned at a spatial resolution of 500 dots per inch, with 8 bits of gray-scale resolution. The compression algorithm for the resulting digital images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition (wavelet/scalar quantization method). The FBI standard produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. The compression standard specifies a class of potential encoders and a universal decoder with sufficient generality to reconstruct compressed images produced by any compliant encoder, allowing flexibility for future improvements in encoder technology. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations.

Image reconstruction for a Positron Emission Tomograph optimized for breast cancer imaging

International Nuclear Information System (INIS)

Virador, Patrick R.G.

2000-01-01

The author performs image reconstruction for a novel Positron Emission Tomography camera that is optimized for breast cancer imaging. This work addresses for the first time, the problem of fully-3D, tomographic reconstruction using a septa-less, stationary, (i.e. no rotation or linear motion), and rectangular camera whose Field of View (FOV) encompasses the entire volume enclosed by detector modules capable of measuring Depth of Interaction (DOI) information. The camera is rectangular in shape in order to accommodate breasts of varying sizes while allowing for soft compression of the breast during the scan. This non-standard geometry of the camera exacerbates two problems: (a) radial elongation due to crystal penetration and (b) reconstructing images from irregularly sampled data. Packing considerations also give rise to regions in projection space that are not sampled which lead to missing information. The author presents new Fourier Methods based image reconstruction algorithms that incorporate DOI information and accommodate the irregular sampling of the camera in a consistent manner by defining lines of responses (LORs) between the measured interaction points instead of rebinning the events into predefined crystal face LORs which is the only other method to handle DOI information proposed thus far. The new procedures maximize the use of the increased sampling provided by the DOI while minimizing interpolation in the data. The new algorithms use fixed-width evenly spaced radial bins in order to take advantage of the speed of the Fast Fourier Transform (FFT), which necessitates the use of irregular angular sampling in order to minimize the number of unnormalizable Zero-Efficiency Bins (ZEBs). In order to address the persisting ZEBs and the issue of missing information originating from packing considerations, the algorithms (a) perform nearest neighbor smoothing in 2D in the radial bins (b) employ a semi-iterative procedure in order to estimate the unsampled data

Image reconstruction for a Positron Emission Tomograph optimized for breast cancer imaging

Energy Technology Data Exchange (ETDEWEB)

Virador, Patrick R.G. [Univ. of California, Berkeley, CA (United States)

2000-04-01

The author performs image reconstruction for a novel Positron Emission Tomography camera that is optimized for breast cancer imaging. This work addresses for the first time, the problem of fully-3D, tomographic reconstruction using a septa-less, stationary, (i.e. no rotation or linear motion), and rectangular camera whose Field of View (FOV) encompasses the entire volume enclosed by detector modules capable of measuring Depth of Interaction (DOI) information. The camera is rectangular in shape in order to accommodate breasts of varying sizes while allowing for soft compression of the breast during the scan. This non-standard geometry of the camera exacerbates two problems: (a) radial elongation due to crystal penetration and (b) reconstructing images from irregularly sampled data. Packing considerations also give rise to regions in projection space that are not sampled which lead to missing information. The author presents new Fourier Methods based image reconstruction algorithms that incorporate DOI information and accommodate the irregular sampling of the camera in a consistent manner by defining lines of responses (LORs) between the measured interaction points instead of rebinning the events into predefined crystal face LORs which is the only other method to handle DOI information proposed thus far. The new procedures maximize the use of the increased sampling provided by the DOI while minimizing interpolation in the data. The new algorithms use fixed-width evenly spaced radial bins in order to take advantage of the speed of the Fast Fourier Transform (FFT), which necessitates the use of irregular angular sampling in order to minimize the number of unnormalizable Zero-Efficiency Bins (ZEBs). In order to address the persisting ZEBs and the issue of missing information originating from packing considerations, the algorithms (a) perform nearest neighbor smoothing in 2D in the radial bins (b) employ a semi-iterative procedure in order to estimate the unsampled data

Heuristic optimization in penumbral image for high resolution reconstructed image

International Nuclear Information System (INIS)

Azuma, R.; Nozaki, S.; Fujioka, S.; Chen, Y. W.; Namihira, Y.

2010-01-01

Penumbral imaging is a technique which uses the fact that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. The size of the penumbral image on the detector can be mathematically determined as its aperture size, object size, and magnification. Conventional reconstruction methods are very sensitive to noise. On the other hand, the heuristic reconstruction method is very tolerant of noise. However, the aperture size influences the accuracy and resolution of the reconstructed image. In this article, we propose the optimization of the aperture size for the neutron penumbral imaging.

Reconstruction Algorithms in Undersampled AFM Imaging

DEFF Research Database (Denmark)

Arildsen, Thomas; Oxvig, Christian Schou; Pedersen, Patrick Steffen

2016-01-01

This paper provides a study of spatial undersampling in atomic force microscopy (AFM) imaging followed by different image reconstruction techniques based on sparse approximation as well as interpolation. The main reasons for using undersampling is that it reduces the path length and thereby...... the scanning time as well as the amount of interaction between the AFM probe and the specimen. It can easily be applied on conventional AFM hardware. Due to undersampling, it is then necessary to further process the acquired image in order to reconstruct an approximation of the image. Based on real AFM cell...... images, our simulations reveal that using a simple raster scanning pattern in combination with conventional image interpolation performs very well. Moreover, this combination enables a reduction by a factor 10 of the scanning time while retaining an average reconstruction quality around 36 dB PSNR...

Impact of PET/CT image reconstruction methods and liver uptake normalization strategies on quantitative image analysis

Energy Technology Data Exchange (ETDEWEB)

Kuhnert, Georg; Sterzer, Sergej; Kahraman, Deniz; Dietlein, Markus; Drzezga, Alexander; Kobe, Carsten [University Hospital of Cologne, Department of Nuclear Medicine, Cologne (Germany); Boellaard, Ronald [VU University Medical Centre, Department of Radiology and Nuclear Medicine, Amsterdam (Netherlands); Scheffler, Matthias; Wolf, Juergen [University Hospital of Cologne, Lung Cancer Group Cologne, Department I of Internal Medicine, Center for Integrated Oncology Cologne Bonn, Cologne (Germany)

2016-02-15

In oncological imaging using PET/CT, the standardized uptake value has become the most common parameter used to measure tracer accumulation. The aim of this analysis was to evaluate ultra high definition (UHD) and ordered subset expectation maximization (OSEM) PET/CT reconstructions for their potential impact on quantification. We analyzed 40 PET/CT scans of lung cancer patients who had undergone PET/CT. Standardized uptake values corrected for body weight (SUV) and lean body mass (SUL) were determined in the single hottest lesion in the lung and normalized to the liver for UHD and OSEM reconstruction. Quantitative uptake values and their normalized ratios for the two reconstruction settings were compared using the Wilcoxon test. The distribution of quantitative uptake values and their ratios in relation to the reconstruction method used were demonstrated in the form of frequency distribution curves, box-plots and scatter plots. The agreement between OSEM and UHD reconstructions was assessed through Bland-Altman analysis. A significant difference was observed after OSEM and UHD reconstruction for SUV and SUL data tested (p < 0.0005 in all cases). The mean values of the ratios after OSEM and UHD reconstruction showed equally significant differences (p < 0.0005 in all cases). Bland-Altman analysis showed that the SUV and SUL and their normalized values were, on average, up to 60 % higher after UHD reconstruction as compared to OSEM reconstruction. OSEM and HD reconstruction brought a significant difference for SUV and SUL, which remained constantly high after normalization to the liver, indicating that standardization of reconstruction and the use of comparable SUV measurements are crucial when using PET/CT. (orig.)

LOR-interleaving image reconstruction for PET imaging with fractional-crystal collimation

International Nuclear Information System (INIS)

Li, Yusheng; Matej, Samuel; Karp, Joel S; Metzler, Scott D

2015-01-01

Positron emission tomography (PET) has become an important modality in medical and molecular imaging. However, in most PET applications, the resolution is still mainly limited by the physical crystal sizes or the detector’s intrinsic spatial resolution. To achieve images with better spatial resolution in a central region of interest (ROI), we have previously proposed using collimation in PET scanners. The collimator is designed to partially mask detector crystals to detect lines of response (LORs) within fractional crystals. A sequence of collimator-encoded LORs is measured with different collimation configurations. This novel collimated scanner geometry makes the reconstruction problem challenging, as both detector and collimator effects need to be modeled to reconstruct high-resolution images from collimated LORs. In this paper, we present a LOR-interleaving (LORI) algorithm, which incorporates these effects and has the advantage of reusing existing reconstruction software, to reconstruct high-resolution images for PET with fractional-crystal collimation. We also develop a 3D ray-tracing model incorporating both the collimator and crystal penetration for simulations and reconstructions of the collimated PET. By registering the collimator-encoded LORs with the collimator configurations, high-resolution LORs are restored based on the modeled transfer matrices using the non-negative least-squares method and EM algorithm. The resolution-enhanced images are then reconstructed from the high-resolution LORs using the MLEM or OSEM algorithm. For validation, we applied the LORI method to a small-animal PET scanner, A-PET, with a specially designed collimator. We demonstrate through simulated reconstructions with a hot-rod phantom and MOBY phantom that the LORI reconstructions can substantially improve spatial resolution and quantification compared to the uncollimated reconstructions. The LORI algorithm is crucial to improve overall image quality of collimated PET, which

Computational acceleration for MR image reconstruction in partially parallel imaging.

Science.gov (United States)

Ye, Xiaojing; Chen, Yunmei; Huang, Feng

2011-05-01

In this paper, we present a fast numerical algorithm for solving total variation and l(1) (TVL1) based image reconstruction with application in partially parallel magnetic resonance imaging. Our algorithm uses variable splitting method to reduce computational cost. Moreover, the Barzilai-Borwein step size selection method is adopted in our algorithm for much faster convergence. Experimental results on clinical partially parallel imaging data demonstrate that the proposed algorithm requires much fewer iterations and/or less computational cost than recently developed operator splitting and Bregman operator splitting methods, which can deal with a general sensing matrix in reconstruction framework, to get similar or even better quality of reconstructed images.

Use of a model for 3D image reconstruction

International Nuclear Information System (INIS)

Delageniere, S.; Grangeat, P.

1991-01-01

We propose a software for 3D image reconstruction in transmission tomography. This software is based on the use of a model and of the RADON algorithm developed at LETI. The introduction of a markovian model helps us to enhance contrast and straitened the natural transitions existing in the objects studied, whereas standard transform methods smoothe them

Simulated annealing image reconstruction for positron emission tomography

International Nuclear Information System (INIS)

Sundermann, E.; Lemahieu, I.; Desmedt, P.

1994-01-01

In Positron Emission Tomography (PET) images have to be reconstructed from moisy projection data. The noise on the PET data can be modeled by a Poison distribution. In this paper, we present the results of using the simulated annealing technique to reconstruct PET images. Various parameter settings of the simulated annealing algorithm are discussed and optimized. The reconstructed images are of good quality and high contrast, in comparison to other reconstruction techniques. (authors)

Practical considerations for image-based PSF and blobs reconstruction in PET

International Nuclear Information System (INIS)

Stute, Simon; Comtat, Claude

2013-01-01

Iterative reconstructions in positron emission tomography (PET) need a model relating the recorded data to the object/patient being imaged, called the system matrix (SM). The more realistic this model, the better the spatial resolution in the reconstructed images. However, a serious concern when using a SM that accurately models the resolution properties of the PET system is the undesirable edge artefact, visible through oscillations near sharp discontinuities in the reconstructed images. This artefact is a natural consequence of solving an ill-conditioned inverse problem, where the recorded data are band-limited. In this paper, we focus on practical aspects when considering image-based point-spread function (PSF) reconstructions. To remove the edge artefact, we propose to use a particular case of the method of sieves (Grenander 1981 Abstract Inference New York: Wiley), which simply consists in performing a standard PSF reconstruction, followed by a post-smoothing using the PSF as the convolution kernel. Using analytical simulations, we investigate the impact of different reconstruction and PSF modelling parameters on the edge artefact and its suppression, in the case of noise-free data and an exactly known PSF. Using Monte-Carlo simulations, we assess the proposed method of sieves with respect to the choice of the geometric projector and the PSF model used in the reconstruction. When the PSF model is accurately known, we show that the proposed method of sieves succeeds in completely suppressing the edge artefact, though after a number of iterations higher than typically used in practice. When applying the method to realistic data (i.e. unknown true SM and noisy data), we show that the choice of the geometric projector and the PSF model does not impact the results in terms of noise and contrast recovery, as long as the PSF has a width close to the true PSF one. Equivalent results were obtained using either blobs or voxels in the same conditions (i.e. the blob�

Quantitative image reconstruction for total-body PET imaging using the 2-meter long EXPLORER scanner

Science.gov (United States)

Zhang, Xuezhu; Zhou, Jian; Cherry, Simon R.; Badawi, Ramsey D.; Qi, Jinyi

2017-03-01

The EXPLORER project aims to build a 2 meter long total-body PET scanner, which will provide extremely high sensitivity for imaging the entire human body. It will possess a range of capabilities currently unavailable to state-of-the-art clinical PET scanners with a limited axial field-of-view. The huge number of lines-of-response (LORs) of the EXPLORER poses a challenge to the data handling and image reconstruction. The objective of this study is to develop a quantitative image reconstruction method for the EXPLORER and compare its performance with current whole-body scanners. Fully 3D image reconstruction was performed using time-of-flight list-mode data with parallel computation. To recover the resolution loss caused by the parallax error between crystal pairs at a large axial ring difference or transaxial radial offset, we applied an image domain resolution model estimated from point source data. To evaluate the image quality, we conducted computer simulations using the SimSET Monte-Carlo toolkit and XCAT 2.0 anthropomorphic phantom to mimic a 20 min whole-body PET scan with an injection of 25 MBq 18F-FDG. We compare the performance of the EXPLORER with a current clinical scanner that has an axial FOV of 22 cm. The comparison results demonstrated superior image quality from the EXPLORER with a 6.9-fold reduction in noise standard deviation comparing with multi-bed imaging using the clinical scanner.

Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images

International Nuclear Information System (INIS)

Mumcuglu, E.U.; Leahy, R.; Zhou, Z.; Cherry, S.R.

1994-01-01

The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density. To ensure nonnegativity of the solution, a penalty function is used to convert the problem to one of unconstrained optimization. Preconditioners are used to enhance convergence rates. These methods generally achieve effective convergence in 15--25 iterations. Reconstructions are presented of an 18 FDG whole body scan from data collected using a Siemens/CTI ECAT931 whole body system. These results indicate significant improvements in emission image quality using the Bayesian approach, in comparison to filtered backprojection, particularly when reprojections of the MAP transmission image are used in place of the standard attenuation correction factors

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

Noise and signal properties in PSF-based fully 3D PET image reconstruction: an experimental evaluation

International Nuclear Information System (INIS)

Tong, S; Alessio, A M; Kinahan, P E

2010-01-01

The addition of accurate system modeling in PET image reconstruction results in images with distinct noise texture and characteristics. In particular, the incorporation of point spread functions (PSF) into the system model has been shown to visually reduce image noise, but the noise properties have not been thoroughly studied. This work offers a systematic evaluation of noise and signal properties in different combinations of reconstruction methods and parameters. We evaluate two fully 3D PET reconstruction algorithms: (1) OSEM with exact scanner line of response modeled (OSEM+LOR), (2) OSEM with line of response and a measured point spread function incorporated (OSEM+LOR+PSF), in combination with the effects of four post-reconstruction filtering parameters and 1-10 iterations, representing a range of clinically acceptable settings. We used a modified NEMA image quality (IQ) phantom, which was filled with 68 Ge and consisted of six hot spheres of different sizes with a target/background ratio of 4:1. The phantom was scanned 50 times in 3D mode on a clinical system to provide independent noise realizations. Data were reconstructed with OSEM+LOR and OSEM+LOR+PSF using different reconstruction parameters, and our implementations of the algorithms match the vendor's product algorithms. With access to multiple realizations, background noise characteristics were quantified with four metrics. Image roughness and the standard deviation image measured the pixel-to-pixel variation; background variability and ensemble noise quantified the region-to-region variation. Image roughness is the image noise perceived when viewing an individual image. At matched iterations, the addition of PSF leads to images with less noise defined as image roughness (reduced by 35% for unfiltered data) and as the standard deviation image, while it has no effect on background variability or ensemble noise. In terms of signal to noise performance, PSF-based reconstruction has a 7% improvement in

Super-Resolution Image Reconstruction Applied to Medical Ultrasound

Science.gov (United States)

Ellis, Michael

Ultrasound is the preferred imaging modality for many diagnostic applications due to its real-time image reconstruction and low cost. Nonetheless, conventional ultrasound is not used in many applications because of limited spatial resolution and soft tissue contrast. Most commercial ultrasound systems reconstruct images using a simple delay-and-sum architecture on receive, which is fast and robust but does not utilize all information available in the raw data. Recently, more sophisticated image reconstruction methods have been developed that make use of far more information in the raw data to improve resolution and contrast. One such method is the Time-Domain Optimized Near-Field Estimator (TONE), which employs a maximum a priori estimation to solve a highly underdetermined problem, given a well-defined system model. TONE has been shown to significantly improve both the contrast and resolution of ultrasound images when compared to conventional methods. However, TONE's lack of robustness to variations from the system model and extremely high computational cost hinder it from being readily adopted in clinical scanners. This dissertation aims to reduce the impact of TONE's shortcomings, transforming it from an academic construct to a clinically viable image reconstruction algorithm. By altering the system model from a collection of individual hypothetical scatterers to a collection of weighted, diffuse regions, dTONE is able to achieve much greater robustness to modeling errors. A method for efficient parallelization of dTONE is presented that reduces reconstruction time by more than an order of magnitude with little loss in image fidelity. An alternative reconstruction algorithm, called qTONE, is also developed and is able to reduce reconstruction times by another two orders of magnitude while simultaneously improving image contrast. Each of these methods for improving TONE are presented, their limitations are explored, and all are used in concert to reconstruct in

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.

Simulated annealing image reconstruction for positron emission tomography

Energy Technology Data Exchange (ETDEWEB)

Sundermann, E; Lemahieu, I; Desmedt, P [Department of Electronics and Information Systems, University of Ghent, St. Pietersnieuwstraat 41, B-9000 Ghent, Belgium (Belgium)

1994-12-31

In Positron Emission Tomography (PET) images have to be reconstructed from moisy projection data. The noise on the PET data can be modeled by a Poison distribution. In this paper, we present the results of using the simulated annealing technique to reconstruct PET images. Various parameter settings of the simulated annealing algorithm are discussed and optimized. The reconstructed images are of good quality and high contrast, in comparison to other reconstruction techniques. (authors). 11 refs., 2 figs.

Sparse Image Reconstruction in Computed Tomography

DEFF Research Database (Denmark)

Jørgensen, Jakob Sauer

In recent years, increased focus on the potentially harmful effects of x-ray computed tomography (CT) scans, such as radiation-induced cancer, has motivated research on new low-dose imaging techniques. Sparse image reconstruction methods, as studied for instance in the field of compressed sensing...... applications. This thesis takes a systematic approach toward establishing quantitative understanding of conditions for sparse reconstruction to work well in CT. A general framework for analyzing sparse reconstruction methods in CT is introduced and two sets of computational tools are proposed: 1...... contributions to a general set of computational characterization tools. Thus, the thesis contributions help advance sparse reconstruction methods toward routine use in...

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.)

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.)

Parallel CT image reconstruction based on GPUs

International Nuclear Information System (INIS)

Flores, Liubov A.; Vidal, Vicent; Mayo, Patricia; Rodenas, Francisco; Verdú, Gumersindo

2014-01-01

In X-ray computed tomography (CT) iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions from a small number of projections. However, in practice, these methods are not widely used due to the high computational cost of their implementation. Nowadays technology provides the possibility to reduce effectively this drawback. It is the goal of this work to develop a fast GPU-based algorithm to reconstruct high quality images from under sampled and noisy projection data. - Highlights: • We developed GPU-based iterative algorithm to reconstruct images. • Iterative algorithms are capable to reconstruct images from under sampled set of projections. • The computer cost of the implementation of the developed algorithm is low. • The efficiency of the algorithm increases for the large scale problems

Research of ART method in CT image reconstruction

International Nuclear Information System (INIS)

Li Zhipeng; Cong Peng; Wu Haifeng

2005-01-01

This paper studied Algebraic Reconstruction Technique (ART) in CT image reconstruction. Discussed the ray number influence on image quality. And the adopting of smooth method got high quality CT image. (authors)

3D reconstruction based on light field images

Science.gov (United States)

Zhu, Dong; Wu, Chunhong; Liu, Yunluo; Fu, Dongmei

2018-04-01

This paper proposed a method of reconstructing three-dimensional (3D) scene from two light field images capture by Lytro illium. The work was carried out by first extracting the sub-aperture images from light field images and using the scale-invariant feature transform (SIFT) for feature registration on the selected sub-aperture images. Structure from motion (SFM) algorithm is further used on the registration completed sub-aperture images to reconstruct the three-dimensional scene. 3D sparse point cloud was obtained in the end. The method shows that the 3D reconstruction can be implemented by only two light field camera captures, rather than at least a dozen times captures by traditional cameras. This can effectively solve the time-consuming, laborious issues for 3D reconstruction based on traditional digital cameras, to achieve a more rapid, convenient and accurate reconstruction.

[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.

Effect of reconstruction algorithm on image quality and identification of ground-glass opacities and partly solid nodules on low-dose thin-section CT: Experimental study using chest phantom

International Nuclear Information System (INIS)

Koyama, Hisanobu; Ohno, Yoshiharu; Kono, Atsushi A.; Kusaka, Akiko; Konishi, Minoru; Yoshii, Masaru; Sugimura, Kazuro

2010-01-01

Purpose: The purpose of this study was to assess the influence of reconstruction algorithm on identification and image quality of ground-glass opacities (GGOs) and partly solid nodules on low-dose thin-section CT. Materials and methods: A chest CT phantom including simulated GGOs and partly solid nodules was scanned with five different tube currents and reconstructed by using standard (A) and newly developed (B) high-resolution reconstruction algorithms, followed by visually assessment of identification and image quality of GGOs and partly solid nodules by two chest radiologists. Inter-observer agreement, ROC analysis and ANOVA were performed to compare identification and image quality of each data set with those of the standard reference. The standard reference used 120 mA s in conjunction with reconstruction algorithm A. Results: Kappa values (κ) of overall identification and image qualities were substantial or almost perfect (0.60 < κ). Assessment of identification showed that area under the curve of 25 mA reconstructed with reconstruction algorithm A was significantly lower than that of standard reference (p < 0.05), while assessment of image quality indicated that 50 mA s reconstructed with reconstruction algorithm A and 25 mA s reconstructed with both reconstruction algorithms were significantly lower than standard reference (p < 0.05). Conclusion: Reconstruction algorithm may be an important factor for identification and image quality of ground-glass opacities and partly solid nodules on low-dose CT examination.

Resolution-recovery-embedded image reconstruction for a high-resolution animal SPECT system.

Science.gov (United States)

Zeraatkar, Navid; Sajedi, Salar; Farahani, Mohammad Hossein; Arabi, Hossein; Sarkar, Saeed; Ghafarian, Pardis; Rahmim, Arman; Ay, Mohammad Reza

2014-11-01

The small-animal High-Resolution SPECT (HiReSPECT) is a dedicated dual-head gamma camera recently designed and developed in our laboratory for imaging of murine models. Each detector is composed of an array of 1.2 × 1.2 mm(2) (pitch) pixelated CsI(Na) crystals. Two position-sensitive photomultiplier tubes (H8500) are coupled to each head's crystal. In this paper, we report on a resolution-recovery-embedded image reconstruction code applicable to the system and present the experimental results achieved using different phantoms and mouse scans. Collimator-detector response functions (CDRFs) were measured via a pixel-driven method using capillary sources at finite distances from the head within the field of view (FOV). CDRFs were then fitted by independent Gaussian functions. Thereafter, linear interpolations were applied to the standard deviation (σ) values of the fitted Gaussians, yielding a continuous map of CDRF at varying distances from the head. A rotation-based maximum-likelihood expectation maximization (MLEM) method was used for reconstruction. A fast rotation algorithm was developed to rotate the image matrix according to the desired angle by means of pre-generated rotation maps. The experiments demonstrated improved resolution utilizing our resolution-recovery-embedded image reconstruction. While the full-width at half-maximum (FWHM) radial and tangential resolution measurements of the system were over 2 mm in nearly all positions within the FOV without resolution recovery, reaching around 2.5 mm in some locations, they fell below 1.8 mm everywhere within the FOV using the resolution-recovery algorithm. The noise performance of the system was also acceptable; the standard deviation of the average counts per voxel in the reconstructed images was 6.6% and 8.3% without and with resolution recovery, respectively. Copyright © 2014 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

A Kalman filter technique applied for medical image reconstruction

International Nuclear Information System (INIS)

Goliaei, S.; Ghorshi, S.; Manzuri, M. T.; Mortazavi, M.

2011-01-01

Medical images contain information about vital organic tissues inside of human body and are widely used for diagnoses of disease or for surgical purposes. Image reconstruction is essential for medical images for some applications such as suppression of noise or de-blurring the image in order to provide images with better quality and contrast. Due to vital rule of image reconstruction in medical sciences the corresponding algorithms with better efficiency and higher speed is desirable. Most algorithms in image reconstruction are operated on frequency domain such as the most popular one known as filtered back projection. In this paper we introduce a Kalman filter technique which is operated in time domain for medical image reconstruction. Results indicated that as the number of projection increases in both normal collected ray sum and the collected ray sum corrupted by noise the quality of reconstructed image becomes better in terms of contract and transparency. It is also seen that as the number of projection increases the error index decreases.

Tomographic Image Reconstruction Using Training Images with Matrix and Tensor Formulations

DEFF Research Database (Denmark)

Soltani, Sara

the image resolution compared to a classical reconstruction method such as Filtered Back Projection (FBP). Some priors for the tomographic reconstruction take the form of cross-section images of similar objects, providing a set of the so-called training images, that hold the key to the structural......Reducing X-ray exposure while maintaining the image quality is a major challenge in computed tomography (CT); since the imperfect data produced from the few view and/or low intensity projections results in low-quality images that are suffering from severe artifacts when using conventional...... information about the solution. The training images must be reliable and application-specific. This PhD project aims at providing a mathematical and computational framework for the use of training sets as non-parametric priors for the solution in tomographic image reconstruction. Through an unsupervised...

Speeding up image reconstruction in computed tomography

CERN Multimedia

CERN. Geneva

2018-01-01

Computed tomography (CT) is a technique for imaging cross-sections of an object using X-ray measurements taken from different angles. In last decades a significant progress has happened there: today advanced algorithms allow fast image reconstruction and obtaining high-quality images even with missing or dirty data, modern detectors provide high resolution without increasing radiation dose, and high-performance multi-core computing devices are there to help us solving such tasks even faster. I will start with CT basics, then briefly present existing classes of reconstruction algorithms and their differences. After that I will proceed to employing distinctive architectural features of modern multi-core devices (CPUs and GPUs) and popular program interfaces (OpenMP, MPI, CUDA, OpenCL) for developing effective parallel realizations of image reconstruction algorithms. Decreasing full reconstruction time from long hours up to minutes or even seconds has a revolutionary impact in diagnostic medicine and industria...

Simultaneous reconstruction and segmentation for dynamic SPECT imaging

International Nuclear Information System (INIS)

Burger, Martin; Rossmanith, Carolin; Zhang, Xiaoqun

2016-01-01

This work deals with the reconstruction of dynamic images that incorporate characteristic dynamics in certain subregions, as arising for the kinetics of many tracers in emission tomography (SPECT, PET). We make use of a basis function approach for the unknown tracer concentration by assuming that the region of interest can be divided into subregions with spatially constant concentration curves. Applying a regularised variational framework reminiscent of the Chan-Vese model for image segmentation we simultaneously reconstruct both the labelling functions of the subregions as well as the subconcentrations within each region. Our particular focus is on applications in SPECT with the Poisson noise model, resulting in a Kullback–Leibler data fidelity in the variational approach. We present a detailed analysis of the proposed variational model and prove existence of minimisers as well as error estimates. The latter apply to a more general class of problems and generalise existing results in literature since we deal with a nonlinear forward operator and a nonquadratic data fidelity. A computational algorithm based on alternating minimisation and splitting techniques is developed for the solution of the problem and tested on appropriately designed synthetic data sets. For those we compare the results to those of standard EM reconstructions and investigate the effects of Poisson noise in the data. (paper)

Image reconstruction design of industrial CT instrument for teaching

International Nuclear Information System (INIS)

Zou Yongning; Cai Yufang

2009-01-01

Industrial CT instrument for teaching is applied to teaching and study in field of physics and radiology major, image reconstruction is an important part of software on CT instrument. The paper expatiate on CT physical theory and first generation CT reconstruction algorithm, describe scan process of industrial CT instrument for teaching; analyze image artifact as result of displacement of rotation center, implement method of center displacement correcting, design and complete image reconstruction software, application shows that reconstructed image is very clear and qualitatively high. (authors)

3D Reconstruction of NMR Images

Directory of Open Access Journals (Sweden)

Peter Izak

2007-01-01

Full Text Available This paper introduces experiment of 3D reconstruction NMR images scanned from magnetic resonance device. There are described methods which can be used for 3D reconstruction magnetic resonance images in biomedical application. The main idea is based on marching cubes algorithm. For this task was chosen sophistication method by program Vision Assistant, which is a part of program LabVIEW.

Stokes image reconstruction for two-color microgrid polarization imaging systems.

Science.gov (United States)

Lemaster, Daniel A

2011-07-18

The Air Force Research Laboratory has developed a new microgrid polarization imaging system capable of simultaneously reconstructing linear Stokes parameter images in two colors on a single focal plane array. In this paper, an effective method for extracting Stokes images is presented for this type of camera system. It is also shown that correlations between the color bands can be exploited to significantly increase overall spatial resolution. Test data is used to show the advantages of this approach over bilinear interpolation. The bounds (in terms of available reconstruction bandwidth) on image resolution are also provided.

Imaging reconstruction based on improved wavelet denoising combined with parallel-beam filtered back-projection algorithm

Science.gov (United States)

Ren, Zhong; Liu, Guodong; Huang, Zhen

2012-11-01

The image reconstruction is a key step in medical imaging (MI) and its algorithm's performance determinates the quality and resolution of reconstructed image. Although some algorithms have been used, filter back-projection (FBP) algorithm is still the classical and commonly-used algorithm in clinical MI. In FBP algorithm, filtering of original projection data is a key step in order to overcome artifact of the reconstructed image. Since simple using of classical filters, such as Shepp-Logan (SL), Ram-Lak (RL) filter have some drawbacks and limitations in practice, especially for the projection data polluted by non-stationary random noises. So, an improved wavelet denoising combined with parallel-beam FBP algorithm is used to enhance the quality of reconstructed image in this paper. In the experiments, the reconstructed effects were compared between the improved wavelet denoising and others (directly FBP, mean filter combined FBP and median filter combined FBP method). To determine the optimum reconstruction effect, different algorithms, and different wavelet bases combined with three filters were respectively test. Experimental results show the reconstruction effect of improved FBP algorithm is better than that of others. Comparing the results of different algorithms based on two evaluation standards i.e. mean-square error (MSE), peak-to-peak signal-noise ratio (PSNR), it was found that the reconstructed effects of the improved FBP based on db2 and Hanning filter at decomposition scale 2 was best, its MSE value was less and the PSNR value was higher than others. Therefore, this improved FBP algorithm has potential value in the medical imaging.

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.

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.

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.)

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging.

Science.gov (United States)

Yan, Hao; Zhen, Xin; Folkerts, Michael; Li, Yongbao; Pan, Tinsu; Cervino, Laura; Jiang, Steve B; Jia, Xun

2014-07-01

4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3-0.5 mm for patients 1-3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1-1.5 min per phase. High-quality 4D-CBCT imaging based

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

Energy Technology Data Exchange (ETDEWEB)

Yan, Hao; Folkerts, Michael; Jiang, Steve B., E-mail: xun.jia@utsouthwestern.edu, E-mail: steve.jiang@UTSouthwestern.edu; Jia, Xun, E-mail: xun.jia@utsouthwestern.edu, E-mail: steve.jiang@UTSouthwestern.edu [Department of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, Texas 75390 (United States); Zhen, Xin [Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515 (China); Li, Yongbao [Department of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, Texas 75390 and Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Pan, Tinsu [Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Houston, Texas 77030 (United States); Cervino, Laura [Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92093 (United States)

2014-07-15

Purpose: 4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. Methods: The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. Results: The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3–0.5 mm for patients 1–3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1–1.5 min per phase

Image reconstruction for digital breast tomosynthesis (DBT) by using projection-angle-dependent filter functions

Energy Technology Data Exchange (ETDEWEB)

Park, Yeonok; Park, Chulkyu; Cho, Hyosung; Je, Uikyu; Hong, Daeki; Lee, Minsik; Cho, Heemoon; Choi, Sungil; Koo, Yangseo [Yonsei University, Wonju (Korea, Republic of)

2014-09-15

Digital breast tomosynthesis (DBT) is considered in clinics as a standard three-dimensional imaging modality, allowing the earlier detection of cancer. It typically acquires only 10-30 projections over a limited angle range of 15 - 60 .deg. with a stationary detector and typically uses a computationally-efficient filtered-backprojection (FBP) algorithm for image reconstruction. However, a common FBP algorithm yields poor image quality resulting from the loss of average image value and the presence of severe image artifacts due to the elimination of the dc component of the image by the ramp filter and to the incomplete data, respectively. As an alternative, iterative reconstruction methods are often used in DBT to overcome these difficulties, even though they are still computationally expensive. In this study, as a compromise, we considered a projection-angle dependent filtering method in which one-dimensional geometry-adapted filter kernels are computed with the aid of a conjugate-gradient method and are incorporated into the standard FBP framework. We implemented the proposed algorithm and performed systematic simulation works to investigate the imaging characteristics. Our results indicate that the proposed method is superior to a conventional FBP method for DBT imaging and has a comparable computational cost, while preserving good image homogeneity and edge sharpening with no serious image artifacts.

Vascular diameter measurement in CT angiography: comparison of model-based iterative reconstruction and standard filtered back projection algorithms in vitro.

Science.gov (United States)

Suzuki, Shigeru; Machida, Haruhiko; Tanaka, Isao; Ueno, Eiko

2013-03-01

The purpose of this study was to evaluate the performance of model-based iterative reconstruction (MBIR) in measurement of the inner diameter of models of blood vessels and compare performance between MBIR and a standard filtered back projection (FBP) algorithm. Vascular models with wall thicknesses of 0.5, 1.0, and 1.5 mm were scanned with a 64-MDCT unit and densities of contrast material yielding 275, 396, and 542 HU. Images were reconstructed images by MBIR and FBP, and the mean diameter of each model vessel was measured by software automation. Twenty separate measurements were repeated for each vessel, and variance among the repeated measures was analyzed for determination of measurement error. For all nine model vessels, CT attenuation profiles were compared along a line passing through the luminal center on axial images reconstructed with FBP and MBIR, and the 10-90% edge rise distances at the boundary between the vascular wall and the lumen were evaluated. For images reconstructed with FBP, measurement errors were smallest for models with 1.5-mm wall thickness, except those filled with 275-HU contrast material, and errors grew as the density of the contrast material decreased. Measurement errors with MBIR were comparable to or less than those with FBP. In CT attenuation profiles of images reconstructed with MBIR, the 10-90% edge rise distances at the boundary between the lumen and vascular wall were relatively short for each vascular model compared with those of the profile curves of FBP images. MBIR is better than standard FBP for reducing reconstruction blur and improving the accuracy of diameter measurement at CT angiography.

Non-Cartesian parallel imaging reconstruction.

Science.gov (United States)

Wright, Katherine L; Hamilton, Jesse I; Griswold, Mark A; Gulani, Vikas; Seiberlich, Nicole

2014-11-01

Non-Cartesian parallel imaging has played an important role in reducing data acquisition time in MRI. The use of non-Cartesian trajectories can enable more efficient coverage of k-space, which can be leveraged to reduce scan times. These trajectories can be undersampled to achieve even faster scan times, but the resulting images may contain aliasing artifacts. Just as Cartesian parallel imaging can be used to reconstruct images from undersampled Cartesian data, non-Cartesian parallel imaging methods can mitigate aliasing artifacts by using additional spatial encoding information in the form of the nonhomogeneous sensitivities of multi-coil phased arrays. This review will begin with an overview of non-Cartesian k-space trajectories and their sampling properties, followed by an in-depth discussion of several selected non-Cartesian parallel imaging algorithms. Three representative non-Cartesian parallel imaging methods will be described, including Conjugate Gradient SENSE (CG SENSE), non-Cartesian generalized autocalibrating partially parallel acquisition (GRAPPA), and Iterative Self-Consistent Parallel Imaging Reconstruction (SPIRiT). After a discussion of these three techniques, several potential promising clinical applications of non-Cartesian parallel imaging will be covered. © 2014 Wiley Periodicals, Inc.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies

Science.gov (United States)

Petibon, Yoann; Rakvongthai, Yothin; El Fakhri, Georges; Ouyang, Jinsong

2017-05-01

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans—each containing 1/8th of the total number of events—were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in-vivo studies

Science.gov (United States)

Petibon, Yoann; Rakvongthai, Yothin; Fakhri, Georges El; Ouyang, Jinsong

2017-01-01

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves -TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in-vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans - each containing 1/8th of the total number of events - were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard Ordered Subset Expectation Maximization (OSEM) reconstruction algorithm on one side, and the One-Step Late Maximum a Posteriori (OSL-MAP) algorithm on the other

Isotope specific resolution recovery image reconstruction in high resolution PET imaging

Energy Technology Data Exchange (ETDEWEB)

Kotasidis, Fotis A. [Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland and Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, M20 3LJ, Manchester (United Kingdom); Angelis, Georgios I. [Faculty of Health Sciences, Brain and Mind Research Institute, University of Sydney, NSW 2006, Sydney (Australia); Anton-Rodriguez, Jose; Matthews, Julian C. [Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester M20 3LJ (United Kingdom); Reader, Andrew J. [Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada and Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King' s College London, St. Thomas’ Hospital, London SE1 7EH (United Kingdom); Zaidi, Habib [Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva (Switzerland); Geneva Neuroscience Centre, Geneva University, CH-1205 Geneva (Switzerland); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, PO Box 30 001, Groningen 9700 RB (Netherlands)

2014-05-15

Purpose: Measuring and incorporating a scanner-specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However, due to the short half-life of clinically used isotopes, other long-lived isotopes not used in clinical practice are used to perform the PSF measurements. As such, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction, usually underestimating the true PSF owing to the difference in positron range. In high resolution brain and preclinical imaging, this effect is of particular importance since the PSFs become more positron range limited and isotope-specific PSFs can help maximize the performance benefit from using resolution recovery image reconstruction algorithms. Methods: In this work, the authors used a printing technique to simultaneously measure multiple point sources on the High Resolution Research Tomograph (HRRT), and the authors demonstrated the feasibility of deriving isotope-dependent system matrices from fluorine-18 and carbon-11 point sources. Furthermore, the authors evaluated the impact of incorporating them within RM image reconstruction, using carbon-11 phantom and clinical datasets on the HRRT. Results: The results obtained using these two isotopes illustrate that even small differences in positron range can result in different PSF maps, leading to further improvements in contrast recovery when used in image reconstruction. The difference is more pronounced in the centre of the field-of-view where the full width at half maximum (FWHM) from the positron range has a larger contribution to the overall FWHM compared to the edge where the parallax error dominates the overall FWHM. Conclusions: Based on the proposed methodology, measured isotope-specific and spatially variant PSFs can be reliably derived and used for improved spatial resolution and variance performance in resolution

Isotope specific resolution recovery image reconstruction in high resolution PET imaging

International Nuclear Information System (INIS)

Kotasidis, Fotis A.; Angelis, Georgios I.; Anton-Rodriguez, Jose; Matthews, Julian C.; Reader, Andrew J.; Zaidi, Habib

2014-01-01

Purpose: Measuring and incorporating a scanner-specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However, due to the short half-life of clinically used isotopes, other long-lived isotopes not used in clinical practice are used to perform the PSF measurements. As such, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction, usually underestimating the true PSF owing to the difference in positron range. In high resolution brain and preclinical imaging, this effect is of particular importance since the PSFs become more positron range limited and isotope-specific PSFs can help maximize the performance benefit from using resolution recovery image reconstruction algorithms. Methods: In this work, the authors used a printing technique to simultaneously measure multiple point sources on the High Resolution Research Tomograph (HRRT), and the authors demonstrated the feasibility of deriving isotope-dependent system matrices from fluorine-18 and carbon-11 point sources. Furthermore, the authors evaluated the impact of incorporating them within RM image reconstruction, using carbon-11 phantom and clinical datasets on the HRRT. Results: The results obtained using these two isotopes illustrate that even small differences in positron range can result in different PSF maps, leading to further improvements in contrast recovery when used in image reconstruction. The difference is more pronounced in the centre of the field-of-view where the full width at half maximum (FWHM) from the positron range has a larger contribution to the overall FWHM compared to the edge where the parallax error dominates the overall FWHM. Conclusions: Based on the proposed methodology, measured isotope-specific and spatially variant PSFs can be reliably derived and used for improved spatial resolution and variance performance in resolution

Isotope specific resolution recovery image reconstruction in high resolution PET imaging.

Science.gov (United States)

Kotasidis, Fotis A; Angelis, Georgios I; Anton-Rodriguez, Jose; Matthews, Julian C; Reader, Andrew J; Zaidi, Habib

2014-05-01

Measuring and incorporating a scanner-specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However, due to the short half-life of clinically used isotopes, other long-lived isotopes not used in clinical practice are used to perform the PSF measurements. As such, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction, usually underestimating the true PSF owing to the difference in positron range. In high resolution brain and preclinical imaging, this effect is of particular importance since the PSFs become more positron range limited and isotope-specific PSFs can help maximize the performance benefit from using resolution recovery image reconstruction algorithms. In this work, the authors used a printing technique to simultaneously measure multiple point sources on the High Resolution Research Tomograph (HRRT), and the authors demonstrated the feasibility of deriving isotope-dependent system matrices from fluorine-18 and carbon-11 point sources. Furthermore, the authors evaluated the impact of incorporating them within RM image reconstruction, using carbon-11 phantom and clinical datasets on the HRRT. The results obtained using these two isotopes illustrate that even small differences in positron range can result in different PSF maps, leading to further improvements in contrast recovery when used in image reconstruction. The difference is more pronounced in the centre of the field-of-view where the full width at half maximum (FWHM) from the positron range has a larger contribution to the overall FWHM compared to the edge where the parallax error dominates the overall FWHM. Based on the proposed methodology, measured isotope-specific and spatially variant PSFs can be reliably derived and used for improved spatial resolution and variance performance in resolution recovery image reconstruction. The

Simbol-X Formation Flight and Image Reconstruction

Science.gov (United States)

Civitani, M.; Djalal, S.; Le Duigou, J. M.; La Marle, O.; Chipaux, R.

2009-05-01

Simbol-X is the first operational mission relying on two satellites flying in formation. The dynamics of the telescope, due to the formation flight concept, raises a variety of problematic, like image reconstruction, that can be better evaluated via a simulation tools. We present here the first results obtained with Simulos, simulation tool aimed to study the relative spacecrafts navigation and the weight of the different parameters in image reconstruction and telescope performance evaluation. The simulation relies on attitude and formation flight sensors models, formation flight dynamics and control, mirror model and focal plane model, while the image reconstruction is based on the Line of Sight (LOS) concept.

Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction

NARCIS (Netherlands)

Karakatsanis, Nicolas A.; Casey, Michael E.; Lodge, Martin A.; Rahmim, Arman; Zaidi, Habib

2016-01-01

Whole-body (WB) dynamic PET has recently demonstrated its potential in translating the quantitative benefits of parametric imaging to the clinic. Post-reconstruction standard Patlak (sPatlak) WB graphical analysis utilizes multi-bed multi-pass PET acquisition to produce quantitative WB images of the

Use of count-based image reconstruction to evaluate the variability and repeatability of measured standardised uptake values.

Directory of Open Access Journals (Sweden)

Tomohiro Kaneta

Full Text Available Standardized uptake values (SUVs are the most widely used quantitative imaging biomarkers in PET. It is important to evaluate the variability and repeatability of measured SUVs. Phantom studies seem to be essential for this purpose; however, repetitive phantom scanning is not recommended due to the decay of radioactivity. In this study, we performed count-based image reconstruction to avoid the influence of decay using two different PET/CT scanners. By adjusting the ratio of 18F-fluorodeoxyglucose solution to tap water, a NEMA IEC body phantom was set for SUVs of 4.0 inside six hot spheres. The PET data were obtained using two scanners (Aquiduo and Celesteion; Toshiba Medical Systems, Tochigi, Japan. We set the start time for image reconstruction when the total radioactivity in the phantom was 2.53 kBq/cc, and employed the counts of the first 2-min acquisition as the standard. To maintain the number of counts for each image, we set the acquisition time for image reconstruction depending on the decay of radioactivity. We obtained 50 images, and calculated the SUVmax and SUVpeak of all six spheres in each image. The average values of the SUVmax were used to calculate the recovery coefficients to compare those measured by the two different scanners. Bland-Altman analyses of the SUVs measured by the two scanners were also performed. The measured SUVs using the two scanners exhibited a 10-30% difference, and the standard deviation (SD of the measured SUVs was between 0.1-0.2. The Celesteion always exhibited higher values than the Aquiduo. The smaller sphere exhibited a larger SD, and the SUVpeak had a smaller SD than the SUVmax. The Bland-Altman analyses showed poor agreement between the SUVs measured by the two scanners. The recovery coefficient curves obtained from the two scanners were considerably different. The Celesteion exhibited higher recovery coefficients than the Aquiduo, especially at approximately 20-mm-diameter. Additionally, the curves

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

Isotope specific resolution recovery image reconstruction in high resolution PET imaging

OpenAIRE

Kotasidis Fotis A.; Kotasidis Fotis A.; Angelis Georgios I.; Anton-Rodriguez Jose; Matthews Julian C.; Reader Andrew J.; Reader Andrew J.; Zaidi Habib; Zaidi Habib; Zaidi Habib

2014-01-01

Purpose: Measuring and incorporating a scanner specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However due to the short half life of clinically used isotopes other long lived isotopes not used in clinical practice are used to perform the PSF measurements. As such non optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction usuall...

The usefulness of multiplanar reconstruction images in preoperative t-staging of advanced gastric cancer

International Nuclear Information System (INIS)

Koo, Young Baek; Kim, Suk; Lee, Jun Woo; Kim, Soo Jin; Choo, Ki Seok; Lee, Tae Hong; Moon, Tae Yong; Lee, Suk Hong; Jeon, Tae Yong

2004-01-01

The purpose of this study was to evaluate the performance of multidetector-row CT (MDCT) in the preoperative T-staging of patients with advanced gastric cancer. A total of 65 patients with an established diagnosis of advanced gastric cancer (T2 or more) were evaluated with MDCT. The protocol of MDCT consisted of high-quality (HQ) mode helical scanning with a slice thickness of 2.5 mm. The axial CT images were reconstructed with a slice thickness of 5 mm. MPR images were reconstructed from the raw axial data with a slice thickness of 5 mm. A comparison between the standard axial and axial MPR images was performed by two radiologists with regard to the evaluation of the tumor location and T-stage. These findings were compared with the pathologic and surgical findings. T-staging of the advanced stomach cancer was correct in 89% (58/65) and 69% (45/65) of the MPR images and axial images, respectively. The MPR images improved the detection rate (5 lesions) of the tumors and increased the accuracy of the T-staging (13 lesions) in comparison with the axial images. The MPR images are of greater diagnostic value for the evaluation of omental seeding (5 lesions: axial images, 9 lesions: MPR images), tumor location and extension. Multiplanar reconstruction (MPR) images provide increased confidence in the location and T-staging of certain cases of advanced gastric cancer, such as those in locations where CT images are susceptible to be affected by the difficulties associated with partial volume averaging. In this study, the MPR images provided more precise information about the tumor location and T-staging than the standard axial images in the preoperative evaluation of advanced gastric cancer

The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation

International Nuclear Information System (INIS)

Zhao, Zhanqi; Möller, Knut; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich

2014-01-01

Analysis methods of electrical impedance tomography (EIT) images based on different reconstruction algorithms were examined. EIT measurements were performed on eight mechanically ventilated patients with acute respiratory distress syndrome. A maneuver with step increase of airway pressure was performed. EIT raw data were reconstructed offline with (1) filtered back-projection (BP); (2) the Dräger algorithm based on linearized Newton–Raphson (DR); (3) the GREIT (Graz consensus reconstruction algorithm for EIT) reconstruction algorithm with a circular forward model (GR C ) and (4) GREIT with individual thorax geometry (GR T ). Individual thorax contours were automatically determined from the routine computed tomography images. Five indices were calculated on the resulting EIT images respectively: (a) the ratio between tidal and deep inflation impedance changes; (b) tidal impedance changes in the right and left lungs; (c) center of gravity; (d) the global inhomogeneity index and (e) ventilation delay at mid-dorsal regions. No significant differences were found in all examined indices among the four reconstruction algorithms (p > 0.2, Kruskal–Wallis test). The examined algorithms used for EIT image reconstruction do not influence the selected indices derived from the EIT image analysis. Indices that validated for images with one reconstruction algorithm are also valid for other reconstruction algorithms. (paper)

The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation.

Science.gov (United States)

Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut

2014-06-01

Analysis methods of electrical impedance tomography (EIT) images based on different reconstruction algorithms were examined. EIT measurements were performed on eight mechanically ventilated patients with acute respiratory distress syndrome. A maneuver with step increase of airway pressure was performed. EIT raw data were reconstructed offline with (1) filtered back-projection (BP); (2) the Dräger algorithm based on linearized Newton-Raphson (DR); (3) the GREIT (Graz consensus reconstruction algorithm for EIT) reconstruction algorithm with a circular forward model (GR(C)) and (4) GREIT with individual thorax geometry (GR(T)). Individual thorax contours were automatically determined from the routine computed tomography images. Five indices were calculated on the resulting EIT images respectively: (a) the ratio between tidal and deep inflation impedance changes; (b) tidal impedance changes in the right and left lungs; (c) center of gravity; (d) the global inhomogeneity index and (e) ventilation delay at mid-dorsal regions. No significant differences were found in all examined indices among the four reconstruction algorithms (p > 0.2, Kruskal-Wallis test). The examined algorithms used for EIT image reconstruction do not influence the selected indices derived from the EIT image analysis. Indices that validated for images with one reconstruction algorithm are also valid for other reconstruction algorithms.

Image reconstruction methods in positron tomography

International Nuclear Information System (INIS)

Townsend, D.W.; Defrise, M.

1993-01-01

In the two decades since the introduction of the X-ray scanner into radiology, medical imaging techniques have become widely established as essential tools in the diagnosis of disease. As a consequence of recent technological and mathematical advances, the non-invasive, three-dimensional imaging of internal organs such as the brain and the heart is now possible, not only for anatomical investigations using X-ray but also for studies which explore the functional status of the body using positron-emitting radioisotopes. This report reviews the historical and physical basis of medical imaging techniques using positron-emitting radioisotopes. Mathematical methods which enable three-dimensional distributions of radioisotopes to be reconstructed from projection data (sinograms) acquired by detectors suitably positioned around the patient are discussed. The extension of conventional two-dimensional tomographic reconstruction algorithms to fully three-dimensional reconstruction is described in detail. (orig.)

Objective evaluation of reconstruction methods for quantitative SPECT imaging in the absence of ground truth.

Science.gov (United States)

Jha, Abhinav K; Song, Na; Caffo, Brian; Frey, Eric C

2015-04-13

Quantitative single-photon emission computed tomography (SPECT) imaging is emerging as an important tool in clinical studies and biomedical research. There is thus a need for optimization and evaluation of systems and algorithms that are being developed for quantitative SPECT imaging. An appropriate objective method to evaluate these systems is by comparing their performance in the end task that is required in quantitative SPECT imaging, such as estimating the mean activity concentration in a volume of interest (VOI) in a patient image. This objective evaluation can be performed if the true value of the estimated parameter is known, i.e. we have a gold standard. However, very rarely is this gold standard known in human studies. Thus, no-gold-standard techniques to optimize and evaluate systems and algorithms in the absence of gold standard are required. In this work, we developed a no-gold-standard technique to objectively evaluate reconstruction methods used in quantitative SPECT when the parameter to be estimated is the mean activity concentration in a VOI. We studied the performance of the technique with realistic simulated image data generated from an object database consisting of five phantom anatomies with all possible combinations of five sets of organ uptakes, where each anatomy consisted of eight different organ VOIs. Results indicate that the method provided accurate ranking of the reconstruction methods. We also demonstrated the application of consistency checks to test the no-gold-standard output.

Effect of radiation dose and adaptive statistical iterative reconstruction on image quality of pulmonary computed tomography

International Nuclear Information System (INIS)

Sato, Jiro; Akahane, Masaaki; Inano, Sachiko; Terasaki, Mariko; Akai, Hiroyuki; Katsura, Masaki; Matsuda, Izuru; Kunimatsu, Akira; Ohtomo, Kuni

2012-01-01

The purpose of this study was to assess the effects of dose and adaptive statistical iterative reconstruction (ASIR) on image quality of pulmonary computed tomography (CT). Inflated and fixed porcine lungs were scanned with a 64-slice CT system at 10, 20, 40 and 400 mAs. Using automatic exposure control, 40 mAs was chosen as standard dose. Scan data were reconstructed with filtered back projection (FBP) and ASIR. Image pairs were obtained by factorial combination of images at a selected level. Using a 21-point scale, three experienced radiologists independently rated differences in quality between adjacently displayed paired images for image noise, image sharpness and conspicuity of tiny nodules. A subjective quality score (SQS) for each image was computed based on Anderson's functional measurement theory. The standard deviation was recorded as a quantitative noise measurement. At all doses examined, SQSs improved with ASIR for all evaluation items. No significant differences were noted between the SQSs for 40%-ASIR images obtained at 20 mAs and those for FBP images at 40 mAs. Compared to the FBP algorithm, ASIR for lung CT can enable an approximately 50% dose reduction from the standard dose while preserving visualization of small structures. (author)

Advanced virtual monochromatic reconstruction of dual-energy unenhanced brain computed tomography in children: comparison of image quality against standard mono-energetic images and conventional polychromatic computed tomography

Energy Technology Data Exchange (ETDEWEB)

Park, Juil [Seoul National University Children' s Hospital, Department of Radiology, Seoul (Korea, Republic of); Choi, Young Hun [Seoul National University Children' s Hospital, Department of Radiology, Seoul (Korea, Republic of); Seoul National University College of Medicine, Department of Radiology, Seoul (Korea, Republic of); Cheon, Jung-Eun; Kim, Woo Sun; Kim, In-One [Seoul National University Children' s Hospital, Department of Radiology, Seoul (Korea, Republic of); Seoul National University College of Medicine, Department of Radiology, Seoul (Korea, Republic of); Seoul National University Medical Research Center, Institute of Radiation Medicine, Seoul (Korea, Republic of); Pak, Seong Yong [Siemens Healthineers, Seoul (Korea, Republic of); Krauss, Bernhard [Siemens Healthineers, Forchheim (Germany)

2017-11-15

Advanced virtual monochromatic reconstruction from dual-energy brain CT has not been evaluated in children. To determine the most effective advanced virtual monochromatic imaging energy level for maximizing pediatric brain parenchymal image quality in dual-energy unenhanced brain CT and to compare this technique with conventional monochromatic reconstruction and polychromatic scanning. Using both conventional (Mono) and advanced monochromatic reconstruction (Mono+) techniques, we retrospectively reconstructed 13 virtual monochromatic imaging energy levels from 40 keV to 100 keV in 5-keV increments from dual-source, dual-energy unenhanced brain CT scans obtained in 23 children. We analyzed gray and white matter noise ratios, signal-to-noise ratios and contrast-to-noise ratio, and posterior fossa artifact. We chose the optimal mono-energetic levels and compared them with conventional CT. For Mono+maximum optima were observed at 60 keV, and minimum posterior fossa artifact at 70 keV. For Mono, optima were at 65-70 keV, with minimum posterior fossa artifact at 75 keV. Mono+ was superior to Mono and to polychromatic CT for image-quality measures. Subjective analysis rated Mono+superior to other image sets. Optimal virtual monochromatic imaging using Mono+ algorithm demonstrated better image quality for gray-white matter differentiation and reduction of the artifact in the posterior fossa. (orig.)

A Case Series of Rapid Prototyping and Intraoperative Imaging in Orbital Reconstruction

Science.gov (United States)

Lim, Christopher G.T.; Campbell, Duncan I.; Cook, Nicholas; Erasmus, Jason

2014-01-01

In Christchurch Hospital, rapid prototyping (RP) and intraoperative imaging are the standard of care in orbital trauma and has been used since February 2013. RP allows the fabrication of an anatomical model to visualize complex anatomical structures which is dimensionally accurate and cost effective. This assists diagnosis, planning, and preoperative implant adaptation for orbital reconstruction. Intraoperative imaging involves a computed tomography scan during surgery to evaluate surgical implants and restored anatomy and allows the clinician to correct errors in implant positioning that may occur during the same procedure. This article aims to demonstrate the potential clinical and cost saving benefits when both these technologies are used in orbital reconstruction which minimize the need for revision surgery. PMID:26000080

A case series of rapid prototyping and intraoperative imaging in orbital reconstruction.

Science.gov (United States)

Lim, Christopher G T; Campbell, Duncan I; Cook, Nicholas; Erasmus, Jason

2015-06-01

In Christchurch Hospital, rapid prototyping (RP) and intraoperative imaging are the standard of care in orbital trauma and has been used since February 2013. RP allows the fabrication of an anatomical model to visualize complex anatomical structures which is dimensionally accurate and cost effective. This assists diagnosis, planning, and preoperative implant adaptation for orbital reconstruction. Intraoperative imaging involves a computed tomography scan during surgery to evaluate surgical implants and restored anatomy and allows the clinician to correct errors in implant positioning that may occur during the same procedure. This article aims to demonstrate the potential clinical and cost saving benefits when both these technologies are used in orbital reconstruction which minimize the need for revision surgery.

Few-view image reconstruction with dual dictionaries

International Nuclear Information System (INIS)

Lu Yang; Zhao Jun; Wang Ge

2012-01-01

In this paper, we formulate the problem of computed tomography (CT) under sparsity and few-view constraints, and propose a novel algorithm for image reconstruction from few-view data utilizing the simultaneous algebraic reconstruction technique (SART) coupled with dictionary learning, sparse representation and total variation (TV) minimization on two interconnected levels. The main feature of our algorithm is the use of two dictionaries: a transitional dictionary for atom matching and a global dictionary for image updating. The atoms in the global and transitional dictionaries represent the image patches from high-quality and low-quality CT images, respectively. Experiments with simulated and real projections were performed to evaluate and validate the proposed algorithm. The results reconstructed using the proposed approach are significantly better than those using either SART or SART–TV. (paper)

Reconstruction CT imaging of the hypopharynx and the larynx

International Nuclear Information System (INIS)

Okuno, Tetsuji; Fujimura, Akiko; Murakami, Yasushi; Shiga, Hayao

1986-01-01

The multiplanar reconstruction CT imaging of the hypopharynx and the larynx was performed on a total of 20 cases: 8 with laryngeal carcinomas, 6 with hypopharyngeal carcinomas, 4 with vocal cord paralyses due to various causes, 1 with laryngeal amyloidosis, 1 with inflammatory granuloma of the hypopharynx. Coronal, segittal, and parasagittal reconstruction images were obtained from either 1 or 2 mm overlapping axial scans with 4 or 5 mm slice thickness (3 cases) using 5 sec scan times during queit breathing. In 15 cases with coronal reconstruction imaging, the anatomical derangements of the laryngopharyngeal structures especially along the undersurface of the true vocal cord to the false cord level, the lateral wall of the pyriform sinus, and the paraglottic space were demonstrated more clearly than the axial CT imaging. In 5 cases with sagittal reconstruction imaging, the vertical extension of the lesions through the anterior commisure was more clearly depicted than the axial CT imaging. In 8 cases with parasagittal reconstruction imaging, which is along the vocal fold or across the aryepiglottic fold, pathological changes along the aryepiglottic fold, the arytenoid-corniculate cartilage complex, and the tip of the pyriform sinus were more clearly demonstrated than the axial CT imaging. In determining the feasibility of conservation surgery of the larynx and the hypopharynx, reconstruction CT imaging is recommended as the diagnostic procedure of a choice, which would supplement the findings of the routine axial CT imaging. (author)

The SENSE-Isomorphism Theoretical Image Voxel Estimation (SENSE-ITIVE) Model for Reconstruction and Observing Statistical Properties of Reconstruction Operators

Science.gov (United States)

Bruce, Iain P.; Karaman, M. Muge; Rowe, Daniel B.

2012-01-01

The acquisition of sub-sampled data from an array of receiver coils has become a common means of reducing data acquisition time in MRI. Of the various techniques used in parallel MRI, SENSitivity Encoding (SENSE) is one of the most common, making use of a complex-valued weighted least squares estimation to unfold the aliased images. It was recently shown in Bruce et al. [Magn. Reson. Imag. 29(2011):1267–1287] that when the SENSE model is represented in terms of a real-valued isomorphism, it assumes a skew-symmetric covariance between receiver coils, as well as an identity covariance structure between voxels. In this manuscript, we show that not only is the skew-symmetric coil covariance unlike that of real data, but the estimated covariance structure between voxels over a time series of experimental data is not an identity matrix. As such, a new model, entitled SENSE-ITIVE, is described with both revised coil and voxel covariance structures. Both the SENSE and SENSE-ITIVE models are represented in terms of real-valued isomorphisms, allowing for a statistical analysis of reconstructed voxel means, variances, and correlations resulting from the use of different coil and voxel covariance structures used in the reconstruction processes to be conducted. It is shown through both theoretical and experimental illustrations that the miss-specification of the coil and voxel covariance structures in the SENSE model results in a lower standard deviation in each voxel of the reconstructed images, and thus an artificial increase in SNR, compared to the standard deviation and SNR of the SENSE-ITIVE model where both the coil and voxel covariances are appropriately accounted for. It is also shown that there are differences in the correlations induced by the reconstruction operations of both models, and consequently there are differences in the correlations estimated throughout the course of reconstructed time series. These differences in correlations could result in meaningful

Effects of acquisition time and reconstruction algorithm on image quality, quantitative parameters, and clinical interpretation of myocardial perfusion imaging

DEFF Research Database (Denmark)

Enevoldsen, Lotte H; Menashi, Changez A K; Andersen, Ulrik B

2013-01-01

time (HT) protocols and Evolution for Cardiac Software. METHODS: We studied 45 consecutive, non-selected patients referred for a clinically indicated routine 2-day stress/rest (99m)Tc-Sestamibi myocardial perfusion SPECT. All patients underwent an FT and an HT scan. Both FT and HT scans were processed......-RR) and for quantitative analysis (FT-FBP, HT-FBP, and HT-RR). The datasets were analyzed using commercially available QGS/QPS software and read by two observers evaluating image quality and clinical interpretation. Image quality was assessed on a 10-cm visual analog scale score. RESULTS: HT imaging was associated......: Use of RR reconstruction algorithms compensates for loss of image quality associated with reduced scan time. Both HT acquisition and RR reconstruction algorithm had significant effects on motion and perfusion parameters obtained with standard software, but these effects were relatively small...

Comparison of power spectra for tomosynthesis projections and reconstructed images

International Nuclear Information System (INIS)

Engstrom, Emma; Reiser, Ingrid; Nishikawa, Robert

2009-01-01

Burgess et al. [Med. Phys. 28, 419-437 (2001)] showed that the power spectrum of mammographic breast background follows a power law and that lesion detectability is affected by the power-law exponent β which measures the amount of structure in the background. Following the study of Burgess et al., the authors measured and compared the power-law exponent of mammographic backgrounds in tomosynthesis projections and reconstructed slices to investigate the effect of tomosynthesis imaging on background structure. Our data set consisted of 55 patient cases. For each case, regions of interest (ROIs) were extracted from both projection images and reconstructed slices. The periodogram of each ROI was computed by taking the squared modulus of the Fourier transform of the ROI. The power-law exponent was determined for each periodogram and averaged across all ROIs extracted from all projections or reconstructed slices for each patient data set. For the projections, the mean β averaged across the 55 cases was 3.06 (standard deviation of 0.21), while it was 2.87 (0.24) for the corresponding reconstructions. The difference in β for a given patient between the projection ROIs and the reconstructed ROIs averaged across the 55 cases was 0.194, which was statistically significant (p<0.001). The 95% CI for the difference between the mean value of β for the projections and reconstructions was [0.170, 0.218]. The results are consistent with the observation that the amount of breast structure in the tomosynthesis slice is reduced compared to projection mammography and that this may lead to improved lesion detectability.

Exact fan-beam image reconstruction algorithm for truncated projection data acquired from an asymmetric half-size detector

International Nuclear Information System (INIS)

Leng Shuai; Zhuang Tingliang; Nett, Brian E; Chen Guanghong

2005-01-01

In this paper, we present a new algorithm designed for a specific data truncation problem in fan-beam CT. We consider a scanning configuration in which the fan-beam projection data are acquired from an asymmetrically positioned half-sized detector. Namely, the asymmetric detector only covers one half of the scanning field of view. Thus, the acquired fan-beam projection data are truncated at every view angle. If an explicit data rebinning process is not invoked, this data acquisition configuration will reek havoc on many known fan-beam image reconstruction schemes including the standard filtered backprojection (FBP) algorithm and the super-short-scan FBP reconstruction algorithms. However, we demonstrate that a recently developed fan-beam image reconstruction algorithm which reconstructs an image via filtering a backprojection image of differentiated projection data (FBPD) survives the above fan-beam data truncation problem. Namely, we may exactly reconstruct the whole image object using the truncated data acquired in a full scan mode (2π angular range). We may also exactly reconstruct a small region of interest (ROI) using the truncated projection data acquired in a short-scan mode (less than 2π angular range). The most important characteristic of the proposed reconstruction scheme is that an explicit data rebinning process is not introduced. Numerical simulations were conducted to validate the new reconstruction algorithm

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.)

Matrix-based image reconstruction methods for tomography

International Nuclear Information System (INIS)

Llacer, J.; Meng, J.D.

1984-10-01

Matrix methods of image reconstruction have not been used, in general, because of the large size of practical matrices, ill condition upon inversion and the success of Fourier-based techniques. An exception is the work that has been done at the Lawrence Berkeley Laboratory for imaging with accelerated radioactive ions. An extension of that work into more general imaging problems shows that, with a correct formulation of the problem, positron tomography with ring geometries results in well behaved matrices which can be used for image reconstruction with no distortion of the point response in the field of view and flexibility in the design of the instrument. Maximum Likelihood Estimator methods of reconstruction, which use the system matrices tailored to specific instruments and do not need matrix inversion, are shown to result in good preliminary images. A parallel processing computer structure based on multiple inexpensive microprocessors is proposed as a system to implement the matrix-MLE methods. 14 references, 7 figures

Direct reconstruction of cardiac PET kinetic parametric images using a preconditioned conjugate gradient approach.

Science.gov (United States)

Rakvongthai, Yothin; Ouyang, Jinsong; Guerin, Bastien; Li, Quanzheng; Alpert, Nathaniel M; El Fakhri, Georges

2013-10-01

Our research goal is to develop an algorithm to reconstruct cardiac positron emission tomography (PET) kinetic parametric images directly from sinograms and compare its performance with the conventional indirect approach. Time activity curves of a NCAT phantom were computed according to a one-tissue compartmental kinetic model with realistic kinetic parameters. The sinograms at each time frame were simulated using the activity distribution for the time frame. The authors reconstructed the parametric images directly from the sinograms by optimizing a cost function, which included the Poisson log-likelihood and a spatial regularization terms, using the preconditioned conjugate gradient (PCG) algorithm with the proposed preconditioner. The proposed preconditioner is a diagonal matrix whose diagonal entries are the ratio of the parameter and the sensitivity of the radioactivity associated with parameter. The authors compared the reconstructed parametric images using the direct approach with those reconstructed using the conventional indirect approach. At the same bias, the direct approach yielded significant relative reduction in standard deviation by 12%-29% and 32%-70% for 50 × 10(6) and 10 × 10(6) detected coincidences counts, respectively. Also, the PCG method effectively reached a constant value after only 10 iterations (with numerical convergence achieved after 40-50 iterations), while more than 500 iterations were needed for CG. The authors have developed a novel approach based on the PCG algorithm to directly reconstruct cardiac PET parametric images from sinograms, and yield better estimation of kinetic parameters than the conventional indirect approach, i.e., curve fitting of reconstructed images. The PCG method increases the convergence rate of reconstruction significantly as compared to the conventional CG method.

A Convex Formulation for Magnetic Particle Imaging X-Space Reconstruction.

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Konkle, Justin J; Goodwill, Patrick W; Hensley, Daniel W; Orendorff, Ryan D; Lustig, Michael; Conolly, Steven M

2015-01-01

Magnetic Particle Imaging (mpi) is an emerging imaging modality with exceptional promise for clinical applications in rapid angiography, cell therapy tracking, cancer imaging, and inflammation imaging. Recent publications have demonstrated quantitative mpi across rat sized fields of view with x-space reconstruction methods. Critical to any medical imaging technology is the reliability and accuracy of image reconstruction. Because the average value of the mpi signal is lost during direct-feedthrough signal filtering, mpi reconstruction algorithms must recover this zero-frequency value. Prior x-space mpi recovery techniques were limited to 1d approaches which could introduce artifacts when reconstructing a 3d image. In this paper, we formulate x-space reconstruction as a 3d convex optimization problem and apply robust a priori knowledge of image smoothness and non-negativity to reduce non-physical banding and haze artifacts. We conclude with a discussion of the powerful extensibility of the presented formulation for future applications.

Monte-Carlo simulations and image reconstruction for novel imaging scenarios in emission tomography

International Nuclear Information System (INIS)

Gillam, John E.; Rafecas, Magdalena

2016-01-01

Emission imaging incorporates both the development of dedicated devices for data acquisition as well as algorithms for recovering images from that data. Emission tomography is an indirect approach to imaging. The effect of device modification on the final image can be understood through both the way in which data are gathered, using simulation, and the way in which the image is formed from that data, or image reconstruction. When developing novel devices, systems and imaging tasks, accurate simulation and image reconstruction allow performance to be estimated, and in some cases optimized, using computational methods before or during the process of physical construction. However, there are a vast range of approaches, algorithms and pre-existing computational tools that can be exploited and the choices made will affect the accuracy of the in silico results and quality of the reconstructed images. On the one hand, should important physical effects be neglected in either the simulation or reconstruction steps, specific enhancements provided by novel devices may not be represented in the results. On the other hand, over-modeling of device characteristics in either step leads to large computational overheads that can confound timely results. Here, a range of simulation methodologies and toolkits are discussed, as well as reconstruction algorithms that may be employed in emission imaging. The relative advantages and disadvantages of a range of options are highlighted using specific examples from current research scenarios.

Monte-Carlo simulations and image reconstruction for novel imaging scenarios in emission tomography

Energy Technology Data Exchange (ETDEWEB)

Gillam, John E. [The University of Sydney, Faculty of Health Sciences and The Brain and Mind Centre, Camperdown (Australia); Rafecas, Magdalena, E-mail: rafecas@imt.uni-luebeck.de [University of Lubeck, Institute of Medical Engineering, Ratzeburger Allee 160, 23538 Lübeck (Germany)

2016-02-11

Emission imaging incorporates both the development of dedicated devices for data acquisition as well as algorithms for recovering images from that data. Emission tomography is an indirect approach to imaging. The effect of device modification on the final image can be understood through both the way in which data are gathered, using simulation, and the way in which the image is formed from that data, or image reconstruction. When developing novel devices, systems and imaging tasks, accurate simulation and image reconstruction allow performance to be estimated, and in some cases optimized, using computational methods before or during the process of physical construction. However, there are a vast range of approaches, algorithms and pre-existing computational tools that can be exploited and the choices made will affect the accuracy of the in silico results and quality of the reconstructed images. On the one hand, should important physical effects be neglected in either the simulation or reconstruction steps, specific enhancements provided by novel devices may not be represented in the results. On the other hand, over-modeling of device characteristics in either step leads to large computational overheads that can confound timely results. Here, a range of simulation methodologies and toolkits are discussed, as well as reconstruction algorithms that may be employed in emission imaging. The relative advantages and disadvantages of a range of options are highlighted using specific examples from current research scenarios.

An efficient algorithm for MR image reconstruction and compression

International Nuclear Information System (INIS)

Wang, Hang; Rosenfeld, D.; Braun, M.; Yan, Hong

1992-01-01

In magnetic resonance imaging (MRI), the original data are sampled in the spatial frequency domain. The sampled data thus constitute a set of discrete Fourier transform (DFT) coefficients. The image is usually reconstructed by taking inverse DFT. The image data may then be efficiently compressed using the discrete cosine transform (DCT). A method of using DCT to treat the sampled data is presented which combines two procedures, image reconstruction and data compression. This method may be particularly useful in medical picture archiving and communication systems where both image reconstruction and compression are important issues. 11 refs., 3 figs

Algorithms for Reconstruction of Undersampled Atomic Force Microscopy Images Supplementary Material

DEFF Research Database (Denmark)

2017-01-01

Two Jupyter Notebooks showcasing reconstructions of undersampled atomic force microscopy images. The reconstructions were obtained using a variety of interpolation and reconstruction methods.......Two Jupyter Notebooks showcasing reconstructions of undersampled atomic force microscopy images. The reconstructions were obtained using a variety of interpolation and reconstruction methods....

Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints

Science.gov (United States)

Lee, Ho; Xing, Lei; Davidi, Ran; Li, Ruijiang; Qian, Jianguo; Lee, Rena

2012-04-01

Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an

Fast dictionary-based reconstruction for diffusion spectrum imaging.

Science.gov (United States)

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F; Yendiki, Anastasia; Wald, Lawrence L; Adalsteinsson, Elfar

2013-11-01

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.

Biologically inspired EM image alignment and neural reconstruction.

Science.gov (United States)

Knowles-Barley, Seymour; Butcher, Nancy J; Meinertzhagen, Ian A; Armstrong, J Douglas

2011-08-15

Three-dimensional reconstruction of consecutive serial-section transmission electron microscopy (ssTEM) images of neural tissue currently requires many hours of manual tracing and annotation. Several computational techniques have already been applied to ssTEM images to facilitate 3D reconstruction and ease this burden. Here, we present an alternative computational approach for ssTEM image analysis. We have used biologically inspired receptive fields as a basis for a ridge detection algorithm to identify cell membranes, synaptic contacts and mitochondria. Detected line segments are used to improve alignment between consecutive images and we have joined small segments of membrane into cell surfaces using a dynamic programming algorithm similar to the Needleman-Wunsch and Smith-Waterman DNA sequence alignment procedures. A shortest path-based approach has been used to close edges and achieve image segmentation. Partial reconstructions were automatically generated and used as a basis for semi-automatic reconstruction of neural tissue. The accuracy of partial reconstructions was evaluated and 96% of membrane could be identified at the cost of 13% false positive detections. An open-source reference implementation is available in the Supplementary information. seymour.kb@ed.ac.uk; douglas.armstrong@ed.ac.uk Supplementary data are available at Bioinformatics online.

Gadgetron: An Open Source Framework for Medical Image Reconstruction

DEFF Research Database (Denmark)

Hansen, Michael Schacht; Sørensen, Thomas Sangild

2013-01-01

This work presents a new open source framework for medical image reconstruction called the “Gadgetron.” The framework implements a flexible system for creating streaming data processing pipelines where data pass through a series of modules or “Gadgets” from raw data to reconstructed images...... with a set of dedicated toolboxes in shared libraries for medical image reconstruction. This includes generic toolboxes for data-parallel (e.g., GPU-based) execution of compute-intensive components. The basic framework architecture is independent of medical imaging modality, but this article focuses on its...

Improvement of Quality of Reconstructed Images in Multi-Frame Fresnel Digital Holography

International Nuclear Information System (INIS)

Xiao-Wei, Lu; Jing-Zhen, Li; Hong-Yi, Chen

2010-01-01

A modified reconstruction algorithm to improve the quality of reconstructed images of multi-frame Fresnel digital holography is presented. When the reference beams are plane or spherical waves with azimuth encoding, by introducing two spherical wave factors, images can be reconstructed with only one time Fourier transform. In numerical simulation, this algorithm could simplify the reconstruction process and improve the signal-to-noise ratio of the reconstructed images. In single-frame reconstruction experiments, the accurate reconstructed image is obtained with this simplified algorithm

Blind compressed sensing image reconstruction based on alternating direction method

Science.gov (United States)

Liu, Qinan; Guo, Shuxu

2018-04-01

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling.

Optimized Quasi-Interpolators for Image Reconstruction.

Science.gov (United States)

Sacht, Leonardo; Nehab, Diego

2015-12-01

We propose new quasi-interpolators for the continuous reconstruction of sampled images, combining a narrowly supported piecewise-polynomial kernel and an efficient digital filter. In other words, our quasi-interpolators fit within the generalized sampling framework and are straightforward to use. We go against standard practice and optimize for approximation quality over the entire Nyquist range, rather than focusing exclusively on the asymptotic behavior as the sample spacing goes to zero. In contrast to previous work, we jointly optimize with respect to all degrees of freedom available in both the kernel and the digital filter. We consider linear, quadratic, and cubic schemes, offering different tradeoffs between quality and computational cost. Experiments with compounded rotations and translations over a range of input images confirm that, due to the additional degrees of freedom and the more realistic objective function, our new quasi-interpolators perform better than the state of the art, at a similar computational cost.

Low dose reconstruction algorithm for differential phase contrast imaging.

Science.gov (United States)

Wang, Zhentian; Huang, Zhifeng; Zhang, Li; Chen, Zhiqiang; Kang, Kejun; Yin, Hongxia; Wang, Zhenchang; Marco, Stampanoni

2011-01-01

Differential phase contrast imaging computed tomography (DPCI-CT) is a novel x-ray inspection method to reconstruct the distribution of refraction index rather than the attenuation coefficient in weakly absorbing samples. In this paper, we propose an iterative reconstruction algorithm for DPCI-CT which benefits from the new compressed sensing theory. We first realize a differential algebraic reconstruction technique (DART) by discretizing the projection process of the differential phase contrast imaging into a linear partial derivative matrix. In this way the compressed sensing reconstruction problem of DPCI reconstruction can be transformed to a resolved problem in the transmission imaging CT. Our algorithm has the potential to reconstruct the refraction index distribution of the sample from highly undersampled projection data. Thus it can significantly reduce the dose and inspection time. The proposed algorithm has been validated by numerical simulations and actual experiments.

An accelerated threshold-based back-projection algorithm for Compton camera image reconstruction

International Nuclear Information System (INIS)

Mundy, Daniel W.; Herman, Michael G.

2011-01-01

parallel to the image plane. This effect decreases the sum of the image, thereby also affecting the mean, standard deviation, and SNR of the image. All back-projected events associated with a simulated point source intersected the voxel containing the source and the FWHM of the back-projected image was similar to that obtained from the marching method. Conclusions: The slight deficit to image quality observed with the threshold-based back-projection algorithm described here is outweighed by the 75% reduction in computation time. The implementation of this method requires the development of an optimum threshold function, which determines the overall accuracy of the method. This makes the algorithm well-suited to applications involving the reconstruction of many large images, where the time invested in threshold development is offset by the decreased image reconstruction time. Implemented in a parallel-computing environment, the threshold-based algorithm has the potential to provide real-time dose verification for radiation therapy.

Characterization of Japanese standards for myocardial sympathetic and metabolic imaging in comparison with perfusion imaging

International Nuclear Information System (INIS)

Matsuo, Shinro; Nakajima, Kenichi; Okuda, Koichi; Yamashina, Shohei; Sakata, Kazuyuki; Momose, Mitsuru; Hashimoto, Jun; Kumita, Shinichiro; Kawano, Masaya

2009-01-01

The standard patterns of myocardial radiotracer distribution of 123 I-metaiodobenzylguanidine (MIBG) and 123 I-β-methyl-p-iodophenyl-pentadecanoic acid (BMIPP) should be defined in a Japanese population. The purpose of this study was to present and provide data on the characteristics of MIBG and BMIPP with respect to myocardial single photon emission computed tomography. The normal database included 123 I-MIBG and 123 I-BMIPP imaging and a 99 mTc-sestamibi/tetrofosmin myocardial perfusion study. The projection images were transferred by digital imaging and communications in medicine (DICOM) format and reconstructed and analyzed with polar maps. The projection data from multiple centers were successfully transferred to a common format for single photon emission computed tomography (SPECT) reconstruction. When the average values were analyzed using a 17-segment model, MIBG uptake in the inferior and apical wall appeared to be slightly lower than anterior uptake (P 99m Tc-tracer uptake (P<0.05). Myocardial sympathetic nerve and metabolic scintigraphy data that were specific for the Japanese population were generated and found to be different from that of perfusion tracers. The normal database can serve as a standard for nuclear cardiology work conducted in Japan. (author)

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

Spectral image reconstruction using an edge preserving spatio-spectral Wiener estimation.

Science.gov (United States)

Urban, Philipp; Rosen, Mitchell R; Berns, Roy S

2009-08-01

Reconstruction of spectral images from camera responses is investigated using an edge preserving spatio-spectral Wiener estimation. A Wiener denoising filter and a spectral reconstruction Wiener filter are combined into a single spatio-spectral filter using local propagation of the noise covariance matrix. To preserve edges the local mean and covariance matrix of camera responses is estimated by bilateral weighting of neighboring pixels. We derive the edge-preserving spatio-spectral Wiener estimation by means of Bayesian inference and show that it fades into the standard Wiener reflectance estimation shifted by a constant reflectance in case of vanishing noise. Simulation experiments conducted on a six-channel camera system and on multispectral test images show the performance of the filter, especially for edge regions. A test implementation of the method is provided as a MATLAB script at the first author's website.

Ultra-Fast Image Reconstruction of Tomosynthesis Mammography Using GPU.

Science.gov (United States)

Arefan, D; Talebpour, A; Ahmadinejhad, N; Kamali Asl, A

2015-06-01

Digital Breast Tomosynthesis (DBT) is a technology that creates three dimensional (3D) images of breast tissue. Tomosynthesis mammography detects lesions that are not detectable with other imaging systems. If image reconstruction time is in the order of seconds, we can use Tomosynthesis systems to perform Tomosynthesis-guided Interventional procedures. This research has been designed to study ultra-fast image reconstruction technique for Tomosynthesis Mammography systems using Graphics Processing Unit (GPU). At first, projections of Tomosynthesis mammography have been simulated. In order to produce Tomosynthesis projections, it has been designed a 3D breast phantom from empirical data. It is based on MRI data in its natural form. Then, projections have been created from 3D breast phantom. The image reconstruction algorithm based on FBP was programmed with C++ language in two methods using central processing unit (CPU) card and the Graphics Processing Unit (GPU). It calculated the time of image reconstruction in two kinds of programming (using CPU and GPU).

Ultra-Fast Image Reconstruction of Tomosynthesis Mammography Using GPU

Directory of Open Access Journals (Sweden)

Arefan D

2015-06-01

Full Text Available Digital Breast Tomosynthesis (DBT is a technology that creates three dimensional (3D images of breast tissue. Tomosynthesis mammography detects lesions that are not detectable with other imaging systems. If image reconstruction time is in the order of seconds, we can use Tomosynthesis systems to perform Tomosynthesis-guided Interventional procedures. This research has been designed to study ultra-fast image reconstruction technique for Tomosynthesis Mammography systems using Graphics Processing Unit (GPU. At first, projections of Tomosynthesis mammography have been simulated. In order to produce Tomosynthesis projections, it has been designed a 3D breast phantom from empirical data. It is based on MRI data in its natural form. Then, projections have been created from 3D breast phantom. The image reconstruction algorithm based on FBP was programmed with C++ language in two methods using central processing unit (CPU card and the Graphics Processing Unit (GPU. It calculated the time of image reconstruction in two kinds of programming (using CPU and GPU.

Superiority of CT imaging reconstruction on Linux OS

International Nuclear Information System (INIS)

Lin Shaochun; Yan Xufeng; Wu Tengfang; Luo Xiaomei; Cai Huasong

2010-01-01

Objective: To compare the speed of CT reconstruction using the Linux and Windows OS. Methods: Shepp-Logan head phantom in different pixel size was projected to obtain the sinogram by using the inverse Fourier transformation, filtered back projection and Radon transformation on both Linux and Windows OS. Results: CT image reconstruction using the Linux operating system was significantly better and more efficient than Windows. Conclusion: CT image reconstruction using the Linux operating system is more efficient. (authors)

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.

Image quality improvements using adaptive statistical iterative reconstruction for evaluating chronic myocardial infarction using iodine density images with spectral CT.

Science.gov (United States)

Kishimoto, Junichi; Ohta, Yasutoshi; Kitao, Shinichiro; Watanabe, Tomomi; Ogawa, Toshihide

2018-04-01

Single-source dual-energy CT (ssDECT) allows the reconstruction of iodine density images (IDIs) from projection based computing. We hypothesized that adding adaptive statistical iterative reconstruction (ASiR) could improve image quality. The aim of our study was to evaluate the effect and determine the optimal blend percentages of ASiR for IDI of myocardial late iodine enhancement (LIE) in the evaluation of chronic myocardial infarction using ssDECT. A total of 28 patients underwent cardiac LIE using a ssDECT scanner. IDIs between 0 and 100% of ASiR contributions in 10% increments were reconstructed. The signal-to-noise ratio (SNR) of remote myocardia and the contrast-to-noise ratio (CNR) of infarcted myocardia were measured. Transmural extent of infarction was graded using a 5-point scale. The SNR, CNR, and transmural extent were assessed for each ASiR contribution ratio. The transmural extents were compared with MRI as a reference standard. Compared to 0% ASiR, the use of 20-100% ASiR resulted in a reduction of image noise (p ASiR images, reconstruction with 100% ASiR image showed the highest improvement in SNR (229%; p ASiR above 80% showed the highest ratio (73.7%) of accurate transmural extent classification. In conclusion, ASiR intensity of 80-100% in IDIs can improve image quality without changes in signal and maximizes the accuracy of transmural extent in infarcted myocardium.

Influence of image reconstruction methods on statistical parametric mapping of brain PET images

International Nuclear Information System (INIS)

Yin Dayi; Chen Yingmao; Yao Shulin; Shao Mingzhe; Yin Ling; Tian Jiahe; Cui Hongyan

2007-01-01

Objective: Statistic parametric mapping (SPM) was widely recognized as an useful tool in brain function study. The aim of this study was to investigate if imaging reconstruction algorithm of PET images could influence SPM of brain. Methods: PET imaging of whole brain was performed in six normal volunteers. Each volunteer had two scans with true and false acupuncturing. The PET scans were reconstructed using ordered subsets expectation maximization (OSEM) and filtered back projection (FBP) with 3 varied parameters respectively. The images were realigned, normalized and smoothed using SPM program. The difference between true and false acupuncture scans was tested using a matched pair t test at every voxel. Results: (1) SPM corrected multiple comparison (P corrected uncorrected <0.001): SPM derived from the images with different reconstruction method were different. The largest difference, in number and position of the activated voxels, was noticed between FBP and OSEM re- construction algorithm. Conclusions: The method of PET image reconstruction could influence the results of SPM uncorrected multiple comparison. Attention should be paid when the conclusion was drawn using SPM uncorrected multiple comparison. (authors)

A combinational fast algorithm for image reconstruction

International Nuclear Information System (INIS)

Wu Zhongquan

1987-01-01

A combinational fast algorithm has been developed in order to increase the speed of reconstruction. First, an interpolation method based on B-spline functions is used in image reconstruction. Next, the influence of the boundary conditions assumed here on the interpolation of filtered projections and on the image reconstruction is discussed. It is shown that this boundary condition has almost no influence on the image in the central region of the image space, because the error of interpolation rapidly decreases by a factor of ten in shifting two pixels from the edge toward the center. In addition, a fast algorithm for computing the detecting angle has been used with the mentioned interpolation algorithm, and the cost for detecting angle computaton is reduced by a factor of two. The implementation results show that in the same subjective and objective fidelity, the computational cost for the interpolation using this algorithm is about one-twelfth of the conventional algorithm

Dual-source CT coronary imaging in heart transplant recipients: image quality and optimal reconstruction interval

International Nuclear Information System (INIS)

Bastarrika, Gorka; Arraiza, Maria; Pueyo, Jesus C.; Cecco, Carlo N. de; Ubilla, Matias; Mastrobuoni, Stefano; Rabago, Gregorio

2008-01-01

The image quality and optimal reconstruction interval for coronary arteries in heart transplant recipients undergoing non-invasive dual-source computed tomography (DSCT) coronary angiography was evaluated. Twenty consecutive heart transplant recipients who underwent DSCT coronary angiography were included (19 male, one female; mean age 63.1±10.7 years). Data sets were reconstructed in 5% steps from 30% to 80% of the R-R interval. Two blinded independent observers assessed the image quality of each coronary segments using a five-point scale (from 0 = not evaluative to 4=excellent quality). A total of 289 coronary segments in 20 heart transplant recipients were evaluated. Mean heart rate during the scan was 89.1±10.4 bpm. At the best reconstruction interval, diagnostic image quality (score ≥2) was obtained in 93.4% of the coronary segments (270/289) with a mean image quality score of 3.04± 0.63. Systolic reconstruction intervals provided better image quality scores than diastolic reconstruction intervals (overall mean quality scores obtained with the systolic and diastolic reconstructions 3.03±1.06 and 2.73±1.11, respectively; P<0.001). Different systolic reconstruction intervals (35%, 40%, 45% of RR interval) did not yield to significant differences in image quality scores for the coronary segments (P=0.74). Reconstructions obtained at the systolic phase of the cardiac cycle allowed excellent diagnostic image quality coronary angiograms in heart transplant recipients undergoing DSCT coronary angiography. (orig.)

Quantitative reconstruction from a single diffraction-enhanced image

International Nuclear Information System (INIS)

Paganin, D.M.; Lewis, R.A.; Kitchen, M.

2003-01-01

Full text: We develop an algorithm for using a single diffraction-enhanced image (DEI) to obtain a quantitative reconstruction of the projected thickness of a single-material sample which is embedded within a substrate of approximately constant thickness. This algorithm is used to quantitatively map inclusions in a breast phantom, from a single synchrotron DEI image. In particular, the reconstructed images quantitatively represent the projected thickness in the bulk of the sample, in contrast to DEI images which greatly emphasise sharp edges (high spatial frequencies). In the context of an ultimate aim of improved methods for breast cancer detection, the reconstructions are potentially of greater diagnostic value compared to the DEI data. Lastly, we point out that the methods of analysis presented here are also applicable to the quantitative analysis of differential interference contrast (DIC) images

Image reconstruction under non-Gaussian noise

DEFF Research Database (Denmark)

Sciacchitano, Federica

During acquisition and transmission, images are often blurred and corrupted by noise. One of the fundamental tasks of image processing is to reconstruct the clean image from a degraded version. The process of recovering the original image from the data is an example of inverse problem. Due...... to the ill-posedness of the problem, the simple inversion of the degradation model does not give any good reconstructions. Therefore, to deal with the ill-posedness it is necessary to use some prior information on the solution or the model and the Bayesian approach. Additive Gaussian noise has been......D thesis intends to solve some of the many open questions for image restoration under non-Gaussian noise. The two main kinds of noise studied in this PhD project are the impulse noise and the Cauchy noise. Impulse noise is due to for instance the malfunctioning pixel elements in the camera sensors, errors...

Application of the fractional Fourier transform to image reconstruction in MRI.

Science.gov (United States)

Parot, Vicente; Sing-Long, Carlos; Lizama, Carlos; Tejos, Cristian; Uribe, Sergio; Irarrazaval, Pablo

2012-07-01

The classic paradigm for MRI requires a homogeneous B(0) field in combination with linear encoding gradients. Distortions are produced when the B(0) is not homogeneous, and several postprocessing techniques have been developed to correct them. Field homogeneity is difficult to achieve, particularly for short-bore magnets and higher B(0) fields. Nonlinear magnetic components can also arise from concomitant fields, particularly in low-field imaging, or intentionally used for nonlinear encoding. In any of these situations, the second-order component is key, because it constitutes the first step to approximate higher-order fields. We propose to use the fractional Fourier transform for analyzing and reconstructing the object's magnetization under the presence of quadratic fields. The fractional fourier transform provides a precise theoretical framework for this. We show how it can be used for reconstruction and for gaining a better understanding of the quadratic field-induced distortions, including examples of reconstruction for simulated and in vivo data. The obtained images have improved quality compared with standard Fourier reconstructions. The fractional fourier transform opens a new paradigm for understanding the MR signal generated by an object under a quadratic main field or nonlinear encoding. Copyright © 2011 Wiley Periodicals, Inc.

Automatic cortical surface reconstruction of high-resolution T1 echo planar imaging data.

Science.gov (United States)

Renvall, Ville; Witzel, Thomas; Wald, Lawrence L; Polimeni, Jonathan R

2016-07-01

Echo planar imaging (EPI) is the method of choice for the majority of functional magnetic resonance imaging (fMRI), yet EPI is prone to geometric distortions and thus misaligns with conventional anatomical reference data. The poor geometric correspondence between functional and anatomical data can lead to severe misplacements and corruption of detected activation patterns. However, recent advances in imaging technology have provided EPI data with increasing quality and resolution. Here we present a framework for deriving cortical surface reconstructions directly from high-resolution EPI-based reference images that provide anatomical models exactly geometric distortion-matched to the functional data. Anatomical EPI data with 1mm isotropic voxel size were acquired using a fast multiple inversion recovery time EPI sequence (MI-EPI) at 7T, from which quantitative T1 maps were calculated. Using these T1 maps, volumetric data mimicking the tissue contrast of standard anatomical data were synthesized using the Bloch equations, and these T1-weighted data were automatically processed using FreeSurfer. The spatial alignment between T2(⁎)-weighted EPI data and the synthetic T1-weighted anatomical MI-EPI-based images was improved compared to the conventional anatomical reference. In particular, the alignment near the regions vulnerable to distortion due to magnetic susceptibility differences was improved, and sampling of the adjacent tissue classes outside of the cortex was reduced when using cortical surface reconstructions derived directly from the MI-EPI reference. The MI-EPI method therefore produces high-quality anatomical data that can be automatically segmented with standard software, providing cortical surface reconstructions that are geometrically matched to the BOLD fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.

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.)

Fast parallel algorithm for CT image reconstruction.

Science.gov (United States)

Flores, Liubov A; Vidal, Vicent; Mayo, Patricia; Rodenas, Francisco; Verdú, Gumersindo

2012-01-01

In X-ray computed tomography (CT) the X rays are used to obtain the projection data needed to generate an image of the inside of an object. The image can be generated with different techniques. Iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions and from a small number of projections. Their use may be important in portable scanners for their functionality in emergency situations. However, in practice, these methods are not widely used due to the high computational cost of their implementation. In this work we analyze iterative parallel image reconstruction with the Portable Extensive Toolkit for Scientific computation (PETSc).

Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging

Science.gov (United States)

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F.; Yendiki, Anastasia; Wald, Lawrence L.; Adalsteinsson, Elfar

2015-01-01

Diffusion Spectrum Imaging (DSI) reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation (TV) transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using Matlab running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using Principal Component Analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm. PMID:23846466

Accelerating image reconstruction in three-dimensional optoacoustic tomography on graphics processing units.

Science.gov (United States)

Wang, Kun; Huang, Chao; Kao, Yu-Jiun; Chou, Cheng-Ying; Oraevsky, Alexander A; Anastasio, Mark A

2013-02-01

Optoacoustic tomography (OAT) is inherently a three-dimensional (3D) inverse problem. However, most studies of OAT image reconstruction still employ two-dimensional imaging models. One important reason is because 3D image reconstruction is computationally burdensome. The aim of this work is to accelerate existing image reconstruction algorithms for 3D OAT by use of parallel programming techniques. Parallelization strategies are proposed to accelerate a filtered backprojection (FBP) algorithm and two different pairs of projection/backprojection operations that correspond to two different numerical imaging models. The algorithms are designed to fully exploit the parallel computing power of graphics processing units (GPUs). In order to evaluate the parallelization strategies for the projection/backprojection pairs, an iterative image reconstruction algorithm is implemented. Computer simulation and experimental studies are conducted to investigate the computational efficiency and numerical accuracy of the developed algorithms. The GPU implementations improve the computational efficiency by factors of 1000, 125, and 250 for the FBP algorithm and the two pairs of projection/backprojection operators, respectively. Accurate images are reconstructed by use of the FBP and iterative image reconstruction algorithms from both computer-simulated and experimental data. Parallelization strategies for 3D OAT image reconstruction are proposed for the first time. These GPU-based implementations significantly reduce the computational time for 3D image reconstruction, complementing our earlier work on 3D OAT iterative image reconstruction.

Common-mask guided image reconstruction (c-MGIR) for enhanced 4D cone-beam computed tomography.

Science.gov (United States)

Park, Justin C; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Li, Jonathan G; Liu, Chihray; Lu, Bo

2015-12-07

Compared to 3D cone beam computed tomography (3D CBCT), the image quality of commercially available four-dimensional (4D) CBCT is severely impaired due to the insufficient amount of projection data available for each phase. Since the traditional Feldkamp-Davis-Kress (FDK)-based algorithm is infeasible for reconstructing high quality 4D CBCT images with limited projections, investigators had developed several compress-sensing (CS) based algorithms to improve image quality. The aim of this study is to develop a novel algorithm which can provide better image quality than the FDK and other CS based algorithms with limited projections. We named this algorithm 'the common mask guided image reconstruction' (c-MGIR).In c-MGIR, the unknown CBCT volume is mathematically modeled as a combination of phase-specific motion vectors and phase-independent static vectors. The common-mask matrix, which is the key concept behind the c-MGIR algorithm, separates the common static part across all phase images from the possible moving part in each phase image. The moving part and the static part of the volumes were then alternatively updated by solving two sub-minimization problems iteratively. As the novel mathematical transformation allows the static volume and moving volumes to be updated (during each iteration) with global projections and 'well' solved static volume respectively, the algorithm was able to reduce the noise and under-sampling artifact (an issue faced by other algorithms) to the maximum extent. To evaluate the performance of our proposed c-MGIR, we utilized imaging data from both numerical phantoms and a lung cancer patient. The qualities of the images reconstructed with c-MGIR were compared with (1) standard FDK algorithm, (2) conventional total variation (CTV) based algorithm, (3) prior image constrained compressed sensing (PICCS) algorithm, and (4) motion-map constrained image reconstruction (MCIR) algorithm, respectively. To improve the efficiency of the algorithm

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

International Nuclear Information System (INIS)

Xu, Qiaofeng; Sawatzky, Alex; Anastasio, Mark A.; Yang, Deshan; Tan, Jun

2016-01-01

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

Science.gov (United States)

Xu, Qiaofeng; Yang, Deshan; Tan, Jun; Sawatzky, Alex; Anastasio, Mark A.

2016-01-01

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated

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.

Development of a technique for three-dimensional image reconstruction from emission computed tomograms (ECT)

International Nuclear Information System (INIS)

Gerischer, R.

1987-01-01

The described technique for three-dimensional image reconstruction from ECT sections is based on a simple procedure, which can be carried out with the aid of any standard-type computer used in nuclear medicine and requires no sophisticated arithmetic approach. (TRV) [de

Three dimensional image reconstruction in the Fourier domain

International Nuclear Information System (INIS)

Stearns, C.W.; Chesler, D.A.; Brownell, G.L.

1987-01-01

Filtered backprojection reconstruction algorithms are based upon the relationship between the Fourier transform of the imaged object and the Fourier transforms of its projections. A new reconstruction algorithm has been developed which performs the image assembly operation in Fourier space, rather than in image space by backprojection. This represents a significant decrease in the number of operations required to assemble the image. The new Fourier domain algorithm has resolution comparable to the filtered backprojection algorithm, and, after correction by a pointwise multiplication, demonstrates proper recovery throughout image space. Although originally intended for three-dimensional imaging applications, the Fourier domain algorithm can also be developed for two-dimensional imaging applications such as planar positron imaging systems

Radionuclide imaging with coded apertures and three-dimensional image reconstruction from focal-plane tomography

International Nuclear Information System (INIS)

Chang, L.T.

1976-05-01

Two techniques for radionuclide imaging and reconstruction have been studied;; both are used for improvement of depth resolution. The first technique is called coded aperture imaging, which is a technique of tomographic imaging. The second technique is a special 3-D image reconstruction method which is introduced as an improvement to the so called focal-plane tomography

Reconstructed coronal views of CT and isotopic images of the pancreas

International Nuclear Information System (INIS)

Kasuga, Toshio; Kobayashi, Toshio; Nakanishi, Fumiko

1980-01-01

To compare functional images of the pancreas by scintigraphy with morphological views of the pancreas by CT, CT coronal views of the pancreas were reconstructed. As CT coronal views were reconstructed from the routine scanning, there was a problem in longitudinal spatial resolution. However, almost satisfactory total images of the pancreas were obtained by improving images adequately. In 27 patients whose diseases had been confirmed, it was easy to compare pancreatic scintigrams with pancreatic CT images by using reconstructed CT coronal views, and information which had not been obtained by original CT images could be obtained by using reconstructed CT coronal views. Especially, defects on pancreatic images and the shape of pancreas which had not been visualized clearly by scintigraphy alone could be visualized by using reconstructed CT coronal views of the pancreas. (Tsunoda, M.)

Denoising of B1+ field maps for noise-robust image reconstruction in electrical properties tomography

International Nuclear Information System (INIS)

Michel, Eric; Hernandez, Daniel; Cho, Min Hyoung; Lee, Soo Yeol

2014-01-01

Purpose: To validate the use of adaptive nonlinear filters in reconstructing conductivity and permittivity images from the noisy B 1 + maps in electrical properties tomography (EPT). Methods: In EPT, electrical property images are computed by taking Laplacian of the B 1 + maps. To mitigate the noise amplification in computing the Laplacian, the authors applied adaptive nonlinear denoising filters to the measured complex B 1 + maps. After the denoising process, they computed the Laplacian by central differences. They performed EPT experiments on phantoms and a human brain at 3 T along with corresponding EPT simulations on finite-difference time-domain models. They evaluated the EPT images comparing them with the ones obtained by previous EPT reconstruction methods. Results: In both the EPT simulations and experiments, the nonlinear filtering greatly improved the EPT image quality when evaluated in terms of the mean and standard deviation of the electrical property values at the regions of interest. The proposed method also improved the overall similarity between the reconstructed conductivity images and the true shapes of the conductivity distribution. Conclusions: The nonlinear denoising enabled us to obtain better-quality EPT images of the phantoms and the human brain at 3 T

TREE STEM RECONSTRUCTION USING VERTICAL FISHEYE IMAGES: A PRELIMINARY STUDY

Directory of Open Access Journals (Sweden)

A. Berveglieri

2016-06-01

Full Text Available A preliminary study was conducted to assess a tree stem reconstruction technique with panoramic images taken with fisheye lenses. The concept is similar to the Structure from Motion (SfM technique, but the acquisition and data preparation rely on fisheye cameras to generate a vertical image sequence with height variations of the camera station. Each vertical image is rectified to four vertical planes, producing horizontal lateral views. The stems in the lateral view are rectified to the same scale in the image sequence to facilitate image matching. Using bundle adjustment, the stems are reconstructed, enabling later measurement and extraction of several attributes. The 3D reconstruction was performed with the proposed technique and compared with SfM. The preliminary results showed that the stems were correctly reconstructed by using the lateral virtual images generated from the vertical fisheye images and with the advantage of using fewer images and taken from one single station.

Qualitative and quantitative analysis of reconstructed images using projections with noises

International Nuclear Information System (INIS)

Lopes, R.T.; Assis, J.T. de

1988-01-01

The reconstruction of a two-dimencional image from one-dimensional projections in an analytic algorithm ''convolution method'' is simulated on a microcomputer. In this work it was analysed the effects caused in the reconstructed image in function of the number of projections and noise level added to the projection data. Qualitative and quantitative (distortion and image noise) comparison were done with the original image and the reconstructed images. (author) [pt

Improving parallel imaging by jointly reconstructing multi-contrast data.

Science.gov (United States)

Bilgic, Berkin; Kim, Tae Hyung; Liao, Congyu; Manhard, Mary Kate; Wald, Lawrence L; Haldar, Justin P; Setsompop, Kawin

2018-08-01

To develop parallel imaging techniques that simultaneously exploit coil sensitivity encoding, image phase prior information, similarities across multiple images, and complementary k-space sampling for highly accelerated data acquisition. We introduce joint virtual coil (JVC)-generalized autocalibrating partially parallel acquisitions (GRAPPA) to jointly reconstruct data acquired with different contrast preparations, and show its application in 2D, 3D, and simultaneous multi-slice (SMS) acquisitions. We extend the joint parallel imaging concept to exploit limited support and smooth phase constraints through Joint (J-) LORAKS formulation. J-LORAKS allows joint parallel imaging from limited autocalibration signal region, as well as permitting partial Fourier sampling and calibrationless reconstruction. We demonstrate highly accelerated 2D balanced steady-state free precession with phase cycling, SMS multi-echo spin echo, 3D multi-echo magnetization-prepared rapid gradient echo, and multi-echo gradient recalled echo acquisitions in vivo. Compared to conventional GRAPPA, proposed joint acquisition/reconstruction techniques provide more than 2-fold reduction in reconstruction error. JVC-GRAPPA takes advantage of additional spatial encoding from phase information and image similarity, and employs different sampling patterns across acquisitions. J-LORAKS achieves a more parsimonious low-rank representation of local k-space by considering multiple images as additional coils. Both approaches provide dramatic improvement in artifact and noise mitigation over conventional single-contrast parallel imaging reconstruction. Magn Reson Med 80:619-632, 2018. © 2018 International Society for Magnetic Resonance in Medicine. © 2018 International Society for Magnetic Resonance in Medicine.

Silhouette-based approach of 3D image reconstruction for automated image acquisition using robotic arm

Science.gov (United States)

Azhar, N.; Saad, W. H. M.; Manap, N. A.; Saad, N. M.; Syafeeza, A. R.

2017-06-01

This study presents the approach of 3D image reconstruction using an autonomous robotic arm for the image acquisition process. A low cost of the automated imaging platform is created using a pair of G15 servo motor connected in series to an Arduino UNO as a main microcontroller. Two sets of sequential images were obtained using different projection angle of the camera. The silhouette-based approach is used in this study for 3D reconstruction from the sequential images captured from several different angles of the object. Other than that, an analysis based on the effect of different number of sequential images on the accuracy of 3D model reconstruction was also carried out with a fixed projection angle of the camera. The effecting elements in the 3D reconstruction are discussed and the overall result of the analysis is concluded according to the prototype of imaging platform.

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

PET image reconstruction using multi-parametric anato-functional priors

Science.gov (United States)

Mehranian, Abolfazl; Belzunce, Martin A.; Niccolini, Flavia; Politis, Marios; Prieto, Claudia; Turkheimer, Federico; Hammers, Alexander; Reader, Andrew J.

2017-08-01

In this study, we investigate the application of multi-parametric anato-functional (MR-PET) priors for the maximum a posteriori (MAP) reconstruction of brain PET data in order to address the limitations of the conventional anatomical priors in the presence of PET-MR mismatches. In addition to partial volume correction benefits, the suitability of these priors for reconstruction of low-count PET data is also introduced and demonstrated, comparing to standard maximum-likelihood (ML) reconstruction of high-count data. The conventional local Tikhonov and total variation (TV) priors and current state-of-the-art anatomical priors including the Kaipio, non-local Tikhonov prior with Bowsher and Gaussian similarity kernels are investigated and presented in a unified framework. The Gaussian kernels are calculated using both voxel- and patch-based feature vectors. To cope with PET and MR mismatches, the Bowsher and Gaussian priors are extended to multi-parametric priors. In addition, we propose a modified joint Burg entropy prior that by definition exploits all parametric information in the MAP reconstruction of PET data. The performance of the priors was extensively evaluated using 3D simulations and two clinical brain datasets of [18F]florbetaben and [18F]FDG radiotracers. For simulations, several anato-functional mismatches were intentionally introduced between the PET and MR images, and furthermore, for the FDG clinical dataset, two PET-unique active tumours were embedded in the PET data. Our simulation results showed that the joint Burg entropy prior far outperformed the conventional anatomical priors in terms of preserving PET unique lesions, while still reconstructing functional boundaries with corresponding MR boundaries. In addition, the multi-parametric extension of the Gaussian and Bowsher priors led to enhanced preservation of edge and PET unique features and also an improved bias-variance performance. In agreement with the simulation results, the clinical results

A PET reconstruction formulation that enforces non-negativity in projection space for bias reduction in Y-90 imaging

Science.gov (United States)

Lim, Hongki; Dewaraja, Yuni K.; Fessler, Jeffrey A.

2018-02-01

Most existing PET image reconstruction methods impose a nonnegativity constraint in the image domain that is natural physically, but can lead to biased reconstructions. This bias is particularly problematic for Y-90 PET because of the low probability positron production and high random coincidence fraction. This paper investigates a new PET reconstruction formulation that enforces nonnegativity of the projections instead of the voxel values. This formulation allows some negative voxel values, thereby potentially reducing bias. Unlike the previously reported NEG-ML approach that modifies the Poisson log-likelihood to allow negative values, the new formulation retains the classical Poisson statistical model. To relax the non-negativity constraint embedded in the standard methods for PET reconstruction, we used an alternating direction method of multipliers (ADMM). Because choice of ADMM parameters can greatly influence convergence rate, we applied an automatic parameter selection method to improve the convergence speed. We investigated the methods using lung to liver slices of XCAT phantom. We simulated low true coincidence count-rates with high random fractions corresponding to the typical values from patient imaging in Y-90 microsphere radioembolization. We compared our new methods with standard reconstruction algorithms and NEG-ML and a regularized version thereof. Both our new method and NEG-ML allow more accurate quantification in all volumes of interest while yielding lower noise than the standard method. The performance of NEG-ML can degrade when its user-defined parameter is tuned poorly, while the proposed algorithm is robust to any count level without requiring parameter tuning.

High-definition computed tomography for coronary artery stents imaging: Initial evaluation of the optimal reconstruction algorithm

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaoming, E-mail: mmayzy2008@126.com; Li, Tao, E-mail: litaofeivip@163.com; Li, Xin, E-mail: lx0803@sina.com.cn; Zhou, Weihua, E-mail: wangxue0606@gmail.com

2015-05-15

Highlights: • High-resolution scan mode is appropriate for imaging coronary stent. • HD-detail reconstruction algorithm is stent-dedicated kernel. • The intrastent lumen visibility also depends on stent diameter and material. - Abstract: Objective: The aim of this study was to evaluate the in vivo performance of four image reconstruction algorithms in a high-definition CT (HDCT) scanner with improved spatial resolution for the evaluation of coronary artery stents and intrastent lumina. Materials and methods: Thirty-nine consecutive patients with a total of 71 implanted coronary stents underwent coronary CT angiography (CCTA) on a HDCT (Discovery CT 750 HD; GE Healthcare) with the high-resolution scanning mode. Four different reconstruction algorithms (HD-stand, HD-detail; HD-stand-plus; HD-detail-plus) were applied to reconstruct the stented coronary arteries. Image quality for stent characterization was assessed. Image noise and intrastent luminal diameter were measured. The relationship between the measurement of inner stent diameter (ISD) and the true stent diameter (TSD) and stent type were analysed. Results: The stent-dedicated kernel (HD-detail) offered the highest percentage (53.5%) of good image quality for stent characterization and the highest ratio (68.0 ± 8.4%) of visible stent lumen/true stent lumen for luminal diameter measurement at the expense of an increased overall image noise. The Pearson correlation coefficient between the ISD and TSD measurement and spearman correlation coefficient between the ISD measurement and stent type were 0.83 and 0.48, respectively. Conclusions: Compared with standard reconstruction algorithms, high-definition CT imaging technique with dedicated high-resolution reconstruction algorithm provides more accurate stent characterization and intrastent luminal diameter measurement.

Priori mask guided image reconstruction (p-MGIR) for ultra-low dose cone-beam computed tomography

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Park, Justin C.; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Kahler, Darren L.; Liu, Chihray; Lu, Bo

2015-11-01

Recently, the compressed sensing (CS) based iterative reconstruction method has received attention because of its ability to reconstruct cone beam computed tomography (CBCT) images with good quality using sparsely sampled or noisy projections, thus enabling dose reduction. However, some challenges remain. In particular, there is always a tradeoff between image resolution and noise/streak artifact reduction based on the amount of regularization weighting that is applied uniformly across the CBCT volume. The purpose of this study is to develop a novel low-dose CBCT reconstruction algorithm framework called priori mask guided image reconstruction (p-MGIR) that allows reconstruction of high-quality low-dose CBCT images while preserving the image resolution. In p-MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions: (1) where anatomical structures are complex, and (2) where intensities are relatively uniform. The priori mask, which is the key concept of the p-MGIR algorithm, was defined as the matrix that distinguishes between the two separate CBCT regions where the resolution needs to be preserved and where streak or noise needs to be suppressed. We then alternately updated each part of image by solving two sub-minimization problems iteratively, where one minimization was focused on preserving the edge information of the first part while the other concentrated on the removal of noise/artifacts from the latter part. To evaluate the performance of the p-MGIR algorithm, a numerical head-and-neck phantom, a Catphan 600 physical phantom, and a clinical head-and-neck cancer case were used for analysis. The results were compared with the standard Feldkamp-Davis-Kress as well as conventional CS-based algorithms. Examination of the p-MGIR algorithm showed that high-quality low-dose CBCT images can be reconstructed without compromising the image resolution. For both phantom and the patient cases, the p-MGIR is able to achieve a clinically

A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction

Science.gov (United States)

Huang, Xiaokun; Zhang, You; Wang, Jing

2018-02-01

Reconstructing four-dimensional cone-beam computed tomography (4D-CBCT) images directly from respiratory phase-sorted traditional 3D-CBCT projections can capture target motion trajectory, reduce motion artifacts, and reduce imaging dose and time. However, the limited numbers of projections in each phase after phase-sorting decreases CBCT image quality under traditional reconstruction techniques. To address this problem, we developed a simultaneous motion estimation and image reconstruction (SMEIR) algorithm, an iterative method that can reconstruct higher quality 4D-CBCT images from limited projections using an inter-phase intensity-driven motion model. However, the accuracy of the intensity-driven motion model is limited in regions with fine details whose quality is degraded due to insufficient projection number, which consequently degrades the reconstructed image quality in corresponding regions. In this study, we developed a new 4D-CBCT reconstruction algorithm by introducing biomechanical modeling into SMEIR (SMEIR-Bio) to boost the accuracy of the motion model in regions with small fine structures. The biomechanical modeling uses tetrahedral meshes to model organs of interest and solves internal organ motion using tissue elasticity parameters and mesh boundary conditions. This physics-driven approach enhances the accuracy of solved motion in the organ’s fine structures regions. This study used 11 lung patient cases to evaluate the performance of SMEIR-Bio, making both qualitative and quantitative comparisons between SMEIR-Bio, SMEIR, and the algebraic reconstruction technique with total variation regularization (ART-TV). The reconstruction results suggest that SMEIR-Bio improves the motion model’s accuracy in regions containing small fine details, which consequently enhances the accuracy and quality of the reconstructed 4D-CBCT images.

Influence of heart rate on diagnostic accuracy and image quality of 16-slice CT coronary angiography: comparison of multisegment and halfscan reconstruction approaches

International Nuclear Information System (INIS)

Dewey, Marc; Teige, Florian; Hamm, Bernd; Laule, Michael

2007-01-01

The lower the heart rate the better image quality in multislice computed tomography (MSCT) coronary angiography. We prospectively assessed the influence of heart rate on per-patient diagnostic accuracy and image quality of MSCT coronary angiography and compared adaptive multisegment and standard halfscan reconstruction. A consecutive cohort of 126 patients scheduled to undergo conventional coronary angiography was examined with 16-slice CT. For all heart rate groups, per-patient diagnostic accuracy was significantly higher for multisegment than halfscan reconstruction with values of 95 vs. 79% (p 74 bpm, 41 patients). Differences in diagnostic accuracy between adjacent heart rate groups were only significant for halfscan reconstruction for the comparison between the 65-74 and >74 bpm group (p < 0.05). The vessel lengths free of motion artifacts were significantly longer with multisegment reconstruction in all heart rate groups and for all coronary arteries (p < 0.005). For noninvasive MSCT coronary angiography, both per-patient diagnostic accuracy and image quality decline with increasing heart rate, and multisegment reconstruction at high heart rates yields similar results as standard halfscan reconstruction at low heart rates. (orig.)

Level-set-based reconstruction algorithm for EIT lung images: first clinical results.

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Rahmati, Peyman; Soleimani, Manuchehr; Pulletz, Sven; Frerichs, Inéz; Adler, Andy

2012-05-01

We show the first clinical results using the level-set-based reconstruction algorithm for electrical impedance tomography (EIT) data. The level-set-based reconstruction method (LSRM) allows the reconstruction of non-smooth interfaces between image regions, which are typically smoothed by traditional voxel-based reconstruction methods (VBRMs). We develop a time difference formulation of the LSRM for 2D images. The proposed reconstruction method is applied to reconstruct clinical EIT data of a slow flow inflation pressure-volume manoeuvre in lung-healthy and adult lung-injury patients. Images from the LSRM and the VBRM are compared. The results show comparable reconstructed images, but with an improved ability to reconstruct sharp conductivity changes in the distribution of lung ventilation using the LSRM.

Level-set-based reconstruction algorithm for EIT lung images: first clinical results

International Nuclear Information System (INIS)

Rahmati, Peyman; Adler, Andy; Soleimani, Manuchehr; Pulletz, Sven; Frerichs, Inéz

2012-01-01

We show the first clinical results using the level-set-based reconstruction algorithm for electrical impedance tomography (EIT) data. The level-set-based reconstruction method (LSRM) allows the reconstruction of non-smooth interfaces between image regions, which are typically smoothed by traditional voxel-based reconstruction methods (VBRMs). We develop a time difference formulation of the LSRM for 2D images. The proposed reconstruction method is applied to reconstruct clinical EIT data of a slow flow inflation pressure–volume manoeuvre in lung-healthy and adult lung-injury patients. Images from the LSRM and the VBRM are compared. The results show comparable reconstructed images, but with an improved ability to reconstruct sharp conductivity changes in the distribution of lung ventilation using the LSRM. (paper)

High-definition computed tomography for coronary artery stents imaging: Initial evaluation of the optimal reconstruction algorithm.

Science.gov (United States)

Cui, Xiaoming; Li, Tao; Li, Xin; Zhou, Weihua

2015-05-01

The aim of this study was to evaluate the in vivo performance of four image reconstruction algorithms in a high-definition CT (HDCT) scanner with improved spatial resolution for the evaluation of coronary artery stents and intrastent lumina. Thirty-nine consecutive patients with a total of 71 implanted coronary stents underwent coronary CT angiography (CCTA) on a HDCT (Discovery CT 750 HD; GE Healthcare) with the high-resolution scanning mode. Four different reconstruction algorithms (HD-stand, HD-detail; HD-stand-plus; HD-detail-plus) were applied to reconstruct the stented coronary arteries. Image quality for stent characterization was assessed. Image noise and intrastent luminal diameter were measured. The relationship between the measurement of inner stent diameter (ISD) and the true stent diameter (TSD) and stent type were analysed. The stent-dedicated kernel (HD-detail) offered the highest percentage (53.5%) of good image quality for stent characterization and the highest ratio (68.0±8.4%) of visible stent lumen/true stent lumen for luminal diameter measurement at the expense of an increased overall image noise. The Pearson correlation coefficient between the ISD and TSD measurement and spearman correlation coefficient between the ISD measurement and stent type were 0.83 and 0.48, respectively. Compared with standard reconstruction algorithms, high-definition CT imaging technique with dedicated high-resolution reconstruction algorithm provides more accurate stent characterization and intrastent luminal diameter measurement. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

On an image reconstruction method for ECT

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Sasamoto, Akira; Suzuki, Takayuki; Nishimura, Yoshihiro

2007-04-01

An image by Eddy Current Testing(ECT) is a blurred image to original flaw shape. In order to reconstruct fine flaw image, a new image reconstruction method has been proposed. This method is based on an assumption that a very simple relationship between measured data and source were described by a convolution of response function and flaw shape. This assumption leads to a simple inverse analysis method with deconvolution.In this method, Point Spread Function (PSF) and Line Spread Function(LSF) play a key role in deconvolution processing. This study proposes a simple data processing to determine PSF and LSF from ECT data of machined hole and line flaw. In order to verify its validity, ECT data for SUS316 plate(200x200x10mm) with artificial machined hole and notch flaw had been acquired by differential coil type sensors(produced by ZETEC Inc). Those data were analyzed by the proposed method. The proposed method restored sharp discrete multiple hole image from interfered data by multiple holes. Also the estimated width of line flaw has been much improved compared with original experimental data. Although proposed inverse analysis strategy is simple and easy to implement, its validity to holes and line flaw have been shown by many results that much finer image than original image have been reconstructed.

Super resolution reconstruction of infrared images based on classified dictionary learning

Science.gov (United States)

Liu, Fei; Han, Pingli; Wang, Yi; Li, Xuan; Bai, Lu; Shao, Xiaopeng

2018-05-01

Infrared images always suffer from low-resolution problems resulting from limitations of imaging devices. An economical approach to combat this problem involves reconstructing high-resolution images by reasonable methods without updating devices. Inspired by compressed sensing theory, this study presents and demonstrates a Classified Dictionary Learning method to reconstruct high-resolution infrared images. It classifies features of the samples into several reasonable clusters and trained a dictionary pair for each cluster. The optimal pair of dictionaries is chosen for each image reconstruction and therefore, more satisfactory results is achieved without the increase in computational complexity and time cost. Experiments and results demonstrated that it is a viable method for infrared images reconstruction since it improves image resolution and recovers detailed information of targets.

Image quality assesment using NEMA NU 4/2008 standards in small animal PET scanner

International Nuclear Information System (INIS)

Gontijo, Rodrigo M.G.; Ferreira, Andréa V.; Silva, Juliana B.; Mamede, Marcelo

2017-01-01

In Brazil, there are few micro PET in use and a quality control protocols standardization are needed to harmonize their use in the research field. Thus, the purpose of this study is to characterize the image quality performance of the micro PET scanner (Lab PET 4, GE healthcare Technologies, Waukesha, WI) using the NEMA NU 4/ 2008 standards and specific phantom. The NEMA image-quality (IQ) phantom consists of 3 different regions to analyze distinct characteristics: image noise (%SD), expressed as percentage SD in a uniform region (%SD), recovery coefficient (RC) and Spill-over (SOR) in air and water. The IQ phantom was filled with 18 F-FDG calibrated at the beginning of acquisition, placed in the center of the field-of-view (FOV) and measured with the typical whole body imaging protocol. The images were reconstructed with different reconstruction methods (FBP-2D; MLEM-3D and OSEM-3D); with and without high resolution (HR) when possible. The results were compared. The LabPET 4 system produces appropriate image and with performance according to the literature. The present study is an initial step to verify the NEMA NU 4/2008 use in the Brazilian scenario for further standardization. (author)

Image quality assesment using NEMA NU 4/2008 standards in small animal PET scanner

Energy Technology Data Exchange (ETDEWEB)

Gontijo, Rodrigo M.G.; Ferreira, Andréa V.; Silva, Juliana B.; Mamede, Marcelo, E-mail: rodrigo.gontijo@cdtn.br, E-mail: rodrigogadelhagontijo1@hotmail.com [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

2017-07-01

In Brazil, there are few micro PET in use and a quality control protocols standardization are needed to harmonize their use in the research field. Thus, the purpose of this study is to characterize the image quality performance of the micro PET scanner (Lab PET 4, GE healthcare Technologies, Waukesha, WI) using the NEMA NU 4/ 2008 standards and specific phantom. The NEMA image-quality (IQ) phantom consists of 3 different regions to analyze distinct characteristics: image noise (%SD), expressed as percentage SD in a uniform region (%SD), recovery coefficient (RC) and Spill-over (SOR) in air and water. The IQ phantom was filled with {sup 18}F-FDG calibrated at the beginning of acquisition, placed in the center of the field-of-view (FOV) and measured with the typical whole body imaging protocol. The images were reconstructed with different reconstruction methods (FBP-2D; MLEM-3D and OSEM-3D); with and without high resolution (HR) when possible. The results were compared. The LabPET 4 system produces appropriate image and with performance according to the literature. The present study is an initial step to verify the NEMA NU 4/2008 use in the Brazilian scenario for further standardization. (author)

MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

International Nuclear Information System (INIS)

Chen, G; Pan, X; Stayman, J; Samei, E

2014-01-01

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within the reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical

Pragmatic fully 3D image reconstruction for the MiCES mouse imaging PET scanner

International Nuclear Information System (INIS)

Lee, Kisung; Kinahan, Paul E; Fessler, Jeffrey A; Miyaoka, Robert S; Janes, Marie; Lewellen, Tom K

2004-01-01

We present a pragmatic approach to image reconstruction for data from the micro crystal elements system (MiCES) fully 3D mouse imaging positron emission tomography (PET) scanner under construction at the University of Washington. Our approach is modelled on fully 3D image reconstruction used in clinical PET scanners, which is based on Fourier rebinning (FORE) followed by 2D iterative image reconstruction using ordered-subsets expectation-maximization (OSEM). The use of iterative methods allows modelling of physical effects (e.g., statistical noise, detector blurring, attenuation, etc), while FORE accelerates the reconstruction process by reducing the fully 3D data to a stacked set of independent 2D sinograms. Previous investigations have indicated that non-stationary detector point-spread response effects, which are typically ignored for clinical imaging, significantly impact image quality for the MiCES scanner geometry. To model the effect of non-stationary detector blurring (DB) in the FORE+OSEM(DB) algorithm, we have added a factorized system matrix to the ASPIRE reconstruction library. Initial results indicate that the proposed approach produces an improvement in resolution without an undue increase in noise and without a significant increase in the computational burden. The impact on task performance, however, remains to be evaluated

Prior image constrained image reconstruction in emerging computed tomography applications

Science.gov (United States)

Brunner, Stephen T.

Advances have been made in computed tomography (CT), especially in the past five years, by incorporating prior images into the image reconstruction process. In this dissertation, we investigate prior image constrained image reconstruction in three emerging CT applications: dual-energy CT, multi-energy photon-counting CT, and cone-beam CT in image-guided radiation therapy. First, we investigate the application of Prior Image Constrained Compressed Sensing (PICCS) in dual-energy CT, which has been called "one of the hottest research areas in CT." Phantom and animal studies are conducted using a state-of-the-art 64-slice GE Discovery 750 HD CT scanner to investigate the extent to which PICCS can enable radiation dose reduction in material density and virtual monochromatic imaging. Second, we extend the application of PICCS from dual-energy CT to multi-energy photon-counting CT, which has been called "one of the 12 topics in CT to be critical in the next decade." Numerical simulations are conducted to generate multiple energy bin images for a photon-counting CT acquisition and to investigate the extent to which PICCS can enable radiation dose efficiency improvement. Third, we investigate the performance of a newly proposed prior image constrained scatter correction technique to correct scatter-induced shading artifacts in cone-beam CT, which, when used in image-guided radiation therapy procedures, can assist in patient localization, and potentially, dose verification and adaptive radiation therapy. Phantom studies are conducted using a Varian 2100 EX system with an on-board imager to investigate the extent to which the prior image constrained scatter correction technique can mitigate scatter-induced shading artifacts in cone-beam CT. Results show that these prior image constrained image reconstruction techniques can reduce radiation dose in dual-energy CT by 50% in phantom and animal studies in material density and virtual monochromatic imaging, can lead to radiation

Evaluation of digital breast tomosynthesis reconstruction algorithms using synchrotron radiation in standard geometry

International Nuclear Information System (INIS)

Bliznakova, K.; Kolitsi, Z.; Speller, R. D.; Horrocks, J. A.; Tromba, G.; Pallikarakis, N.

2010-01-01

Purpose: In this article, the image quality of reconstructed volumes by four algorithms for digital tomosynthesis, applied in the case of breast, is investigated using synchrotron radiation. Methods: An angular data set of 21 images of a complex phantom with heterogeneous tissue-mimicking background was obtained using the SYRMEP beamline at ELETTRA Synchrotron Light Laboratory, Trieste, Italy. The irradiated part was reconstructed using the multiple projection algorithm (MPA) and the filtered backprojection with ramp followed by hamming windows (FBR-RH) and filtered backprojection with ramp (FBP-R). Additionally, an algorithm for reducing the noise in reconstructed planes based on noise mask subtraction from the planes of the originally reconstructed volume using MPA (MPA-NM) has been further developed. The reconstruction techniques were evaluated in terms of calculations and comparison of the contrast-to-noise ratio (CNR) and artifact spread function. Results: It was found that the MPA-NM resulted in higher CNR, comparable with the CNR of FBP-RH for high contrast details. Low contrast objects are well visualized and characterized by high CNR using the simple MPA and the MPA-NM. In addition, the image quality of the reconstructed features in terms of CNR and visual appearance as a function of the initial number of projection images and the reconstruction arc was carried out. Slices reconstructed with more input projection images result in less reconstruction artifacts and higher detail CNR, while those reconstructed from projection images acquired in reduced angular range causes pronounced streak artifacts. Conclusions: Of the reconstruction algorithms implemented, the MPA-NM and MPA are a good choice for detecting low contrast objects, while the FBP-RH, FBP-R, and MPA-NM provide high CNR and well outlined edges in case of microcalcifications.

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).

Science.gov (United States)

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

2012-03-01

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 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 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. © Georg Thieme Verlag KG Stuttgart · New York.

3D Reconstruction of NMR Images by LabVIEW

Directory of Open Access Journals (Sweden)

Peter IZAK

2007-01-01

Full Text Available This paper introduces the experiment of 3D reconstruction NMR images via virtual instrumentation - LabVIEW. The main idea is based on marching cubes algorithm and image processing implemented by module of Vision assistant. The two dimensional images shot by the magnetic resonance device provide information about the surface properties of human body. There is implemented algorithm which can be used for 3D reconstruction of magnetic resonance images in biomedical application.

Longitudinal and transverse digital image reconstruction with a tomographic scanner

International Nuclear Information System (INIS)

Pickens, D.R.; Price, R.R.; Erickson, J.J.; Patton, J.A.; Partain, C.L.; Rollo, F.D.

1981-01-01

A Siemens Gammasonics PHO/CON-192 Multiplane Imager is interfaced to a digital computer for the purpose of performing tomographic reconstructions from the data collected during a single scan. Data from the two moving gamma cameras as well as camera position information are sent to the computer by an interface designed in the authors' laboratory. Backprojection reconstruction is implemented by the computer. Longitudinal images in whole-body format as well as smaller formats are reconstructed for up to six planes simultaneously from the list mode data. Transverse reconstructions are demonstrated for 201 T1 myocardial scans. Post-reconstruction deconvolution processing to remove the blur artifact (characteristic of focal plane tomography) is applied to a multiplane phantom. Digital data acquisition of data and reconstruction of images are practical, and can extend the usefulness of the machine when compared with the film output (author)

Assessment of the impact of modeling axial compression on PET image reconstruction.

Science.gov (United States)

Belzunce, Martin A; Reader, Andrew J

2017-10-01

To comprehensively evaluate both the acceleration and image-quality impacts of axial compression and its degree of modeling in fully 3D PET image reconstruction. Despite being used since the very dawn of 3D PET reconstruction, there are still no extensive studies on the impact of axial compression and its degree of modeling during reconstruction on the end-point reconstructed image quality. In this work, an evaluation of the impact of axial compression on the image quality is performed by extensively simulating data with span values from 1 to 121. In addition, two methods for modeling the axial compression in the reconstruction were evaluated. The first method models the axial compression in the system matrix, while the second method uses an unmatched projector/backprojector, where the axial compression is modeled only in the forward projector. The different system matrices were analyzed by computing their singular values and the point response functions for small subregions of the FOV. The two methods were evaluated with simulated and real data for the Biograph mMR scanner. For the simulated data, the axial compression with span values lower than 7 did not show a decrease in the contrast of the reconstructed images. For span 11, the standard sinogram size of the mMR scanner, losses of contrast in the range of 5-10 percentage points were observed when measured for a hot lesion. For higher span values, the spatial resolution was degraded considerably. However, impressively, for all span values of 21 and lower, modeling the axial compression in the system matrix compensated for the spatial resolution degradation and obtained similar contrast values as the span 1 reconstructions. Such approaches have the same processing times as span 1 reconstructions, but they permit significant reduction in storage requirements for the fully 3D sinograms. For higher span values, the system has a large condition number and it is therefore difficult to recover accurately the higher

Enhancing the performance of model-based elastography by incorporating additional a priori information in the modulus image reconstruction process

International Nuclear Information System (INIS)

Doyley, Marvin M; Srinivasan, Seshadri; Dimidenko, Eugene; Soni, Nirmal; Ophir, Jonathan

2006-01-01

Model-based elastography is fraught with problems owing to the ill-posed nature of the inverse elasticity problem. To overcome this limitation, we have recently developed a novel inversion scheme that incorporates a priori information concerning the mechanical properties of the underlying tissue structures, and the variance incurred during displacement estimation in the modulus image reconstruction process. The information was procured by employing standard strain imaging methodology, and introduced in the reconstruction process through the generalized Tikhonov approach. In this paper, we report the results of experiments conducted on gelatin phantoms to evaluate the performance of modulus elastograms computed with the generalized Tikhonov (GTK) estimation criterion relative to those computed by employing the un-weighted least-squares estimation criterion, the weighted least-squares estimation criterion and the standard Tikhonov method (i.e., the generalized Tikhonov method with no modulus prior). The results indicate that modulus elastograms computed with the generalized Tikhonov approach had superior elastographic contrast discrimination and contrast recovery. In addition, image reconstruction was more resilient to structural decorrelation noise when additional constraints were imposed on the reconstruction process through the GTK method

High-order noise analysis for low dose iterative image reconstruction methods: ASIR, IRIS, and MBAI

Science.gov (United States)

Do, Synho; Singh, Sarabjeet; Kalra, Mannudeep K.; Karl, W. Clem; Brady, Thomas J.; Pien, Homer

2011-03-01

Iterative reconstruction techniques (IRTs) has been shown to suppress noise significantly in low dose CT imaging. However, medical doctors hesitate to accept this new technology because visual impression of IRT images are different from full-dose filtered back-projection (FBP) images. Most common noise measurements such as the mean and standard deviation of homogeneous region in the image that do not provide sufficient characterization of noise statistics when probability density function becomes non-Gaussian. In this study, we measure L-moments of intensity values of images acquired at 10% of normal dose and reconstructed by IRT methods of two state-of-art clinical scanners (i.e., GE HDCT and Siemens DSCT flash) by keeping dosage level identical to each other. The high- and low-dose scans (i.e., 10% of high dose) were acquired from each scanner and L-moments of noise patches were calculated for the comparison.

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.

Choice of reconstructed tissue properties affects interpretation of lung EIT images.

Science.gov (United States)

Grychtol, Bartłomiej; Adler, Andy

2014-06-01

Electrical impedance tomography (EIT) estimates an image of change in electrical properties within a body from stimulations and measurements at surface electrodes. There is significant interest in EIT as a tool to monitor and guide ventilation therapy in mechanically ventilated patients. In lung EIT, the EIT inverse problem is commonly linearized and only changes in electrical properties are reconstructed. Early algorithms reconstructed changes in resistivity, while most recent work using the finite element method reconstructs conductivity. Recently, we demonstrated that EIT images of ventilation can be misleading if the electrical contrasts within the thorax are not taken into account during the image reconstruction process. In this paper, we explore the effect of the choice of the reconstructed electrical properties (resistivity or conductivity) on the resulting EIT images. We show in simulation and experimental data that EIT images reconstructed with the same algorithm but with different parametrizations lead to large and clinically significant differences in the resulting images, which persist even after attempts to eliminate the impact of the parameter choice by recovering volume changes from the EIT images. Since there is no consensus among the most popular reconstruction algorithms and devices regarding the parametrization, this finding has implications for potential clinical use of EIT. We propose a program of research to develop reconstruction techniques that account for both the relationship between air volume and electrical properties of the lung and artefacts introduced by the linearization.

Choice of reconstructed tissue properties affects interpretation of lung EIT images

International Nuclear Information System (INIS)

Grychtol, Bartłomiej; Adler, Andy

2014-01-01

Electrical impedance tomography (EIT) estimates an image of change in electrical properties within a body from stimulations and measurements at surface electrodes. There is significant interest in EIT as a tool to monitor and guide ventilation therapy in mechanically ventilated patients. In lung EIT, the EIT inverse problem is commonly linearized and only changes in electrical properties are reconstructed. Early algorithms reconstructed changes in resistivity, while most recent work using the finite element method reconstructs conductivity. Recently, we demonstrated that EIT images of ventilation can be misleading if the electrical contrasts within the thorax are not taken into account during the image reconstruction process. In this paper, we explore the effect of the choice of the reconstructed electrical properties (resistivity or conductivity) on the resulting EIT images. We show in simulation and experimental data that EIT images reconstructed with the same algorithm but with different parametrizations lead to large and clinically significant differences in the resulting images, which persist even after attempts to eliminate the impact of the parameter choice by recovering volume changes from the EIT images. Since there is no consensus among the most popular reconstruction algorithms and devices regarding the parametrization, this finding has implications for potential clinical use of EIT. We propose a program of research to develop reconstruction techniques that account for both the relationship between air volume and electrical properties of the lung and artefacts introduced by the linearization. (paper)

Evaluation of imaging protocol for ECT based on CS image reconstruction algorithm

International Nuclear Information System (INIS)

Zhou Xiaolin; Yun Mingkai; Cao Xuexiang; Liu Shuangquan; Wang Lu; Huang Xianchao; Wei Long

2014-01-01

Single-photon emission computerized tomography and positron emission tomography are essential medical imaging tools, for which the sampling angle number and scan time should be carefully chosen to give a good compromise between image quality and radiopharmaceutical dose. In this study, the image quality of different acquisition protocols was evaluated via varied angle number and count number per angle with Monte Carlo simulation data. It was shown that, when similar imaging counts were used, the factor of acquisition counts was more important than that of the sampling number in emission computerized tomography. To further reduce the activity requirement and the scan duration, an iterative image reconstruction algorithm for limited-view and low-dose tomography based on compressed sensing theory has been developed. The total variation regulation was added to the reconstruction process to improve the signal to noise Ratio and reduce artifacts caused by the limited angle sampling. Maximization of the maximum likelihood of the estimated image and the measured data and minimization of the total variation of the image are alternatively implemented. By using this advanced algorithm, the reconstruction process is able to achieve image quality matching or exceed that of normal scans with only half of the injection radiopharmaceutical dose. (authors)

Preoperative implant selection for unilateral breast reconstruction using 3D imaging with the Microsoft Kinect sensor.

Science.gov (United States)

Pöhlmann, Stefanie T L; Harkness, Elaine; Taylor, Christopher J; Gandhi, Ashu; Astley, Susan M

2017-08-01

This study aimed to investigate whether breast volume measured preoperatively using a Kinect 3D sensor could be used to determine the most appropriate implant size for reconstruction. Ten patients underwent 3D imaging before and after unilateral implant-based reconstruction. Imaging used seven configurations, varying patient pose and Kinect location, which were compared regarding suitability for volume measurement. Four methods of defining the breast boundary for automated volume calculation were compared, and repeatability assessed over five repetitions. The most repeatable breast boundary annotation used an ellipse to track the inframammary fold and a plane describing the chest wall (coefficient of repeatability: 70 ml). The most reproducible imaging position comparing pre- and postoperative volume measurement of the healthy breast was achieved for the sitting patient with elevated arms and Kinect centrally positioned (coefficient of repeatability: 141 ml). Optimal implant volume was calculated by correcting used implant volume by the observed postoperative asymmetry. It was possible to predict implant size using a linear model derived from preoperative volume measurement of the healthy breast (coefficient of determination R 2  = 0.78, standard error of prediction 120 ml). Mastectomy specimen weight and experienced surgeons' choice showed similar predictive ability (both: R 2  = 0.74, standard error: 141/142 ml). A leave one-out validation showed that in 61% of cases, 3D imaging could predict implant volume to within 10%; however for 17% of cases it was >30%. This technology has the potential to facilitate reconstruction surgery planning and implant procurement to maximise symmetry after unilateral reconstruction. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Fan-beam and cone-beam image reconstruction via filtering the backprojection image of differentiated projection data

International Nuclear Information System (INIS)

Zhuang Tingliang; Leng Shuai; Nett, Brian E; Chen Guanghong

2004-01-01

In this paper, a new image reconstruction scheme is presented based on Tuy's cone-beam inversion scheme and its fan-beam counterpart. It is demonstrated that Tuy's inversion scheme may be used to derive a new framework for fan-beam and cone-beam image reconstruction. In this new framework, images are reconstructed via filtering the backprojection image of differentiated projection data. The new framework is mathematically exact and is applicable to a general source trajectory provided the Tuy data sufficiency condition is satisfied. By choosing a piece-wise constant function for one of the components in the factorized weighting function, the filtering kernel is one dimensional, viz. the filtering process is along a straight line. Thus, the derived image reconstruction algorithm is mathematically exact and efficient. In the cone-beam case, the derived reconstruction algorithm is applicable to a large class of source trajectories where the pi-lines or the generalized pi-lines exist. In addition, the new reconstruction scheme survives the super-short scan mode in both the fan-beam and cone-beam cases provided the data are not transversely truncated. Numerical simulations were conducted to validate the new reconstruction scheme for the fan-beam case

Reconstruction of CT images by the Bayes- back projection method

CERN Document Server

Haruyama, M; Takase, M; Tobita, H

2002-01-01

In the course of research on quantitative assay of non-destructive measurement of radioactive waste, the have developed a unique program based on the Bayesian theory for reconstruction of transmission computed tomography (TCT) image. The reconstruction of cross-section images in the CT technology usually employs the Filtered Back Projection method. The new imaging reconstruction program reported here is based on the Bayesian Back Projection method, and it has a function of iterative improvement images by every step of measurement. Namely, this method has the capability of prompt display of a cross-section image corresponding to each angled projection data from every measurement. Hence, it is possible to observe an improved cross-section view by reflecting each projection data in almost real time. From the basic theory of Baysian Back Projection method, it can be not only applied to CT types of 1st, 2nd, and 3rd generation. This reported deals with a reconstruction program of cross-section images in the CT of ...

Reconstruction of Optical Thickness from Hoffman Modulation Contrast Images

DEFF Research Database (Denmark)

Olsen, Niels Holm; Sporring, Jon; Nielsen, Mads

2003-01-01

Hoffman microscopy imaging systems are part of numerous fertility clinics world-wide. We discuss the physics of the Hoffman imaging system from optical thickness to image intensity, implement a simple, yet fast, reconstruction algorithm using Fast Fourier Transformation and discuss the usability...... of the method on a number of cells from a human embryo. Novelty is identifying the non-linearity of a typical Hoffman imaging system, and the application of Fourier Transformation to reconstruct the optical thickness....

PROMISE: parallel-imaging and compressed-sensing reconstruction of multicontrast imaging using SharablE information.

Science.gov (United States)

Gong, Enhao; Huang, Feng; Ying, Kui; Wu, Wenchuan; Wang, Shi; Yuan, Chun

2015-02-01

A typical clinical MR examination includes multiple scans to acquire images with different contrasts for complementary diagnostic information. The multicontrast scheme requires long scanning time. The combination of partially parallel imaging and compressed sensing (CS-PPI) has been used to reconstruct accelerated scans. However, there are several unsolved problems in existing methods. The target of this work is to improve existing CS-PPI methods for multicontrast imaging, especially for two-dimensional imaging. If the same field of view is scanned in multicontrast imaging, there is significant amount of sharable information. It is proposed in this study to use manifold sharable information among multicontrast images to enhance CS-PPI in a sequential way. Coil sensitivity information and structure based adaptive regularization, which were extracted from previously reconstructed images, were applied to enhance the following reconstructions. The proposed method is called Parallel-imaging and compressed-sensing Reconstruction Of Multicontrast Imaging using SharablE information (PROMISE). Using L1 -SPIRiT as a CS-PPI example, results on multicontrast brain and carotid scans demonstrated that lower error level and better detail preservation can be achieved by exploiting manifold sharable information. Besides, the privilege of PROMISE still exists while there is interscan motion. Using the sharable information among multicontrast images can enhance CS-PPI with tolerance to motions. © 2014 Wiley Periodicals, Inc.

Common-mask guided image reconstruction (c-MGIR) for enhanced 4D cone-beam computed tomography

International Nuclear Information System (INIS)

Park, Justin C; Li, Jonathan G; Liu, Chihray; Lu, Bo; Zhang, Hao; Chen, Yunmei; Fan, Qiyong

2015-01-01

Compared to 3D cone beam computed tomography (3D CBCT), the image quality of commercially available four-dimensional (4D) CBCT is severely impaired due to the insufficient amount of projection data available for each phase. Since the traditional Feldkamp-Davis-Kress (FDK)-based algorithm is infeasible for reconstructing high quality 4D CBCT images with limited projections, investigators had developed several compress-sensing (CS) based algorithms to improve image quality. The aim of this study is to develop a novel algorithm which can provide better image quality than the FDK and other CS based algorithms with limited projections. We named this algorithm ‘the common mask guided image reconstruction’ (c-MGIR).In c-MGIR, the unknown CBCT volume is mathematically modeled as a combination of phase-specific motion vectors and phase-independent static vectors. The common-mask matrix, which is the key concept behind the c-MGIR algorithm, separates the common static part across all phase images from the possible moving part in each phase image. The moving part and the static part of the volumes were then alternatively updated by solving two sub-minimization problems iteratively. As the novel mathematical transformation allows the static volume and moving volumes to be updated (during each iteration) with global projections and ‘well’ solved static volume respectively, the algorithm was able to reduce the noise and under-sampling artifact (an issue faced by other algorithms) to the maximum extent. To evaluate the performance of our proposed c-MGIR, we utilized imaging data from both numerical phantoms and a lung cancer patient. The qualities of the images reconstructed with c-MGIR were compared with (1) standard FDK algorithm, (2) conventional total variation (CTV) based algorithm, (3) prior image constrained compressed sensing (PICCS) algorithm, and (4) motion-map constrained image reconstruction (MCIR) algorithm, respectively. To improve the efficiency of the

Use of model-based iterative reconstruction (MBIR) in reduced-dose CT for routine follow-up of patients with malignant lymphoma: dose savings, image quality and phantom study

International Nuclear Information System (INIS)

Herin, Edouard; Chiaradia, Melanie; Cavet, Madeleine; Deux, Jean-Francois; Rahmouni, Alain; Gardavaud, Francois; Beaussart, Pauline; Richard, Philippe; Haioun, Corinne; Itti, Emmanuel; Luciani, Alain

2015-01-01

To evaluate both in vivo and in phantom studies, dose reduction, and image quality of body CT reconstructed with model-based iterative reconstruction (MBIR), performed during patient follow-ups for lymphoma. This study included 40 patients (mean age 49 years) with lymphoma. All underwent reduced-dose CT during follow-up, reconstructed using MBIR or 50 % advanced statistical iterative reconstruction (ASIR). All had previously undergone a standard dose CT with filtered back projection (FBP) reconstruction. The volume CT dose index (CTDIvol), the density measures in liver, spleen, fat, air, and muscle, and the image quality (noise and signal to noise ratio, SNR) (ANOVA) observed using standard or reduced-dose CT were compared both in patients and a phantom study (Catphan 600) (Kruskal Wallis). The CTDIvol was decreased on reduced-dose body CT (4.06 mGy vs. 15.64 mGy p < 0.0001). SNR was higher in reduced-dose CT reconstructed with MBIR than in 50 % ASIR or than standard dose CT with FBP (patients, p ≤ 0.01; phantoms, p = 0.003). Low contrast detectability and spatial resolution in phantoms were not altered on MBIR-reconstructed CT (p ≥ 0.11). Reduced-dose CT with MBIR reconstruction can decrease radiation dose delivered to patients with lymphoma, while keeping an image quality similar to that obtained on standard-dose CT. (orig.)

Use of model-based iterative reconstruction (MBIR) in reduced-dose CT for routine follow-up of patients with malignant lymphoma: dose savings, image quality and phantom study

Energy Technology Data Exchange (ETDEWEB)

Herin, Edouard; Chiaradia, Melanie; Cavet, Madeleine; Deux, Jean-Francois; Rahmouni, Alain [AP-HP, Hopitaux Universitaires Henri Mondor, Imagerie Medicale, Creteil (France); Universite Paris Est Creteil, Faculte de Medecine, Creteil (France); Gardavaud, Francois; Beaussart, Pauline [AP-HP, Hopitaux Universitaires Henri Mondor, Imagerie Medicale, Creteil (France); Richard, Philippe [GE Healthcare France, Buc (France); Haioun, Corinne [Universite Paris Est Creteil, Faculte de Medecine, Creteil (France); AP-HP, Hopitaux Universitaires Henri Mondor, Hemopathies Lymphoides, Creteil (France); Itti, Emmanuel [Universite Paris Est Creteil, Faculte de Medecine, Creteil (France); AP-HP, Hopitaux Universitaires Henri Mondor, Medecine Nucleaire, Creteil (France); Luciani, Alain [AP-HP, Hopitaux Universitaires Henri Mondor, Imagerie Medicale, Creteil (France); Universite Paris Est Creteil, Faculte de Medecine, Creteil (France); INSERM Unite U 955, Creteil (France); AP-HP, Groupe Henri Mondor Albert Chenevier, Imagerie Medicale, CHU Henri Mondor, Creteil Cedex (France)

2015-08-15

To evaluate both in vivo and in phantom studies, dose reduction, and image quality of body CT reconstructed with model-based iterative reconstruction (MBIR), performed during patient follow-ups for lymphoma. This study included 40 patients (mean age 49 years) with lymphoma. All underwent reduced-dose CT during follow-up, reconstructed using MBIR or 50 % advanced statistical iterative reconstruction (ASIR). All had previously undergone a standard dose CT with filtered back projection (FBP) reconstruction. The volume CT dose index (CTDIvol), the density measures in liver, spleen, fat, air, and muscle, and the image quality (noise and signal to noise ratio, SNR) (ANOVA) observed using standard or reduced-dose CT were compared both in patients and a phantom study (Catphan 600) (Kruskal Wallis). The CTDIvol was decreased on reduced-dose body CT (4.06 mGy vs. 15.64 mGy p < 0.0001). SNR was higher in reduced-dose CT reconstructed with MBIR than in 50 % ASIR or than standard dose CT with FBP (patients, p ≤ 0.01; phantoms, p = 0.003). Low contrast detectability and spatial resolution in phantoms were not altered on MBIR-reconstructed CT (p ≥ 0.11). Reduced-dose CT with MBIR reconstruction can decrease radiation dose delivered to patients with lymphoma, while keeping an image quality similar to that obtained on standard-dose CT. (orig.)

Optical image reconstruction using DC data: simulations and experiments

International Nuclear Information System (INIS)

Huabei Jiang; Paulsen, K.D.; Oesterberg, U.L.

1996-01-01

In this paper, we explore optical image formation using a diffusion approximation of light propagation in tissue which is modelled with a finite-element method for optically heterogeneous media. We demonstrate successful image reconstruction based on absolute experimental DC data obtained with a continuous wave 633 nm He-Ne laser system and a 751 nm diode laser system in laboratory phantoms having two optically distinct regions. The experimental systems used exploit a tomographic type of data collection scheme that provides information from which a spatially variable optical property map is deduced. Reconstruction of scattering coefficient only and simultaneous reconstruction of both scattering and absorption profiles in tissue-like phantoms are obtained from measured and simulated data. Images with different contrast levels between the heterogeneity and the background are also reported and the results show that although it is possible to obtain qualitative visual information on the location and size of a heterogeneity, it may not be possible to quantitatively resolve contrast levels or optical properties using reconstructions from DC data only. Sensitivity of image reconstruction to noise in the measurement data is investigated through simulations. The application of boundary constraints has also been addressed. (author)

Electro-optical system for the high speed reconstruction of computed tomography images

International Nuclear Information System (INIS)

Tresp, V.

1989-01-01

An electro-optical system for the high-speed reconstruction of computed tomography (CT) images has been built and studied. The system is capable of reconstructing high-contrast and high-resolution images at video rate (30 images per second), which is more than two orders of magnitude faster than the reconstruction rate achieved by special purpose digital computers used in commercial CT systems. The filtered back-projection algorithm which was implemented in the reconstruction system requires the filtering of all projections with a prescribed filter function. A space-integrating acousto-optical convolver, a surface acoustic wave filter and a digital finite-impulse response filter were used for this purpose and their performances were compared. The second part of the reconstruction, the back projection of the filtered projections, is computationally very expensive. An optical back projector has been built which maps the filtered projections onto the two-dimensional image space using an anamorphic lens system and a prism image rotator. The reconstructed image is viewed by a video camera, routed through a real-time image-enhancement system, and displayed on a TV monitor. The system reconstructs parallel-beam projection data, and in a modified version, is also capable of reconstructing fan-beam projection data. This extension is important since the latter are the kind of projection data actually acquired in high-speed X-ray CT scanners. The reconstruction system was tested by reconstructing precomputed projection data of phantom images. These were stored in a special purpose projection memory and transmitted to the reconstruction system as an electronic signal. In this way, a projection measurement system that acquires projections sequentially was simulated

Does thorax EIT image analysis depend on the image reconstruction method?

Science.gov (United States)

Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut

2013-04-01

Different methods were proposed to analyze the resulting images of electrical impedance tomography (EIT) measurements during ventilation. The aim of our study was to examine if the analysis methods based on back-projection deliver the same results when applied on images based on other reconstruction algorithms. Seven mechanically ventilated patients with ARDS were examined by EIT. The thorax contours were determined from the routine CT images. EIT raw data was reconstructed offline with (1) filtered back-projection with circular forward model (BPC); (2) GREIT reconstruction method with circular forward model (GREITC) and (3) GREIT with individual thorax geometry (GREITT). Three parameters were calculated on the resulting images: linearity, global ventilation distribution and regional ventilation distribution. The results of linearity test are 5.03±2.45, 4.66±2.25 and 5.32±2.30 for BPC, GREITC and GREITT, respectively (median ±interquartile range). The differences among the three methods are not significant (p = 0.93, Kruskal-Wallis test). The proportions of ventilation in the right lung are 0.58±0.17, 0.59±0.20 and 0.59±0.25 for BPC, GREITC and GREITT, respectively (p = 0.98). The differences of the GI index based on different reconstruction methods (0.53±0.16, 0.51±0.25 and 0.54±0.16 for BPC, GREITC and GREITT, respectively) are also not significant (p = 0.93). We conclude that the parameters developed for images generated with GREITT are comparable with filtered back-projection and GREITC.

GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging.

Science.gov (United States)

Wen, Tiexiang; Li, Ling; Zhu, Qingsong; Qin, Wenjian; Gu, Jia; Yang, Feng; Xie, Yaoqin

2017-07-01

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor-based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of [Formula: see text] and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.

Usefulness of three dimensional reconstructive images for thoracic trauma induced fractures

Energy Technology Data Exchange (ETDEWEB)

Koh, Kyung Hun; Kim, Dong Hun; Kim, Young Sook; Byun, Joo Nam [Chosun University Hospital, Gwangju (Korea, Republic of)

2006-09-15

We wanted to evaluate the usefulness of three-dimensional reconstructive images using multidetector computed tomography (MDCT) for thoracic traumatic patients visiting emergency room. 76 patients with fractures of the 105 patients who visited our emergency room with complaints of thoracic trauma were analyzed retrospectively. All the patients had thoracic MDCT performed and the three-dimensional reconstructive images were taken. The fractures were confirmed by axial CT, the clinical information, whole body bone scanning and the multiplanar reformation images. Plain x-ray images were analyzed by the fractured sites in a blind comparison of two radiologists' readings, and then that finding was compared with the axial CT scans and the three-dimensional reconstructive images. The fracture sites were rib (n 68), sternum (n = 14), clavicle (n = 6), scapula (n = 3), spine (n = 5) and combined fractures (n = 14). Plain x-ray and axial CT scans had a correspondency of 0.555 for the rib fractures. Axial CT scans and the three-dimensional reconstructive images had a correspondency of .952. For sternal fractures, those values were 0.692 and 0.928, respectively. The axial CT scans and three-dimensional reconstructive images showed sensitivities of 94% and 91% for rib and other fractures, respectively, and 93% and 100% for sternal fracture, respectively. Three-dimensional reconstructive image had an especially high sensitivity for the diagnosis of sternal fracture. While evaluating thoracic trauma at the emergency room, the three-dimensional reconstructive image was useful to easily diagnose the extent of fracture and it was very sensitive for detecting sternal fracture.

Usefulness of three dimensional reconstructive images for thoracic trauma induced fractures

International Nuclear Information System (INIS)

Koh, Kyung Hun; Kim, Dong Hun; Kim, Young Sook; Byun, Joo Nam

2006-01-01

We wanted to evaluate the usefulness of three-dimensional reconstructive images using multidetector computed tomography (MDCT) for thoracic traumatic patients visiting emergency room. 76 patients with fractures of the 105 patients who visited our emergency room with complaints of thoracic trauma were analyzed retrospectively. All the patients had thoracic MDCT performed and the three-dimensional reconstructive images were taken. The fractures were confirmed by axial CT, the clinical information, whole body bone scanning and the multiplanar reformation images. Plain x-ray images were analyzed by the fractured sites in a blind comparison of two radiologists' readings, and then that finding was compared with the axial CT scans and the three-dimensional reconstructive images. The fracture sites were rib (n 68), sternum (n = 14), clavicle (n = 6), scapula (n = 3), spine (n = 5) and combined fractures (n = 14). Plain x-ray and axial CT scans had a correspondency of 0.555 for the rib fractures. Axial CT scans and the three-dimensional reconstructive images had a correspondency of .952. For sternal fractures, those values were 0.692 and 0.928, respectively. The axial CT scans and three-dimensional reconstructive images showed sensitivities of 94% and 91% for rib and other fractures, respectively, and 93% and 100% for sternal fracture, respectively. Three-dimensional reconstructive image had an especially high sensitivity for the diagnosis of sternal fracture. While evaluating thoracic trauma at the emergency room, the three-dimensional reconstructive image was useful to easily diagnose the extent of fracture and it was very sensitive for detecting sternal fracture

Parallel Algorithm for Reconstruction of TAC Images

International Nuclear Information System (INIS)

Vidal Gimeno, V.

2012-01-01

The algebraic reconstruction methods are based on solving a system of linear equations. In a previous study, was used and showed as the PETSc library, was and is a scientific computing tool, which facilitates and enables the optimal use of a computer system in the image reconstruction process.

Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET

International Nuclear Information System (INIS)

Ahn, Sangtae; Asma, Evren; Cheng, Lishui; Manjeshwar, Ravindra M; Ross, Steven G; Miao, Jun; Jin, Xiao; Wollenweber, Scott D

2015-01-01

Ordered subset expectation maximization (OSEM) is the most widely used algorithm for clinical PET image reconstruction. OSEM is usually stopped early and post-filtered to control image noise and does not necessarily achieve optimal quantitation accuracy. As an alternative to OSEM, we have recently implemented a penalized likelihood (PL) image reconstruction algorithm for clinical PET using the relative difference penalty with the aim of improving quantitation accuracy without compromising visual image quality. Preliminary clinical studies have demonstrated visual image quality including lesion conspicuity in images reconstructed by the PL algorithm is better than or at least as good as that in OSEM images. In this paper we evaluate lesion quantitation accuracy of the PL algorithm with the relative difference penalty compared to OSEM by using various data sets including phantom data acquired with an anthropomorphic torso phantom, an extended oval phantom and the NEMA image quality phantom; clinical data; and hybrid clinical data generated by adding simulated lesion data to clinical data. We focus on mean standardized uptake values and compare them for PL and OSEM using both time-of-flight (TOF) and non-TOF data. The results demonstrate improvements of PL in lesion quantitation accuracy compared to OSEM with a particular improvement in cold background regions such as lungs. (paper)

A tensor-based dictionary learning approach to tomographic image reconstruction

DEFF Research Database (Denmark)

Soltani, Sara; Kilmer, Misha E.; Hansen, Per Christian

2016-01-01

We consider tomographic reconstruction using priors in the form of a dictionary learned from training images. The reconstruction has two stages: first we construct a tensor dictionary prior from our training data, and then we pose the reconstruction problem in terms of recovering the expansion...... coefficients in that dictionary. Our approach differs from past approaches in that (a) we use a third-order tensor representation for our images and (b) we recast the reconstruction problem using the tensor formulation. The dictionary learning problem is presented as a non-negative tensor factorization problem...... with sparsity constraints. The reconstruction problem is formulated in a convex optimization framework by looking for a solution with a sparse representation in the tensor dictionary. Numerical results show that our tensor formulation leads to very sparse representations of both the training images...

Complications of anterior cruciate ligament reconstruction: MR imaging

Energy Technology Data Exchange (ETDEWEB)

Papakonstantinou, Olympia; Chung, Christine B.; Chanchairujira, Kullanuch; Resnick, Donald L. [Department of Radiology, Veterans Affairs Medical Center, University of California, 3350 La Jolla Village Dr., San Diego, CA 92161 (United States)

2003-05-01

Arthroscopic reconstruction of the anterior cruciate ligament (ACL) using autografts or allografts is being performed with increasing frequency, particularly in young athletes. Although the procedure is generally well tolerated, with good success rates, early and late complications have been documented. As clinical manifestations of graft complications are often non-specific and plain radiographs cannot directly visualize the graft and the adjacent soft tissues, MR imaging has a definite role in the diagnosis of complications after ACL reconstruction and may direct subsequent therapeutic management. Our purpose is to review the normal MR imaging of the ACL graft and present the MR imaging findings of a wide spectrum of complications after ACL reconstruction, such as graft impingement, graft rupture, cystic degeneration of the graft, postoperative infection of the knee, diffuse and localized (i.e., cyclops lesion) arthrofibrosis, and associated donor site abnormalities. Awareness of the MR imaging findings of complications as well as the normal appearances of the normal ACL graft is essential for correct interpretation. (orig.)

Complications of anterior cruciate ligament reconstruction: MR imaging

International Nuclear Information System (INIS)

Papakonstantinou, Olympia; Chung, Christine B.; Chanchairujira, Kullanuch; Resnick, Donald L.

2003-01-01

Arthroscopic reconstruction of the anterior cruciate ligament (ACL) using autografts or allografts is being performed with increasing frequency, particularly in young athletes. Although the procedure is generally well tolerated, with good success rates, early and late complications have been documented. As clinical manifestations of graft complications are often non-specific and plain radiographs cannot directly visualize the graft and the adjacent soft tissues, MR imaging has a definite role in the diagnosis of complications after ACL reconstruction and may direct subsequent therapeutic management. Our purpose is to review the normal MR imaging of the ACL graft and present the MR imaging findings of a wide spectrum of complications after ACL reconstruction, such as graft impingement, graft rupture, cystic degeneration of the graft, postoperative infection of the knee, diffuse and localized (i.e., cyclops lesion) arthrofibrosis, and associated donor site abnormalities. Awareness of the MR imaging findings of complications as well as the normal appearances of the normal ACL graft is essential for correct interpretation. (orig.)

CT image reconstruction system based on hardware implementation

International Nuclear Information System (INIS)

Silva, Hamilton P. da; Evseev, Ivan; Schelin, Hugo R.; Paschuk, Sergei A.; Milhoretto, Edney; Setti, Joao A.P.; Zibetti, Marcelo; Hormaza, Joel M.; Lopes, Ricardo T.

2009-01-01

Full text: The timing factor is very important for medical imaging systems, which can nowadays be synchronized by vital human signals, like heartbeats or breath. The use of hardware implemented devices in such a system has advantages considering the high speed of information treatment combined with arbitrary low cost on the market. This article refers to a hardware system which is based on electronic programmable logic called FPGA, model Cyclone II from ALTERA Corporation. The hardware was implemented on the UP3 ALTERA Kit. A partially connected neural network with unitary weights was programmed. The system was tested with 60 topographic projections, 100 points in each, of the Shepp and Logan phantom created by MATLAB. The main restriction was found to be the memory size available on the device: the dynamic range of reconstructed image was limited to 0 65535. Also, the normalization factor must be observed in order to do not saturate the image during the reconstruction and filtering process. The test shows a principal possibility to build CT image reconstruction systems for any reasonable amount of input data by arranging the parallel work of the hardware units like we have tested. However, further studies are necessary for better understanding of the error propagation from topographic projections to reconstructed image within the implemented method. (author)

Real-time cardiac magnetic resonance cine imaging with sparse sampling and iterative reconstruction for left-ventricular measures: Comparison with gold-standard segmented steady-state free precession.

Science.gov (United States)

Camargo, Gabriel C; Erthal, Fernanda; Sabioni, Leticia; Penna, Filipe; Strecker, Ralph; Schmidt, Michaela; Zenge, Michael O; Lima, Ronaldo de S L; Gottlieb, Ilan

2017-05-01

Segmented cine imaging with a steady-state free-precession sequence (Cine-SSFP) is currently the gold standard technique for measuring ventricular volumes and mass, but due to multi breath-hold (BH) requirements, it is prone to misalignment of consecutive slices, time consuming and dependent on respiratory capacity. Real-time cine avoids those limitations, but poor spatial and temporal resolution of conventional sequences has prevented its routine application. We sought to examine the accuracy and feasibility of a newly developed real-time sequence with aggressive under-sampling of k-space using sparse sampling and iterative reconstruction (Cine-RT). Stacks of short-axis cines were acquired covering both ventricles in a 1.5T system using gold standard Cine-SSFP and Cine-RT. Acquisition parameters for Cine-SSFP were: acquisition matrix of 224×196, temporal resolution of 39ms, retrospective gating, with an average of 8 heartbeats per slice and 1-2 slices/BH. For Cine-RT: acquisition matrix of 224×196, sparse sampling net acceleration factor of 11.3, temporal resolution of 41ms, prospective gating, real-time acquisition of 1 heart-beat/slice and all slices in one BH. LV contours were drawn at end diastole and systole to derive LV volumes and mass. Forty-one consecutive patients (15 male; 41±17years) in sinus rhythm were successfully included. All images from Cine-SSFP and Cine-RT were considered to have excellent quality. Cine-RT-derived LV volumes and mass were slightly underestimated but strongly correlated with gold standard Cine-SSFP. Inter- and intra-observer analysis presented similar results between both sequences. Cine-RT featuring sparse sampling and iterative reconstruction can achieve spatial and temporal resolution equivalent to Cine-SSFP, providing excellent image quality, with similar precision measurements and highly correlated and only slightly underestimated volume and mass values. Copyright © 2017 Elsevier Inc. All rights reserved.

Bayesian PET image reconstruction incorporating anato-functional joint entropy

International Nuclear Information System (INIS)

Tang Jing; Rahmim, Arman

2009-01-01

We developed a maximum a posterior (MAP) reconstruction method for positron emission tomography (PET) image reconstruction incorporating magnetic resonance (MR) image information, with the joint entropy between the PET and MR image features serving as the regularization constraint. A non-parametric method was used to estimate the joint probability density of the PET and MR images. Using realistically simulated PET and MR human brain phantoms, the quantitative performance of the proposed algorithm was investigated. Incorporation of the anatomic information via this technique, after parameter optimization, was seen to dramatically improve the noise versus bias tradeoff in every region of interest, compared to the result from using conventional MAP reconstruction. In particular, hot lesions in the FDG PET image, which had no anatomical correspondence in the MR image, also had improved contrast versus noise tradeoff. Corrections were made to figures 3, 4 and 6, and to the second paragraph of section 3.1 on 13 November 2009. The corrected electronic version is identical to the print version.

Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

International Nuclear Information System (INIS)

Wang, Yan; Zhou, Jiliu; Zhang, Pei; An, Le; Ma, Guangkai; Kang, Jiayin; Shi, Feng; Shen, Dinggang; Wu, Xi; Lalush, David S; Lin, Weili

2016-01-01

Positron emission tomography (PET) has been widely used in clinical diagnosis for diseases and disorders. To obtain high-quality PET images requires a standard-dose radionuclide (tracer) injection into the human body, which inevitably increases risk of radiation exposure. One possible solution to this problem is to predict the standard-dose PET image from its low-dose counterpart and its corresponding multimodal magnetic resonance (MR) images. Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, we propose a mapping-based SR (m-SR) framework for standard-dose PET image prediction. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients, estimated from the multimodal MR images and low-dose PET image, can be applied directly to the prediction of standard-dose PET image. As the mapping between multimodal MR images (or low-dose PET image) and standard-dose PET images can be particularly complex, one step of mapping is often insufficient. To this end, an incremental refinement framework is therefore proposed. Specifically, the predicted standard-dose PET image is further mapped to the target standard-dose PET image, and then the SR is performed again to predict a new standard-dose PET image. This procedure can be repeated for prediction refinement of the iterations. Also, a patch selection based dictionary construction method is further used to speed up the prediction process. The proposed method is validated on a human brain dataset. The experimental results show that our method can outperform benchmark methods in both qualitative and quantitative measures. (paper)

The SRT reconstruction algorithm for semiquantification in PET imaging

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Kastis, George A., E-mail: gkastis@academyofathens.gr [Research Center of Mathematics, Academy of Athens, Athens 11527 (Greece); Gaitanis, Anastasios [Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens 11527 (Greece); Samartzis, Alexandros P. [Nuclear Medicine Department, Evangelismos General Hospital, Athens 10676 (Greece); Fokas, Athanasios S. [Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB30WA, United Kingdom and Research Center of Mathematics, Academy of Athens, Athens 11527 (Greece)

2015-10-15

Purpose: The spline reconstruction technique (SRT) is a new, fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The mathematical details of this algorithm and comparisons with filtered backprojection were presented earlier in the literature. In this study, the authors present a comparison between SRT and the ordered-subsets expectation–maximization (OSEM) algorithm for determining contrast and semiquantitative indices of {sup 18}F-FDG uptake. Methods: The authors implemented SRT in the software for tomographic image reconstruction (STIR) open-source platform and evaluated this technique using simulated and real sinograms obtained from the GE Discovery ST positron emission tomography/computer tomography scanner. All simulations and reconstructions were performed in STIR. For OSEM, the authors used the clinical protocol of their scanner, namely, 21 subsets and two iterations. The authors also examined images at one, four, six, and ten iterations. For the simulation studies, the authors analyzed an image-quality phantom with cold and hot lesions. Two different versions of the phantom were employed at two different hot-sphere lesion-to-background ratios (LBRs), namely, 2:1 and 4:1. For each noiseless sinogram, 20 Poisson realizations were created at five different noise levels. In addition to making visual comparisons of the reconstructed images, the authors determined contrast and bias as a function of the background image roughness (IR). For the real-data studies, sinograms of an image-quality phantom simulating the human torso were employed. The authors determined contrast and LBR as a function of the background IR. Finally, the authors present plots of contrast as a function of IR after smoothing each reconstructed image with Gaussian filters of six different sizes. Statistical significance was determined by employing the Wilcoxon rank-sum test. Results: In both simulated and real studies, SRT

The SRT reconstruction algorithm for semiquantification in PET imaging

International Nuclear Information System (INIS)

Kastis, George A.; Gaitanis, Anastasios; Samartzis, Alexandros P.; Fokas, Athanasios S.

2015-01-01

Purpose: The spline reconstruction technique (SRT) is a new, fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The mathematical details of this algorithm and comparisons with filtered backprojection were presented earlier in the literature. In this study, the authors present a comparison between SRT and the ordered-subsets expectation–maximization (OSEM) algorithm for determining contrast and semiquantitative indices of 18 F-FDG uptake. Methods: The authors implemented SRT in the software for tomographic image reconstruction (STIR) open-source platform and evaluated this technique using simulated and real sinograms obtained from the GE Discovery ST positron emission tomography/computer tomography scanner. All simulations and reconstructions were performed in STIR. For OSEM, the authors used the clinical protocol of their scanner, namely, 21 subsets and two iterations. The authors also examined images at one, four, six, and ten iterations. For the simulation studies, the authors analyzed an image-quality phantom with cold and hot lesions. Two different versions of the phantom were employed at two different hot-sphere lesion-to-background ratios (LBRs), namely, 2:1 and 4:1. For each noiseless sinogram, 20 Poisson realizations were created at five different noise levels. In addition to making visual comparisons of the reconstructed images, the authors determined contrast and bias as a function of the background image roughness (IR). For the real-data studies, sinograms of an image-quality phantom simulating the human torso were employed. The authors determined contrast and LBR as a function of the background IR. Finally, the authors present plots of contrast as a function of IR after smoothing each reconstructed image with Gaussian filters of six different sizes. Statistical significance was determined by employing the Wilcoxon rank-sum test. Results: In both simulated and real studies, SRT

Robust framework for PET image reconstruction incorporating system and measurement uncertainties.

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Huafeng Liu

Full Text Available In Positron Emission Tomography (PET, an optimal estimate of the radioactivity concentration is obtained from the measured emission data under certain criteria. So far, all the well-known statistical reconstruction algorithms require exactly known system probability matrix a priori, and the quality of such system model largely determines the quality of the reconstructed images. In this paper, we propose an algorithm for PET image reconstruction for the real world case where the PET system model is subject to uncertainties. The method counts PET reconstruction as a regularization problem and the image estimation is achieved by means of an uncertainty weighted least squares framework. The performance of our work is evaluated with the Shepp-Logan simulated and real phantom data, which demonstrates significant improvements in image quality over the least squares reconstruction efforts.

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

Demosaicing and Superresolution for Color Filter Array via Residual Image Reconstruction and Sparse Representation

OpenAIRE

Sun, Guangling

2012-01-01

A framework of demosaicing and superresolution for color filter array (CFA) via residual image reconstruction and sparse representation is presented.Given the intermediate image produced by certain demosaicing and interpolation technique, a residual image between the final reconstruction image and the intermediate image is reconstructed using sparse representation.The final reconstruction image has richer edges and details than that of the intermediate image. Specifically, a generic dictionar...

Propagation stability of self-reconstructing Bessel beams enables contrast-enhanced imaging in thick media.

Science.gov (United States)

Fahrbach, Florian O; Rohrbach, Alexander

2012-01-17

Laser beams that can self-reconstruct their initial beam profile even in the presence of massive phase perturbations are able to propagate deeper into inhomogeneous media. This ability has crucial advantages for light sheet-based microscopy in thick media, such as cell clusters, embryos, skin or brain tissue or plants, as well as scattering synthetic materials. A ring system around the central intensity maximum of a Bessel beam enables its self-reconstruction, but at the same time illuminates out-of-focus regions and deteriorates image contrast. Here we present a detection method that minimizes the negative effect of the ring system. The beam's propagation stability along one straight line enables the use of a confocal line principle, resulting in a significant increase in image contrast. The axial resolution could be improved by nearly 100% relative to the standard light-sheet techniques using scanned Gaussian beams, while demonstrating self-reconstruction also for high propagation depths.

Molecular Imaging : Computer Reconstruction and Practice - Proceedings of the NATO Advanced Study Institute on Molecular Imaging from Physical Principles to Computer Reconstruction and Practice

CERN Document Server

Lemoigne, Yves

2008-01-01

This volume collects the lectures presented at the ninth ESI School held at Archamps (FR) in November 2006 and is dedicated to nuclear physics applications in molecular imaging. The lectures focus on the multiple facets of image reconstruction processing and management and illustrate the role of digital imaging in clinical practice. Medical computing and image reconstruction are introduced by analysing the underlying physics principles and their implementation, relevant quality aspects, clinical performance and recent advancements in the field. Several stages of the imaging process are specifically addressed, e.g. optimisation of data acquisition and storage, distributed computing, physiology and detector modelling, computer algorithms for image reconstruction and measurement in tomography applications, for both clinical and biomedical research applications. All topics are presented with didactical language and style, making this book an appropriate reference for students and professionals seeking a comprehen...

Image-reconstruction algorithms for positron-emission tomography systems

International Nuclear Information System (INIS)

Cheng, S.N.C.

1982-01-01

The positional uncertainty in the time-of-flight measurement of a positron-emission tomography system is modelled as a Gaussian distributed random variable and the image is assumed to be piecewise constant on a rectilinear lattice. A reconstruction algorithm using maximum-likelihood estimation is derived for the situation in which time-of-flight data are sorted as the most-likely-position array. The algorithm is formulated as a linear system described by a nonseparable, block-banded, Toeplitz matrix, and a sine-transform technique is used to implement this algorithm efficiently. The reconstruction algorithms for both the most-likely-position array and the confidence-weighted array are described by similar equations, hence similar linear systems can be used to described the reconstruction algorithm for a discrete, confidence-weighted array, when the matrix and the entries in the data array are properly identified. It is found that the mean square-error depends on the ratio of the full width at half the maximum of time-of-flight measurement over the size of a pixel. When other parameters are fixed, the larger the pixel size, the smaller is the mean square-error. In the study of resolution, parameters that affect the impulse response of time-of-flight reconstruction algorithms are identified. It is found that the larger the pixel size, the larger is the standard deviation of the impulse response. This shows that small mean square-error and fine resolution are two contradictory requirements

Accelerated Computing in Magnetic Resonance Imaging: Real-Time Imaging Using Nonlinear Inverse Reconstruction

Directory of Open Access Journals (Sweden)

Sebastian Schaetz

2017-01-01

Full Text Available Purpose. To develop generic optimization strategies for image reconstruction using graphical processing units (GPUs in magnetic resonance imaging (MRI and to exemplarily report on our experience with a highly accelerated implementation of the nonlinear inversion (NLINV algorithm for dynamic MRI with high frame rates. Methods. The NLINV algorithm is optimized and ported to run on a multi-GPU single-node server. The algorithm is mapped to multiple GPUs by decomposing the data domain along the channel dimension. Furthermore, the algorithm is decomposed along the temporal domain by relaxing a temporal regularization constraint, allowing the algorithm to work on multiple frames in parallel. Finally, an autotuning method is presented that is capable of combining different decomposition variants to achieve optimal algorithm performance in different imaging scenarios. Results. The algorithm is successfully ported to a multi-GPU system and allows online image reconstruction with high frame rates. Real-time reconstruction with low latency and frame rates up to 30 frames per second is demonstrated. Conclusion. Novel parallel decomposition methods are presented which are applicable to many iterative algorithms for dynamic MRI. Using these methods to parallelize the NLINV algorithm on multiple GPUs, it is possible to achieve online image reconstruction with high frame rates.

A new approximate algorithm for image reconstruction in cone-beam spiral CT at small cone-angles

International Nuclear Information System (INIS)

Schaller, S.; Flohr, T.; Steffen, P.

1996-01-01

This paper presents a new approximate algorithm for image reconstruction with cone-beam spiral CT data at relatively small cone-angles. Based on the algorithm of Wang et al., our method combines a special complementary interpolation with filtered backprojection. The presented algorithm has three main advantages over Wang's algorithm: (1) It overcomes the pitch limitation of Wang's algorithm. (2) It significantly improves z-resolution when suitable sampling schemes are applied. (3) It avoids the waste of applied radiation dose inherent to Wang's algorithm. Usage of the total applied dose is an important requirement in medical imaging. Our method has been implemented on a standard workstation. Reconstructions of computer-simulated data of different phantoms, assuming sampling conditions and image quality requirements typical to medical CT, show encouraging results

Image reconstruction technique using projection data from neutron tomography system

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Waleed Abd el Bar

2015-12-01

Full Text Available Neutron tomography is a very powerful technique for nondestructive evaluation of heavy industrial components as well as for soft hydrogenous materials enclosed in heavy metals which are usually difficult to image using X-rays. Due to the properties of the image acquisition system, the projection images are distorted by several artifacts, and these reduce the quality of the reconstruction. In order to eliminate these harmful effects the projection images should be corrected before reconstruction. This paper gives a description of a filter back projection (FBP technique, which is used for reconstruction of projected data obtained from transmission measurements by neutron tomography system We demonstrated the use of spatial Discrete Fourier Transform (DFT and the 2D Inverse DFT in the formulation of the method, and outlined the theory of reconstruction of a 2D neutron image from a sequence of 1D projections taken at different angles between 0 and π in MATLAB environment. Projections are generated by applying the Radon transform to the original image at different angles.

Development of a new prior knowledge based image reconstruction algorithm for the cone-beam-CT in radiation therapy

International Nuclear Information System (INIS)

Vaegler, Sven

2016-01-01

follow up reconstructed images are not appropriate considered so far. These deviations may result from changes in anatomy including tumour shrinkage and loss of weight and may result in a degraded image quality of the reconstructed images. Deformable registration methods that adapt the prior images adequately can compensate this shortcoming of PICCS. Such registration techniques, however, suffer from limited accurateness and much higher computation time for the overall reconstruction process. Therefore, the aim of this thesis was to develop a new knowledge-based reconstruction algorithm that incorporates additionally local dependent reliability information about the prior images into reconstruction algorithm. The basic idea of the new algorithm is the assumption that the prior images are composed of areas with large and of areas with small deviations. Accordingly, the areas of the prior image were assigned as variable where substantial deformations due to motion or change in structure over the time series were expected. Hence, these regions were not providing valuable structural information for the anticipated result anymore. In contrast, ''a priori'' information was assigned to structurally stationary areas where no changes were expected. Based on this composition, a weighting matrix was generated that considers the strength of these variations during reconstruction. The new algorithm was tested in different feasibility studies to common dose reduction strategies. These dose reduction strategies includes the reduction of the number of projections, the acquisition of projections with strong noise and the reduction of the acquisition space. The main aim of this work was to demonstrate the gain of image quality when prior images with major variations are used compared to standard reconstruction techniques. The studies were performed with a computer phantom, and in particular with experimental data that have been acquired with the clinical CBCT. The new reconstruction

Photogrammetric 3D reconstruction using mobile imaging

Science.gov (United States)

Fritsch, Dieter; Syll, Miguel

2015-03-01

In our paper we demonstrate the development of an Android Application (AndroidSfM) for photogrammetric 3D reconstruction that works on smartphones and tablets likewise. The photos are taken with mobile devices, and can thereafter directly be calibrated using standard calibration algorithms of photogrammetry and computer vision, on that device. Due to still limited computing resources on mobile devices, a client-server handshake using Dropbox transfers the photos to the sever to run AndroidSfM for the pose estimation of all photos by Structure-from-Motion and, thereafter, uses the oriented bunch of photos for dense point cloud estimation by dense image matching algorithms. The result is transferred back to the mobile device for visualization and ad-hoc on-screen measurements.

Block Compressed Sensing of Images Using Adaptive Granular Reconstruction

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Ran Li

2016-01-01

Full Text Available In the framework of block Compressed Sensing (CS, the reconstruction algorithm based on the Smoothed Projected Landweber (SPL iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.

Adaptive reconstructions for magnetic resonance imaging of moving organs

International Nuclear Information System (INIS)

Lohezic, Maelene

2011-01-01

Magnetic resonance imaging (MRI) is a valuable tool for the clinical diagnosis for brain imaging as well as cardiac and abdominal imaging. For instance, MRI is the only modality that enables the visualization and characterization myocardial edema. However, motion remains a challenging problem for cardiac MRI. Breathing as well as cardiac beating have to be carefully handled during patient examination. Moreover they limit the achievable temporal and spatial resolution of the images. In this work an approach that takes these physiological motions into account during image reconstruction process has been proposed. It allows performing cardiac examination while breathing freely. First, an iterative reconstruction algorithm, that compensates motion estimated from a motion model constrained by physiological signals, is applied to morphological cardiac imaging. A semi-automatic method for edema detection has been tested on reconstructed images. It has also been associated with an adaptive acquisition strategy which enables free-breathing end-systolic imaging. This reconstruction has then been extended to the assessment of transverse relaxation times T2, which is used for myocardial edema characterization. The proposed method, ARTEMIS, enables free-breathing T2 mapping without additional acquisition time. The proposed free breathing approaches take advantage of physiological signals to estimate the motion that occurs during MR acquisitions. Several solutions have been tested to measure this information. Among them, accelerometer-based external sensors allow local measurements at several locations. Another approach consists in the use of k-space based measurements, which are 'embedded' inside the MRI pulse sequence (navigator) and prevent from the requirement of additional recording hardware. Hence, several adaptive reconstruction algorithms were developed to obtain diagnostic information from free breathing acquisitions. These works allow performing efficient and accurate

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

Science.gov (United States)

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.

Application of Super-Resolution Image Reconstruction to Digital Holography

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Zhang Shuqun

2006-01-01

Full Text Available We describe a new application of super-resolution image reconstruction to digital holography which is a technique for three-dimensional information recording and reconstruction. Digital holography has suffered from the low resolution of CCD sensors, which significantly limits the size of objects that can be recorded. The existing solution to this problem is to use optics to bandlimit the object to be recorded, which can cause the loss of details. Here super-resolution image reconstruction is proposed to be applied in enhancing the spatial resolution of digital holograms. By introducing a global camera translation before sampling, a high-resolution hologram can be reconstructed from a set of undersampled hologram images. This permits the recording of larger objects and reduces the distance between the object and the hologram. Practical results from real and simulated holograms are presented to demonstrate the feasibility of the proposed technique.

Edge-oriented dual-dictionary guided enrichment (EDGE) for MRI-CT image reconstruction.

Science.gov (United States)

Li, Liang; Wang, Bigong; Wang, Ge

2016-01-01

In this paper, we formulate the joint/simultaneous X-ray CT and MRI image reconstruction. In particular, a novel algorithm is proposed for MRI image reconstruction from highly under-sampled MRI data and CT images. It consists of two steps. First, a training dataset is generated from a series of well-registered MRI and CT images on the same patients. Then, an initial MRI image of a patient can be reconstructed via edge-oriented dual-dictionary guided enrichment (EDGE) based on the training dataset and a CT image of the patient. Second, an MRI image is reconstructed using the dictionary learning (DL) algorithm from highly under-sampled k-space data and the initial MRI image. Our algorithm can establish a one-to-one correspondence between the two imaging modalities, and obtain a good initial MRI estimation. Both noise-free and noisy simulation studies were performed to evaluate and validate the proposed algorithm. The results with different under-sampling factors show that the proposed algorithm performed significantly better than those reconstructed using the DL algorithm from MRI data alone.

Paediatric cardiac CT examinations: impact of the iterative reconstruction method ASIR on image quality--preliminary findings.

Science.gov (United States)

Miéville, Frédéric A; Gudinchet, François; Rizzo, Elena; Ou, Phalla; Brunelle, Francis; Bochud, François O; Verdun, Francis R

2011-09-01

Radiation dose exposure is of particular concern in children due to the possible harmful effects of ionizing radiation. The adaptive statistical iterative reconstruction (ASIR) method is a promising new technique that reduces image noise and produces better overall image quality compared with routine-dose contrast-enhanced methods. To assess the benefits of ASIR on the diagnostic image quality in paediatric cardiac CT examinations. Four paediatric radiologists based at two major hospitals evaluated ten low-dose paediatric cardiac examinations (80 kVp, CTDI(vol) 4.8-7.9 mGy, DLP 37.1-178.9 mGy·cm). The average age of the cohort studied was 2.6 years (range 1 day to 7 years). Acquisitions were performed on a 64-MDCT scanner. All images were reconstructed at various ASIR percentages (0-100%). For each examination, radiologists scored 19 anatomical structures using the relative visual grading analysis method. To estimate the potential for dose reduction, acquisitions were also performed on a Catphan phantom and a paediatric phantom. The best image quality for all clinical images was obtained with 20% and 40% ASIR (p ASIR above 50%, image quality significantly decreased (p ASIR, a strong noise-free appearance of the structures reduced image conspicuity. A potential for dose reduction of about 36% is predicted for a 2- to 3-year-old child when using 40% ASIR rather than the standard filtered back-projection method. Reconstruction including 20% to 40% ASIR slightly improved the conspicuity of various paediatric cardiac structures in newborns and children with respect to conventional reconstruction (filtered back-projection) alone.

A clearer view of the insect brain – combining bleaching with standard whole-mount immunocytochemistry allows confocal imaging of pigment-covered brain areas for 3D reconstruction.

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Anna Lisa Stöckl

2015-09-01

Full Text Available In the study of insect neuroanatomy, three-dimensional reconstructions of neurons and neuropils have become a standard technique. As images have to be obtained from whole-mount brain preparations, pigmentation on the brain surface poses a serious challenge to imaging. In insects, this is a major problematic in the first visual neuropil of the optic lobe, the lamina, which is obstructed by the pigment of the retina as well as by the pigmented fenestration layer. This has prevented inclusion of this major processing center of the insect visual system into most neuroanatomical brain atlases and hinders imaging of neurons within the lamina by confocal microscopy. It has recently been shown that hydrogen peroxide bleaching is compatible with immunohistochemical labeling in insect brains, and we therefore developed a simple technique for removal of pigments on the surface of insect brains by chemical bleaching. We show that our technique enables imaging of the pigment-obstructed regions of insect brains when combined with standard protocols for both anti-synapsin-labeled as well as neurobiotin-injected samples. This method can be combined with different fixation procedures, as well as different fluorophore excitation wavelengths without negative effects on staining quality. It can therefore serve as an effective addition to most standard histology protocols used in insect neuroanatomy.

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.

Algorithms for reconstructing images for industrial applications

International Nuclear Information System (INIS)

Lopes, R.T.; Crispim, V.R.

1986-01-01

Several algorithms for reconstructing objects from their projections are being studied in our Laboratory, for industrial applications. Such algorithms are useful locating the position and shape of different composition of materials in the object. A Comparative study of two algorithms is made. The two investigated algorithsm are: The MART (Multiplicative - Algebraic Reconstruction Technique) and the Convolution Method. The comparison are carried out from the point view of the quality of the image reconstructed, number of views and cost. (Author) [pt

Reconstruction algorithm medical imaging DRR; Algoritmo de construccion de imagenes medicas DRR

Energy Technology Data Exchange (ETDEWEB)

Estrada Espinosa, J. C.

2013-07-01

The method of reconstruction for digital radiographic Imaging (DRR), is based on two orthogonal images, on the dorsal and lateral decubitus position of the simulation. DRR images are reconstructed with an algorithm that simulates running a conventional X-ray, a single rendition team, beam emitted is not divergent, in this case, the rays are considered to be parallel in the image reconstruction DRR, for this purpose, it is necessary to use all the values of the units (HU) hounsfield of each voxel in all axial cuts that form the study TC, finally obtaining the reconstructed image DRR performing a transformation from 3D to 2D. (Author)

A fast image reconstruction technique based on ART

International Nuclear Information System (INIS)

Zhang Shunli; Zhang Dinghua; Wang Kai; Huang Kuidong; Li Weibin

2007-01-01

Algebraic Reconstruction Technique (ART) is an iterative method for image reconstruction. Improving its reconstruction speed has been one of the important researching aspects of ART. For the simplified weight coefficients reconstruction model of ART, a fast grid traverse algorithm is proposed, which can determine the grid index by simple operations such as addition, subtraction and comparison. Since the weight coefficients are calculated at real time during iteration, large amount of storage is saved and the reconstruction speed is greatly increased. Experimental results show that the new algorithm is very effective and the reconstruction speed is improved about 10 times compared with the traditional algorithm. (authors)

High spatial resolution CT image reconstruction using parallel computing

International Nuclear Information System (INIS)

Yin Yin; Liu Li; Sun Gongxing

2003-01-01

Using the PC cluster system with 16 dual CPU nodes, we accelerate the FBP and OR-OSEM reconstruction of high spatial resolution image (2048 x 2048). Based on the number of projections, we rewrite the reconstruction algorithms into parallel format and dispatch the tasks to each CPU. By parallel computing, the speedup factor is roughly equal to the number of CPUs, which can be up to about 25 times when 25 CPUs used. This technique is very suitable for real-time high spatial resolution CT image reconstruction. (authors)

Evaluation of aortocoronary bypass graft patency by reconstructed CT image

International Nuclear Information System (INIS)

Kawakita, Seizaburo; Koide, Takashi; Saito, Yoshio; Yamamoto, Tadao; Iwasaki, Tadaaki

1982-01-01

Ten patients were examined in the period of three months from January to March 1981. The patients were operated from 1 month to 7 years before CT. A bypass to the left anterior descending artery (LAD) was grafted in 10 cases, 2 to the right coronary artery (RCA), 4 to an obtuse marginal artery (OM), and 1 to a diagonal artery. Image reconstruction was performed in 10 cases by using an image analytical computer Evaluskop. Appropriate planes for reconstruction were selected by trial and error methods upon observation of CT images. When gained picture of a graft course coincided with surgical records or angiography, the work of building images was concluded. On cross section, grafts to LAD were visualized in all 10 cases: 9 in the entire course and 1 in a proximal part of the graft. Two to RCA, 4 to OM and 1 to a diagonal were also successfully visualized. Reconstruction of graft images succeeded in 9 grafts of 6 cases. The course of a graft could be pursued from the proximal to the distal end adjacent to the cardiac chamber. The picture of a bypass to LAD was visualized in 6 of 10 grafts. Two bypass to RCA could be depicted, and 1 to OM was also found. However 3 to OM and 1 to a diagonal failed to be visualized throughout their courses in reconstructed images. I think that the causes of faillure mainly depended upon the course of the graft. When a graft was running arc-like surrounding the heart chamber, it was very difficult to depict its entire length in reconstructed images, though the graft could be detected in cross sections. These preliminary studies indicated that reconstruction of CT images had some benefits for the pursuit of graft courses. (J.P.N.)

Acceleration of the direct reconstruction of linear parametric images using nested algorithms

International Nuclear Information System (INIS)

Wang Guobao; Qi Jinyi

2010-01-01

Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.

Proton computed tomography images with algebraic reconstruction

Energy Technology Data Exchange (ETDEWEB)

Bruzzi, M. [Physics and Astronomy Department, University of Florence, Florence (Italy); Civinini, C.; Scaringella, M. [INFN - Florence Division, Florence (Italy); Bonanno, D. [INFN - Catania Division, Catania (Italy); Brianzi, M. [INFN - Florence Division, Florence (Italy); Carpinelli, M. [INFN - Laboratori Nazionali del Sud, Catania (Italy); Chemistry and Pharmacy Department, University of Sassari, Sassari (Italy); Cirrone, G.A.P.; Cuttone, G. [INFN - Laboratori Nazionali del Sud, Catania (Italy); Presti, D. Lo [INFN - Catania Division, Catania (Italy); Physics and Astronomy Department, University of Catania, Catania (Italy); Maccioni, G. [INFN – Cagliari Division, Cagliari (Italy); Pallotta, S. [INFN - Florence Division, Florence (Italy); Department of Biomedical, Experimental and Clinical Sciences, University of Florence, Florence (Italy); SOD Fisica Medica, Azienda Ospedaliero-Universitaria Careggi, Firenze (Italy); Randazzo, N. [INFN - Catania Division, Catania (Italy); Romano, F. [INFN - Laboratori Nazionali del Sud, Catania (Italy); Sipala, V. [INFN - Laboratori Nazionali del Sud, Catania (Italy); Chemistry and Pharmacy Department, University of Sassari, Sassari (Italy); Talamonti, C. [INFN - Florence Division, Florence (Italy); Department of Biomedical, Experimental and Clinical Sciences, University of Florence, Florence (Italy); SOD Fisica Medica, Azienda Ospedaliero-Universitaria Careggi, Firenze (Italy); Vanzi, E. [Fisica Sanitaria, Azienda Ospedaliero-Universitaria Senese, Siena (Italy)

2017-02-11

A prototype of proton Computed Tomography (pCT) system for hadron-therapy has been manufactured and tested in a 175 MeV proton beam with a non-homogeneous phantom designed to simulate high-contrast material. BI-SART reconstruction algorithms have been implemented with GPU parallelism, taking into account of most likely paths of protons in matter. Reconstructed tomography images with density resolutions r.m.s. down to ~1% and spatial resolutions <1 mm, achieved within processing times of ~15′ for a 512×512 pixels image prove that this technique will be beneficial if used instead of X-CT in hadron-therapy.

Image reconstruction of dynamic infrared single-pixel imaging system

Science.gov (United States)

Tong, Qi; Jiang, Yilin; Wang, Haiyan; Guo, Limin

2018-03-01

Single-pixel imaging technique has recently received much attention. Most of the current single-pixel imaging is aimed at relatively static targets or the imaging system is fixed, which is limited by the number of measurements received through the single detector. In this paper, we proposed a novel dynamic compressive imaging method to solve the imaging problem, where exists imaging system motion behavior, for the infrared (IR) rosette scanning system. The relationship between adjacent target images and scene is analyzed under different system movement scenarios. These relationships are used to build dynamic compressive imaging models. Simulation results demonstrate that the proposed method can improve the reconstruction quality of IR image and enhance the contrast between the target and the background in the presence of system movement.

Image reconstruction from limited angle Compton camera data

International Nuclear Information System (INIS)

Tomitani, T.; Hirasawa, M.

2002-01-01

The Compton camera is used for imaging the distributions of γ ray direction in a γ ray telescope for astrophysics and for imaging radioisotope distributions in nuclear medicine without the need for collimators. The integration of γ rays on a cone is measured with the camera, so that some sort of inversion method is needed. Parra found an analytical inversion algorithm based on spherical harmonics expansion of projection data. His algorithm is applicable to the full set of projection data. In this paper, six possible reconstruction algorithms that allow image reconstruction from projections with a finite range of scattering angles are investigated. Four algorithms have instability problems and two others are practical. However, the variance of the reconstructed image diverges in these two cases, so that window functions are introduced with which the variance becomes finite at a cost of spatial resolution. These two algorithms are compared in terms of variance. The algorithm based on the inversion of the summed back-projection is superior to the algorithm based on the inversion of the summed projection. (author)

Model-based iterative reconstruction in pediatric chest CT: assessment of image quality in a prospective study of children with cystic fibrosis

Energy Technology Data Exchange (ETDEWEB)

Mieville, Frederic A.; Bochud, Francois O.; Verdun, Francis R. [Lausanne University Hospital, Institute of Radiation Physics, Lausanne (Switzerland); Berteloot, Laureline; Brunelle, Francis [Necker Children' s Hospital of Paris and University of Paris Descartes, Department of Pediatric Radiology, Paris (France); Grandjean, Albane; Ayestaran, Paul [General Electric Medical Systems Europe, Paris (France); Gudinchet, Francois; Schmidt, Sabine [Lausanne University Hospital, Department of Radiology, Lausanne (Switzerland)

2013-03-15

The potential effects of ionizing radiation are of particular concern in children. The model-based iterative reconstruction VEO trademark is a technique commercialized to improve image quality and reduce noise compared with the filtered back-projection (FBP) method. To evaluate the potential of VEO trademark on diagnostic image quality and dose reduction in pediatric chest CT examinations. Twenty children (mean 11.4 years) with cystic fibrosis underwent either a standard CT or a moderately reduced-dose CT plus a minimum-dose CT performed at 100 kVp. Reduced-dose CT examinations consisted of two consecutive acquisitions: one moderately reduced-dose CT with increased noise index (NI = 70) and one minimum-dose CT at CTDI{sub vol} 0.14 mGy. Standard CTs were reconstructed using the FBP method while low-dose CTs were reconstructed using FBP and VEO. Two senior radiologists evaluated diagnostic image quality independently by scoring anatomical structures using a four-point scale (1 = excellent, 2 = clear, 3 = diminished, 4 = non-diagnostic). Standard deviation (SD) and signal-to-noise ratio (SNR) were also computed. At moderately reduced doses, VEO images had significantly lower SD (P < 0.001) and higher SNR (P < 0.05) in comparison to filtered back-projection images. Further improvements were obtained at minimum-dose CT. The best diagnostic image quality was obtained with VEO at minimum-dose CT for the small structures (subpleural vessels and lung fissures) (P < 0.001). The potential for dose reduction was dependent on the diagnostic task because of the modification of the image texture produced by this reconstruction. At minimum-dose CT, VEO enables important dose reduction depending on the clinical indication and makes visible certain small structures that were not perceptible with filtered back-projection. (orig.)

Sub-milliSievert (sub-mSv) CT colonography: a prospective comparison of image quality and polyp conspicuity at reduced-dose versus standard-dose imaging

Energy Technology Data Exchange (ETDEWEB)

Lubner, Meghan G.; Pooler, B.D.; Kitchin, Douglas R.; Kim, David H.; Munoz del Rio, Alejandro; Pickhardt, Perry J. [University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, Departments of Radiology, Madison, WI (United States); Tang, Jie [University of Wisconsin School of Medicine and Public Health, Medical Physics, Madison, WI (United States); Li, Ke; Chen, Guang-Hong [University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, Departments of Radiology, Madison, WI (United States); University of Wisconsin School of Medicine and Public Health, Medical Physics, Madison, WI (United States)

2015-07-15

To prospectively compare reduced-dose (RD) CT colonography (CTC) with standard-dose (SD) imaging using several reconstruction algorithms. Following SD supine CTC, 40 patients (mean age, 57.3 years; 17 M/23 F; mean BMI, 27.2) underwent an additional RD supine examination (targeted dose reduction, 70-90 %). DLP, CTDI{sub vol}, effective dose, and SSDE were compared. Several reconstruction algorithms were applied to RD series. SD-FBP served as reference standard. Objective image noise, subjective image quality and polyp conspicuity were assessed. Mean CTDI{sub vol} and effective dose for RD series was 0.89 mGy (median 0.65) and 0.6 mSv (median 0.44), compared with 3.8 mGy (median 3.1) and 2.8 mSv (median 2.3) for SD series, respectively. Mean dose reduction was 78 %. Mean image noise was significantly reduced on RD-PICCS (24.3 ± 19HU) and RD-MBIR (19 ± 18HU) compared with RD-FBP (90 ± 33), RD-ASIR (72 ± 27) and SD-FBP (47 ± 14 HU). 2D image quality score was higher with RD-PICCS, RD-MBIR, and SD-FBP (2.7 ± 0.4/2.8 ± 0.4/2.9 ± 0.6) compared with RD-FBP (1.5 ± 0.4) and RD-ASIR (1.8 ± 0.44). A similar trend was seen with 3D image quality scores. Polyp conspicuity scores were similar between SD-FBP/RD-PICCS/RD-MBIR (3.5 ± 0.6/3.2 ± 0.8/3.3 ± 0.6). Sub-milliSievert CTC performed with iterative reconstruction techniques demonstrate decreased image quality compared to SD, but improved image quality compared to RD images reconstructed with FBP. (orig.)

Upgrade to iterative image reconstruction (IR) in MDCT imaging: a clinical study for detailed parameter optimization beyond vendor recommendations using the adaptive statistical iterative reconstruction environment (ASIR) Part2: The chest.

Science.gov (United States)

Mueck, F G; Michael, L; Deak, Z; Scherr, M K; Maxien, D; Geyer, L L; Reiser, M; Wirth, S

2013-07-01

To compare the image quality in dose-reduced 64-row CT of the chest at different levels of adaptive statistical iterative reconstruction (ASIR) to full-dose baseline examinations reconstructed solely with filtered back projection (FBP) in a realistic upgrade scenario. A waiver of consent was granted by the institutional review board (IRB). The noise index (NI) relates to the standard deviation of Hounsfield units in a water phantom. Baseline exams of the chest (NI = 29; LightSpeed VCT XT, GE Healthcare) were intra-individually compared to follow-up studies on a CT with ASIR after system upgrade (NI = 45; Discovery HD750, GE Healthcare), n = 46. Images were calculated in slice and volume mode with ASIR levels of 0 - 100 % in the standard and lung kernel. Three radiologists independently compared the image quality to the corresponding full-dose baseline examinations (-2: diagnostically inferior, -1: inferior, 0: equal, + 1: superior, + 2: diagnostically superior). Statistical analysis used Wilcoxon's test, Mann-Whitney U test and the intraclass correlation coefficient (ICC). The mean CTDIvol decreased by 53 % from the FBP baseline to 8.0 ± 2.3 mGy for ASIR follow-ups; p ASIR 70 % in volume mode (-0.07 ± 0.29, p = 0.29). Concerning the lung kernel, every ASIR level outperformed the baseline image quality (p ASIR 30 % rated best (slice: 0.70 ± 0.6, volume: 0.74 ± 0.61). Vendors' recommendation of 50 % ASIR is fair. In detail, the ASIR 70 % in volume mode for the standard kernel and ASIR 30 % for the lung kernel performed best, allowing for a dose reduction of approximately 50 %. © Georg Thieme Verlag KG Stuttgart · New York.

Colour reconstruction of underwater images

OpenAIRE

Hoth, Julian; Kowalczyk, Wojciech

2017-01-01

Objects look very different in the underwater environment compared to their appearance in sunlight. Images with correct colouring simplify the detection of underwater objects and may allow the use of visual SLAM algorithms developed for land-based robots underwater. Hence, image processing is required. Current algorithms focus on the colour reconstruction of scenery at diving depth where different colours can still be distinguished. At greater depth this is not the case. In this study it is i...

Image Reconstruction Algorithm For Electrical Capacitance Tomography (ECT)

International Nuclear Information System (INIS)

Arko

2001-01-01

). Most image reconstruction algorithms for electrical capacitance tomography (ECT) use sensitivity maps as weighting factors. The computation is fast, involving a simple multiply-and- accumulate (MAC) operation, but the resulting image suffers from blurring due to the soft-field effect of the sensor. This paper presents a low cost iterative method employing proportional thresholding, which improves image quality dramatically. The strategy for implementation, computational cost, and achievable speed is examined when using a personal computer (PC) and Digital Signal Processor (DSP). For PC implementation, Watcom C++ 10.6 and Visual C++ 5.0 compilers were used. The experimental results are compared to the images reconstructed by commercially available software. The new algorithm improves the image quality significantly at a cost of a few iterations. This technique can be readily exploited for online applications

A Superresolution Image Reconstruction Algorithm Based on Landweber in Electrical Capacitance Tomography

Directory of Open Access Journals (Sweden)

Chen Deyun

2013-01-01

Full Text Available According to the image reconstruction accuracy influenced by the “soft field” nature and ill-conditioned problems in electrical capacitance tomography, a superresolution image reconstruction algorithm based on Landweber is proposed in the paper, which is based on the working principle of the electrical capacitance tomography system. The method uses the algorithm which is derived by regularization of solutions derived and derives closed solution by fast Fourier transform of the convolution kernel. So, it ensures the certainty of the solution and improves the stability and quality of image reconstruction results. Simulation results show that the imaging precision and real-time imaging of the algorithm are better than Landweber algorithm, and this algorithm proposes a new method for the electrical capacitance tomography image reconstruction algorithm.

SU-E-I-73: Clinical Evaluation of CT Image Reconstructed Using Interior Tomography

International Nuclear Information System (INIS)

Zhang, J; Ge, G; Winkler, M; Cong, W; Wang, G

2014-01-01

Purpose: Radiation dose reduction has been a long standing challenge in CT imaging of obese patients. Recent advances in interior tomography (reconstruction of an interior region of interest (ROI) from line integrals associated with only paths through the ROI) promise to achieve significant radiation dose reduction without compromising image quality. This study is to investigate the application of this technique in CT imaging through evaluating imaging quality reconstructed from patient data. Methods: Projection data were directly obtained from patients who had CT examinations in a Dual Source CT scanner (DSCT). Two detectors in a DSCT acquired projection data simultaneously. One detector provided projection data for full field of view (FOV, 50 cm) while another detectors provided truncated projection data for a FOV of 26 cm. Full FOV CT images were reconstructed using both filtered back projection and iterative algorithm; while interior tomography algorithm was implemented to reconstruct ROI images. For comparison reason, FBP was also used to reconstruct ROI images. Reconstructed CT images were evaluated by radiologists and compared with images from CT scanner. Results: The results show that the reconstructed ROI image was in excellent agreement with the truth inside the ROI, obtained from images from CT scanner, and the detailed features in the ROI were quantitatively accurate. Radiologists evaluation shows that CT images reconstructed with interior tomography met diagnosis requirements. Radiation dose may be reduced up to 50% using interior tomography, depending on patient size. Conclusion: This study shows that interior tomography can be readily employed in CT imaging for radiation dose reduction. It may be especially useful in imaging obese patients, whose subcutaneous tissue is less clinically relevant but may significantly increase radiation dose

Compositional-prior-guided image reconstruction algorithm for multi-modality imaging

Science.gov (United States)

Fang, Qianqian; Moore, Richard H.; Kopans, Daniel B.; Boas, David A.

2010-01-01

The development of effective multi-modality imaging methods typically requires an efficient information fusion model, particularly when combining structural images with a complementary imaging modality that provides functional information. We propose a composition-based image segmentation method for X-ray digital breast tomosynthesis (DBT) and a structural-prior-guided image reconstruction for a combined DBT and diffuse optical tomography (DOT) breast imaging system. Using the 3D DBT images from 31 clinically measured healthy breasts, we create an empirical relationship between the X-ray intensities for adipose and fibroglandular tissue. We use this relationship to then segment another 58 healthy breast DBT images from 29 subjects into compositional maps of different tissue types. For each breast, we build a weighted-graph in the compositional space and construct a regularization matrix to incorporate the structural priors into a finite-element-based DOT image reconstruction. Use of the compositional priors enables us to fuse tissue anatomy into optical images with less restriction than when using a binary segmentation. This allows us to recover the image contrast captured by DOT but not by DBT. We show that it is possible to fine-tune the strength of the structural priors by changing a single regularization parameter. By estimating the optical properties for adipose and fibroglandular tissue using the proposed algorithm, we found the results are comparable or superior to those estimated with expert-segmentations, but does not involve the time-consuming manual selection of regions-of-interest. PMID:21258460

Cryo-EM Structure Determination Using Segmented Helical Image Reconstruction.

Science.gov (United States)

Fromm, S A; Sachse, C

2016-01-01

Treating helices as single-particle-like segments followed by helical image reconstruction has become the method of choice for high-resolution structure determination of well-ordered helical viruses as well as flexible filaments. In this review, we will illustrate how the combination of latest hardware developments with optimized image processing routines have led to a series of near-atomic resolution structures of helical assemblies. Originally, the treatment of helices as a sequence of segments followed by Fourier-Bessel reconstruction revealed the potential to determine near-atomic resolution structures from helical specimens. In the meantime, real-space image processing of helices in a stack of single particles was developed and enabled the structure determination of specimens that resisted classical Fourier helical reconstruction and also facilitated high-resolution structure determination. Despite the progress in real-space analysis, the combination of Fourier and real-space processing is still commonly used to better estimate the symmetry parameters as the imposition of the correct helical symmetry is essential for high-resolution structure determination. Recent hardware advancement by the introduction of direct electron detectors has significantly enhanced the image quality and together with improved image processing procedures has made segmented helical reconstruction a very productive cryo-EM structure determination method. © 2016 Elsevier Inc. All rights reserved.

Y-90 SPECT ML image reconstruction with a new model for tissue-dependent bremsstrahlung production using CT information: a proof-of-concept study

Science.gov (United States)

Lim, Hongki; Fessler, Jeffrey A.; Wilderman, Scott J.; Brooks, Allen F.; Dewaraja, Yuni K.

2018-06-01

While the yield of positrons used in Y-90 PET is independent of tissue media, Y-90 SPECT imaging is complicated by the tissue dependence of bremsstrahlung photon generation. The probability of bremsstrahlung production is proportional to the square of the atomic number of the medium. Hence, the same amount of activity in different tissue regions of the body will produce different numbers of bremsstrahlung photons. Existing reconstruction methods disregard this tissue-dependency, potentially impacting both qualitative and quantitative imaging of heterogeneous regions of the body such as bone with marrow cavities. In this proof-of-concept study, we propose a new maximum-likelihood method that incorporates bremsstrahlung generation probabilities into the system matrix, enabling images of the desired Y-90 distribution to be reconstructed instead of the ‘bremsstrahlung distribution’ that is obtained with existing methods. The tissue-dependent probabilities are generated by Monte Carlo simulation while bone volume fractions for each SPECT voxel are obtained from co-registered CT. First, we demonstrate the tissue dependency in a SPECT/CT imaging experiment with Y-90 in bone equivalent solution and water. Visually, the proposed reconstruction approach better matched the true image and the Y-90 PET image than the standard bremsstrahlung reconstruction approach. An XCAT phantom simulation including bone and marrow regions also demonstrated better agreement with the true image using the proposed reconstruction method. Quantitatively, compared with the standard reconstruction, the new method improved estimation of the liquid bone:water activity concentration ratio by 40% in the SPECT measurement and the cortical bone:marrow activity concentration ratio by 58% in the XCAT simulation.

Analytic 3D image reconstruction using all detected events

International Nuclear Information System (INIS)

Kinahan, P.E.; Rogers, J.G.

1988-11-01

We present the results of testing a previously presented algorithm for three-dimensional image reconstruction that uses all gamma-ray coincidence events detected by a PET volume-imaging scanner. By using two iterations of an analytic filter-backprojection method, the algorithm is not constrained by the requirement of a spatially invariant detector point spread function, which limits normal analytic techniques. Removing this constraint allows the incorporation of all detected events, regardless of orientation, which improves the statistical quality of the final reconstructed image

Multiple-image hiding using super resolution reconstruction in high-frequency domains

Science.gov (United States)

Li, Xiao-Wei; Zhao, Wu-Xiang; Wang, Jun; Wang, Qiong-Hua

2017-12-01

In this paper, a robust multiple-image hiding method using the computer-generated integral imaging and the modified super-resolution reconstruction algorithm is proposed. In our work, the host image is first transformed into frequency domains by cellular automata (CA), to assure the quality of the stego-image, the secret images are embedded into the CA high-frequency domains. The proposed method has the following advantages: (1) robustness to geometric attacks because of the memory-distributed property of elemental images, (2) increasing quality of the reconstructed secret images as the scheme utilizes the modified super-resolution reconstruction algorithm. The simulation results show that the proposed multiple-image hiding method outperforms other similar hiding methods and is robust to some geometric attacks, e.g., Gaussian noise and JPEG compression attacks.

Reconstruction-of-difference (RoD) imaging for cone-beam CT neuro-angiography

Science.gov (United States)

Wu, P.; Stayman, J. W.; Mow, M.; Zbijewski, W.; Sisniega, A.; Aygun, N.; Stevens, R.; Foos, D.; Wang, X.; Siewerdsen, J. H.

2018-06-01

Timely evaluation of neurovasculature via CT angiography (CTA) is critical to the detection of pathology such as ischemic stroke. Cone-beam CTA (CBCT-A) systems provide potential advantages in the timely use at the point-of-care, although challenges of a relatively slow gantry rotation speed introduce tradeoffs among image quality, data consistency and data sparsity. This work describes and evaluates a new reconstruction-of-difference (RoD) approach that is robust to such challenges. A fast digital simulation framework was developed to test the performance of the RoD over standard reference reconstruction methods such as filtered back-projection (FBP) and penalized likelihood (PL) over a broad range of imaging conditions, grouped into three scenarios to test the trade-off between data consistency, data sparsity and peak contrast. Two experiments were also conducted using a CBCT prototype and an anthropomorphic neurovascular phantom to test the simulation findings in real data. Performance was evaluated primarily in terms of normalized root mean square error (NRMSE) in comparison to truth, with reconstruction parameters chosen to optimize performance in each case to ensure fair comparison. The RoD approach reduced NRMSE in reconstructed images by up to 50%–53% compared to FBP and up to 29%–31% compared to PL for each scenario. Scan protocols well suited to the RoD approach were identified that balance tradeoffs among data consistency, sparsity and peak contrast—for example, a CBCT-A scan with 128 projections acquired in 8.5 s over a 180°  +  fan angle half-scan for a time attenuation curve with ~8.5 s time-to-peak and 600 HU peak contrast. With imaging conditions such as the simulation scenarios of fixed data sparsity (i.e. varying levels of data consistency and peak contrast), the experiments confirmed the reduction of NRMSE by 34% and 17% compared to FBP and PL, respectively. The RoD approach demonstrated superior performance in 3D angiography

Influence of heart rate on diagnostic accuracy and image quality of 16-slice CT coronary angiography: comparison of multisegment and halfscan reconstruction approaches

Energy Technology Data Exchange (ETDEWEB)

Dewey, Marc; Teige, Florian; Hamm, Bernd [Charite - Universitaetsmedizin Berlin, Humboldt-Universitaet zu Berlin, Department of Radiology, Chariteplatz 1, P.O. Box 10098, Berlin (Germany); Laule, Michael [Department of Cardiology, Charite, Berlin (Germany)

2007-11-15

The lower the heart rate the better image quality in multislice computed tomography (MSCT) coronary angiography. We prospectively assessed the influence of heart rate on per-patient diagnostic accuracy and image quality of MSCT coronary angiography and compared adaptive multisegment and standard halfscan reconstruction. A consecutive cohort of 126 patients scheduled to undergo conventional coronary angiography was examined with 16-slice CT. For all heart rate groups, per-patient diagnostic accuracy was significantly higher for multisegment than halfscan reconstruction with values of 95 vs. 79% (p < 0.05, <65 bpm, 38 patients), 85 vs. 66% (p < 0.05, 65-74 bpm, 47 patients), and 78% vs. 41% (p < 0.001, >74 bpm, 41 patients). Differences in diagnostic accuracy between adjacent heart rate groups were only significant for halfscan reconstruction for the comparison between the 65-74 and >74 bpm group (p < 0.05). The vessel lengths free of motion artifacts were significantly longer with multisegment reconstruction in all heart rate groups and for all coronary arteries (p < 0.005). For noninvasive MSCT coronary angiography, both per-patient diagnostic accuracy and image quality decline with increasing heart rate, and multisegment reconstruction at high heart rates yields similar results as standard halfscan reconstruction at low heart rates. (orig.)

Computed tomography of the cervical spine: comparison of image quality between a standard-dose and a low-dose protocol using filtered back-projection and iterative reconstruction

Energy Technology Data Exchange (ETDEWEB)

Becce, Fabio [University of Lausanne, Department of Diagnostic and Interventional Radiology, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland); Universite Catholique Louvain, Department of Radiology, Cliniques Universitaires Saint-Luc, Brussels (Belgium); Ben Salah, Yosr; Berg, Bruno C. vande; Lecouvet, Frederic E.; Omoumi, Patrick [Universite Catholique Louvain, Department of Radiology, Cliniques Universitaires Saint-Luc, Brussels (Belgium); Verdun, Francis R. [University of Lausanne, Institute of Radiation Physics, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland); Meuli, Reto [University of Lausanne, Department of Diagnostic and Interventional Radiology, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland)

2013-07-15

To compare image quality of a standard-dose (SD) and a low-dose (LD) cervical spine CT protocol using filtered back-projection (FBP) and iterative reconstruction (IR). Forty patients investigated by cervical spine CT were prospectively randomised into two groups: SD (120 kVp, 275 mAs) and LD (120 kVp, 150 mAs), both applying automatic tube current modulation. Data were reconstructed using both FBP and sinogram-affirmed IR. Image noise, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were measured. Two radiologists independently and blindly assessed the following anatomical structures at C3-C4 and C6-C7 levels, using a four-point scale: intervertebral disc, content of neural foramina and dural sac, ligaments, soft tissues and vertebrae. They subsequently rated overall image quality using a ten-point scale. For both protocols and at each disc level, IR significantly decreased image noise and increased SNR and CNR, compared with FBP. SNR and CNR were statistically equivalent in LD-IR and SD-FBP protocols. Regardless of the dose and disc level, the qualitative scores with IR compared with FBP, and with LD-IR compared with SD-FBP, were significantly higher or not statistically different for intervertebral discs, neural foramina and ligaments, while significantly lower or not statistically different for soft tissues and vertebrae. The overall image quality scores were significantly higher with IR compared with FBP, and with LD-IR compared with SD-FBP. LD-IR cervical spine CT provides better image quality for intervertebral discs, neural foramina and ligaments, and worse image quality for soft tissues and vertebrae, compared with SD-FBP, while reducing radiation dose by approximately 40 %. (orig.)

Computed tomography of the cervical spine: comparison of image quality between a standard-dose and a low-dose protocol using filtered back-projection and iterative reconstruction

International Nuclear Information System (INIS)

Becce, Fabio; Ben Salah, Yosr; Berg, Bruno C. vande; Lecouvet, Frederic E.; Omoumi, Patrick; Verdun, Francis R.; Meuli, Reto

2013-01-01

To compare image quality of a standard-dose (SD) and a low-dose (LD) cervical spine CT protocol using filtered back-projection (FBP) and iterative reconstruction (IR). Forty patients investigated by cervical spine CT were prospectively randomised into two groups: SD (120 kVp, 275 mAs) and LD (120 kVp, 150 mAs), both applying automatic tube current modulation. Data were reconstructed using both FBP and sinogram-affirmed IR. Image noise, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were measured. Two radiologists independently and blindly assessed the following anatomical structures at C3-C4 and C6-C7 levels, using a four-point scale: intervertebral disc, content of neural foramina and dural sac, ligaments, soft tissues and vertebrae. They subsequently rated overall image quality using a ten-point scale. For both protocols and at each disc level, IR significantly decreased image noise and increased SNR and CNR, compared with FBP. SNR and CNR were statistically equivalent in LD-IR and SD-FBP protocols. Regardless of the dose and disc level, the qualitative scores with IR compared with FBP, and with LD-IR compared with SD-FBP, were significantly higher or not statistically different for intervertebral discs, neural foramina and ligaments, while significantly lower or not statistically different for soft tissues and vertebrae. The overall image quality scores were significantly higher with IR compared with FBP, and with LD-IR compared with SD-FBP. LD-IR cervical spine CT provides better image quality for intervertebral discs, neural foramina and ligaments, and worse image quality for soft tissues and vertebrae, compared with SD-FBP, while reducing radiation dose by approximately 40 %. (orig.)

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)

Fingerprint image reconstruction for swipe sensor using Predictive Overlap Method

Directory of Open Access Journals (Sweden)

Mardiansyah Ahmad Zafrullah

2018-01-01

Full Text Available Swipe sensor is one of many biometric authentication sensor types that widely applied to embedded devices. The sensor produces an overlap on every pixel block of the image, so the picture requires a reconstruction process before heading to the feature extraction process. Conventional reconstruction methods require extensive computation, causing difficult to apply to embedded devices that have limited computing process. In this paper, image reconstruction is proposed using predictive overlap method, which determines the image block shift from the previous set of change data. The experiments were performed using 36 images generated by a swipe sensor with 128 x 8 pixels size of the area, where each image has an overlap in each block. The results reveal computation can increase up to 86.44% compared with conventional methods, with accuracy decreasing to 0.008% in average.

Development of computed tomography system and image reconstruction algorithm

International Nuclear Information System (INIS)

Khairiah Yazid; Mohd Ashhar Khalid; Azaman Ahmad; Khairul Anuar Mohd Salleh; Ab Razak Hamzah

2006-01-01

Computed tomography is one of the most advanced and powerful nondestructive inspection techniques, which is currently used in many different industries. In several CT systems, detection has been by combination of an X-ray image intensifier and charge -coupled device (CCD) camera or by using line array detector. The recent development of X-ray flat panel detector has made fast CT imaging feasible and practical. Therefore this paper explained the arrangement of a new detection system which is using the existing high resolution (127 μm pixel size) flat panel detector in MINT and the image reconstruction technique developed. The aim of the project is to develop a prototype flat panel detector based CT imaging system for NDE. The prototype consisted of an X-ray tube, a flat panel detector system, a rotation table and a computer system to control the sample motion and image acquisition. Hence this project is divided to two major tasks, firstly to develop image reconstruction algorithm and secondly to integrate X-ray imaging components into one CT system. The image reconstruction algorithm using filtered back-projection method is developed and compared to other techniques. The MATLAB program is the tools used for the simulations and computations for this project. (Author)

Paediatric cardiac CT examinations: impact of the iterative reconstruction method ASIR on image quality - preliminary findings

International Nuclear Information System (INIS)

Mieville, Frederic A.; Gudinchet, Francois; Rizzo, Elena; Ou, Phalla; Brunelle, Francis; Bochud, Francois O.; Verdun, Francis R.

2011-01-01

Radiation dose exposure is of particular concern in children due to the possible harmful effects of ionizing radiation. The adaptive statistical iterative reconstruction (ASIR) method is a promising new technique that reduces image noise and produces better overall image quality compared with routine-dose contrast-enhanced methods. To assess the benefits of ASIR on the diagnostic image quality in paediatric cardiac CT examinations. Four paediatric radiologists based at two major hospitals evaluated ten low-dose paediatric cardiac examinations (80 kVp, CTDI vol 4.8-7.9 mGy, DLP 37.1-178.9 mGy.cm). The average age of the cohort studied was 2.6 years (range 1 day to 7 years). Acquisitions were performed on a 64-MDCT scanner. All images were reconstructed at various ASIR percentages (0-100%). For each examination, radiologists scored 19 anatomical structures using the relative visual grading analysis method. To estimate the potential for dose reduction, acquisitions were also performed on a Catphan phantom and a paediatric phantom. The best image quality for all clinical images was obtained with 20% and 40% ASIR (p < 0.001) whereas with ASIR above 50%, image quality significantly decreased (p < 0.001). With 100% ASIR, a strong noise-free appearance of the structures reduced image conspicuity. A potential for dose reduction of about 36% is predicted for a 2- to 3-year-old child when using 40% ASIR rather than the standard filtered back-projection method. Reconstruction including 20% to 40% ASIR slightly improved the conspicuity of various paediatric cardiac structures in newborns and children with respect to conventional reconstruction (filtered back-projection) alone. (orig.)

Paediatric cardiac CT examinations: impact of the iterative reconstruction method ASIR on image quality - preliminary findings

Energy Technology Data Exchange (ETDEWEB)

Mieville, Frederic A. [University Hospital Center and University of Lausanne, Institute of Radiation Physics, Lausanne (Switzerland); University Hospital Center and University of Lausanne, Institute of Radiation Physics - Medical Radiology, Lausanne (Switzerland); Gudinchet, Francois; Rizzo, Elena [University Hospital Center and University of Lausanne, Department of Radiology, Lausanne (Switzerland); Ou, Phalla; Brunelle, Francis [Necker Children' s Hospital, Department of Radiology, Paris (France); Bochud, Francois O.; Verdun, Francis R. [University Hospital Center and University of Lausanne, Institute of Radiation Physics, Lausanne (Switzerland)

2011-09-15

Radiation dose exposure is of particular concern in children due to the possible harmful effects of ionizing radiation. The adaptive statistical iterative reconstruction (ASIR) method is a promising new technique that reduces image noise and produces better overall image quality compared with routine-dose contrast-enhanced methods. To assess the benefits of ASIR on the diagnostic image quality in paediatric cardiac CT examinations. Four paediatric radiologists based at two major hospitals evaluated ten low-dose paediatric cardiac examinations (80 kVp, CTDI{sub vol} 4.8-7.9 mGy, DLP 37.1-178.9 mGy.cm). The average age of the cohort studied was 2.6 years (range 1 day to 7 years). Acquisitions were performed on a 64-MDCT scanner. All images were reconstructed at various ASIR percentages (0-100%). For each examination, radiologists scored 19 anatomical structures using the relative visual grading analysis method. To estimate the potential for dose reduction, acquisitions were also performed on a Catphan phantom and a paediatric phantom. The best image quality for all clinical images was obtained with 20% and 40% ASIR (p < 0.001) whereas with ASIR above 50%, image quality significantly decreased (p < 0.001). With 100% ASIR, a strong noise-free appearance of the structures reduced image conspicuity. A potential for dose reduction of about 36% is predicted for a 2- to 3-year-old child when using 40% ASIR rather than the standard filtered back-projection method. Reconstruction including 20% to 40% ASIR slightly improved the conspicuity of various paediatric cardiac structures in newborns and children with respect to conventional reconstruction (filtered back-projection) alone. (orig.)

Direct 4D reconstruction of parametric images incorporating anato-functional joint entropy.

Science.gov (United States)

Tang, Jing; Kuwabara, Hiroto; Wong, Dean F; Rahmim, Arman

2010-08-07

We developed an anatomy-guided 4D closed-form algorithm to directly reconstruct parametric images from projection data for (nearly) irreversible tracers. Conventional methods consist of individually reconstructing 2D/3D PET data, followed by graphical analysis on the sequence of reconstructed image frames. The proposed direct reconstruction approach maintains the simplicity and accuracy of the expectation-maximization (EM) algorithm by extending the system matrix to include the relation between the parametric images and the measured data. A closed-form solution was achieved using a different hidden complete-data formulation within the EM framework. Furthermore, the proposed method was extended to maximum a posterior reconstruction via incorporation of MR image information, taking the joint entropy between MR and parametric PET features as the prior. Using realistic simulated noisy [(11)C]-naltrindole PET and MR brain images/data, the quantitative performance of the proposed methods was investigated. Significant improvements in terms of noise versus bias performance were demonstrated when performing direct parametric reconstruction, and additionally upon extending the algorithm to its Bayesian counterpart using the MR-PET joint entropy measure.

Separated reconstruction of images from ultrasonic holograms with tridimensional object by digital processing

International Nuclear Information System (INIS)

Son, J.H.

1979-01-01

Because of much attractiveness, digital reconstruction of image from ultrasonic hologram by computer has been widely studied in recent years. But the method of digital reconstruction of image is displayed in the plain only, so study is done mainly of the hologram obtained from bidimensional objects. Many applications of the ultrasonic holography such as the non-distructive testing and the ultrasonic diagnosis are mostly of the tridimensional object. In the ordinary digital reconstruction of the image from the hologram obtained from tridimensional object, a question of hidden-image problem arises, and the separated reconstruction of the image for the considered part of the object is required. In this paper, multi-diffraction by tridimensional object is assumed to have linearity, ie. superposition property by each diffraction of bidimensional objects. And a new algorithm is proposed here, namely reconstructed image for considered one of bidimensional objects in tridimensional object obtained by means of operation from the two holograms tilted in unequal angles. Such tilted holograms are obtained from the tilted linear array receivers by scanning method. That images can be reconstructed by the operation from two holograms means that the new algorithm is verified. And another new method of the transformation of hologram, that is, transformation of a hologram to arbitrarily tilted hologram, has been proved valid. The reconstructed images obtained with the method of transformation and the method of operation, are the images reconstructed from one hologram by the tridimensional object and more distinctly separated that any images mentioned above. (author)

3D EIT image reconstruction with GREIT.

Science.gov (United States)

Grychtol, Bartłomiej; Müller, Beat; Adler, Andy

2016-06-01

Most applications of thoracic EIT use a single plane of electrodes on the chest from which a transverse image 'slice' is calculated. However, interpretation of EIT images is made difficult by the large region above and below the electrode plane to which EIT is sensitive. Volumetric EIT images using two (or more) electrode planes should help compensate, but are little used currently. The Graz consensus reconstruction algorithm for EIT (GREIT) has become popular in lung EIT. One shortcoming of the original formulation of GREIT is its restriction to reconstruction onto a 2D planar image. We present an extension of the GREIT algorithm to 3D and develop open-source tools to evaluate its performance as a function of the choice of stimulation and measurement pattern. Results show 3D GREIT using two electrode layers has significantly more uniform sensitivity profiles through the chest region. Overall, the advantages of 3D EIT are compelling.

Graph-cut based discrete-valued image reconstruction.

Science.gov (United States)

Tuysuzoglu, Ahmet; Karl, W Clem; Stojanovic, Ivana; Castañòn, David; Ünlü, M Selim

2015-05-01

Efficient graph-cut methods have been used with great success for labeling and denoising problems occurring in computer vision. Unfortunately, the presence of linear image mappings has prevented the use of these techniques in most discrete-amplitude image reconstruction problems. In this paper, we develop a graph-cut based framework for the direct solution of discrete amplitude linear image reconstruction problems cast as regularized energy function minimizations. We first analyze the structure of discrete linear inverse problem cost functions to show that the obstacle to the application of graph-cut methods to their solution is the variable mixing caused by the presence of the linear sensing operator. We then propose to use a surrogate energy functional that overcomes the challenges imposed by the sensing operator yet can be utilized efficiently in existing graph-cut frameworks. We use this surrogate energy functional to devise a monotonic iterative algorithm for the solution of discrete valued inverse problems. We first provide experiments using local convolutional operators and show the robustness of the proposed technique to noise and stability to changes in regularization parameter. Then we focus on nonlocal, tomographic examples where we consider limited-angle data problems. We compare our technique with state-of-the-art discrete and continuous image reconstruction techniques. Experiments show that the proposed method outperforms state-of-the-art techniques in challenging scenarios involving discrete valued unknowns.

Comparison of pure and hybrid iterative reconstruction techniques with conventional filtered back projection: Image quality assessment in the cervicothoracic region

International Nuclear Information System (INIS)

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

2013-01-01

Objectives: To evaluate the impact on image quality of three different image reconstruction techniques in the cervicothoracic region: model-based iterative reconstruction (MBIR), adaptive statistical iterative reconstruction (ASIR), and filtered back projection (FBP). Methods: Forty-four patients underwent unenhanced standard-of-care clinical computed tomography (CT) examinations which included the cervicothoracic region with a 64-row multidetector CT scanner. Images were reconstructed with FBP, 50% ASIR-FBP blending (ASIR50), and MBIR. Two radiologists assessed the cervicothoracic region in a blinded manner for streak artifacts, pixilated blotchy appearances, critical reproduction of visually sharp anatomical structures (thyroid gland, common carotid artery, and esophagus), and overall diagnostic acceptability. Objective image noise was measured in the internal jugular vein. Data were analyzed using the sign test and pair-wise Student's t-test. Results: MBIR images had significant lower quantitative image noise (8.88 ± 1.32) compared to ASIR images (18.63 ± 4.19, P 0.9 for ASIR vs. FBP for both readers). MBIR images were all diagnostically acceptable. Unique features of MBIR images included pixilated blotchy appearances, which did not adversely affect diagnostic acceptability. Conclusions: MBIR significantly improves image noise and streak artifacts of the cervicothoracic region over ASIR and FBP. MBIR is expected to enhance the value of CT examinations for areas where image noise and streak artifacts are problematic

Propagation of errors from the sensitivity image in list mode reconstruction

International Nuclear Information System (INIS)

Qi, Jinyi; Huesman, Ronald H.

2003-01-01

List mode image reconstruction is attracting renewed attention. It eliminates the storage of empty sinogram bins. However, a single back projection of all LORs is still necessary for the pre-calculation of a sensitivity image. Since the detection sensitivity is dependent on the object attenuation and detector efficiency, it must be computed for each study. Exact computation of the sensitivity image can be a daunting task for modern scanners with huge numbers of LORs. Thus, some fast approximate calculation may be desirable. In this paper, we theoretically analyze the error propagation from the sensitivity image into the reconstructed image. The theoretical analysis is based on the fixed point condition of the list mode reconstruction. The non-negativity constraint is modeled using the Kuhn-Tucker condition. With certain assumptions and the first order Taylor series approximation, we derive a closed form expression for the error in the reconstructed image as a function of the error in the sensitivity image. The result provides insights on what kind of error might be allowable in the sensitivity image. Computer simulations show that the theoretical results are in good agreement with the measured results

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.

PET image reconstruction: mean, variance, and optimal minimax criterion

International Nuclear Information System (INIS)

Liu, Huafeng; Guo, Min; Gao, Fei; Shi, Pengcheng; Xue, Liying; Nie, Jing

2015-01-01

Given the noise nature of positron emission tomography (PET) measurements, it is critical to know the image quality and reliability as well as expected radioactivity map (mean image) for both qualitative interpretation and quantitative analysis. While existing efforts have often been devoted to providing only the reconstructed mean image, we present a unified framework for joint estimation of the mean and corresponding variance of the radioactivity map based on an efficient optimal min–max criterion. The proposed framework formulates the PET image reconstruction problem to be a transformation from system uncertainties to estimation errors, where the minimax criterion is adopted to minimize the estimation errors with possibly maximized system uncertainties. The estimation errors, in the form of a covariance matrix, express the measurement uncertainties in a complete way. The framework is then optimized by ∞-norm optimization and solved with the corresponding H ∞ filter. Unlike conventional statistical reconstruction algorithms, that rely on the statistical modeling methods of the measurement data or noise, the proposed joint estimation stands from the point of view of signal energies and can handle from imperfect statistical assumptions to even no a priori statistical assumptions. The performance and accuracy of reconstructed mean and variance images are validated using Monte Carlo simulations. Experiments on phantom scans with a small animal PET scanner and real patient scans are also conducted for assessment of clinical potential. (paper)

A study of transverse image reconstruction with digital subtraction angiography

International Nuclear Information System (INIS)

Sakamoto, Kiyoshi; Kotoura, Noriko; Terasawa, Yuuji; Oda, Masahiko; Gotou, Hiroshi; Nasada, Toshiya; Tanooka, Masao

1995-01-01

For digital subtraction angiography (DSA) with C-type equipment, it is possible to radiate an X-ray during rotation and to collect data at different angular settings. We tried to reconstruct transverse image from data obtained by scanning DSA images at different angular settings. 88 projection data were obtained by rotating the object at 180deg during radiation. Reconstruction was made using the convolution method with pixel value distribution for each projection. Similarly, the image quality of the reconstructed images were compared with the unsubtracted and subtracted ones. In case a part object was outside the calculating region, artifacts were generally produced. However, the artifacts were reduced by subtracting the background from the image. In addition, the cupping phenomenon caused by beam hardening was relaxed and high-quality imaging could be achieved. This method will become even more effective, if we will use it with selective angiography in which the limited area is enhanced. (author)

Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE

Energy Technology Data Exchange (ETDEWEB)

Solomon, Justin, E-mail: justin.solomon@duke.edu [Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705 (United States); Samei, Ehsan [Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705 and Departments of Biomedical Engineering and Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina 27705 (United States)

2014-09-15

Purpose: Quantum noise properties of CT images are generally assessed using simple geometric phantoms with uniform backgrounds. Such phantoms may be inadequate when assessing nonlinear reconstruction or postprocessing algorithms. The purpose of this study was to design anatomically informed textured phantoms and use the phantoms to assess quantum noise properties across two clinically available reconstruction algorithms, filtered back projection (FBP) and sinogram affirmed iterative reconstruction (SAFIRE). Methods: Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom included intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based on a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and, along with a uniform phantom of similar size, were imaged on a Siemens SOMATOM Definition Flash CT scanner and reconstructed with FBP and SAFIRE. Fifty repeated acquisitions were acquired for each background type and noise was assessed by estimating pixel-value statistics, such as standard deviation (i.e., noise magnitude), autocorrelation, and noise power spectrum. Noise stationarity was also assessed by examining the spatial distribution of noise magnitude. The noise properties were compared across background types and between the two reconstruction algorithms. Results: In FBP and SAFIRE images, noise was globally nonstationary for all phantoms. In FBP images of all phantoms, and in SAFIRE images of the uniform phantom, noise appeared to be locally stationary (within a reasonably small region of interest). Noise was locally nonstationary in SAFIRE images of the textured phantoms with edge pixels showing higher noise magnitude compared to pixels in more homogenous regions. For pixels in uniform regions, noise magnitude was

Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE

International Nuclear Information System (INIS)

Solomon, Justin; Samei, Ehsan

2014-01-01

Purpose: Quantum noise properties of CT images are generally assessed using simple geometric phantoms with uniform backgrounds. Such phantoms may be inadequate when assessing nonlinear reconstruction or postprocessing algorithms. The purpose of this study was to design anatomically informed textured phantoms and use the phantoms to assess quantum noise properties across two clinically available reconstruction algorithms, filtered back projection (FBP) and sinogram affirmed iterative reconstruction (SAFIRE). Methods: Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom included intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based on a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and, along with a uniform phantom of similar size, were imaged on a Siemens SOMATOM Definition Flash CT scanner and reconstructed with FBP and SAFIRE. Fifty repeated acquisitions were acquired for each background type and noise was assessed by estimating pixel-value statistics, such as standard deviation (i.e., noise magnitude), autocorrelation, and noise power spectrum. Noise stationarity was also assessed by examining the spatial distribution of noise magnitude. The noise properties were compared across background types and between the two reconstruction algorithms. Results: In FBP and SAFIRE images, noise was globally nonstationary for all phantoms. In FBP images of all phantoms, and in SAFIRE images of the uniform phantom, noise appeared to be locally stationary (within a reasonably small region of interest). Noise was locally nonstationary in SAFIRE images of the textured phantoms with edge pixels showing higher noise magnitude compared to pixels in more homogenous regions. For pixels in uniform regions, noise magnitude was

An automated 3D reconstruction method of UAV images

Science.gov (United States)

Liu, Jun; Wang, He; Liu, Xiaoyang; Li, Feng; Sun, Guangtong; Song, Ping

2015-10-01

In this paper a novel fully automated 3D reconstruction approach based on low-altitude unmanned aerial vehicle system (UAVs) images will be presented, which does not require previous camera calibration or any other external prior knowledge. Dense 3D point clouds are generated by integrating orderly feature extraction, image matching, structure from motion (SfM) and multi-view stereo (MVS) algorithms, overcoming many of the cost, time limitations of rigorous photogrammetry techniques. An image topology analysis strategy is introduced to speed up large scene reconstruction by taking advantage of the flight-control data acquired by UAV. Image topology map can significantly reduce the running time of feature matching by limiting the combination of images. A high-resolution digital surface model of the study area is produced base on UAV point clouds by constructing the triangular irregular network. Experimental results show that the proposed approach is robust and feasible for automatic 3D reconstruction of low-altitude UAV images, and has great potential for the acquisition of spatial information at large scales mapping, especially suitable for rapid response and precise modelling in disaster emergency.

A comparison of reconstruction methods for undersampled atomic force microscopy images

International Nuclear Information System (INIS)

Luo, Yufan; Andersson, Sean B

2015-01-01

Non-raster scanning and undersampling of atomic force microscopy (AFM) images is a technique for improving imaging rate and reducing the amount of tip–sample interaction needed to produce an image. Generation of the final image can be done using a variety of image processing techniques based on interpolation or optimization. The choice of reconstruction method has a large impact on the quality of the recovered image and the proper choice depends on the sample under study. In this work we compare interpolation through the use of inpainting algorithms with reconstruction based on optimization through the use of the basis pursuit algorithm commonly used for signal recovery in compressive sensing. Using four different sampling patterns found in non-raster AFM, namely row subsampling, spiral scanning, Lissajous scanning, and random scanning, we subsample data from existing images and compare reconstruction performance against the original image. The results illustrate that inpainting generally produces superior results when the image contains primarily low frequency content while basis pursuit is better when the images have mixed, but sparse, frequency content. Using support vector machines, we then classify images based on their frequency content and sparsity and, from this classification, develop a fast decision strategy to select a reconstruction algorithm to be used on subsampled data. The performance of the classification and decision test are demonstrated on test AFM images. (paper)

Brief review of image reconstruction methods for imaging in nuclear medicine

International Nuclear Information System (INIS)

Murayama, Hideo

1999-01-01

Emission computed tomography (ECT) has as its major emphasis the quantitative determination of the moment to moment changes in the chemistry and flow physiology of injected or inhaled compounds labeled with radioactive atoms in a human body. The major difference lies in the fact that ECT seeks to describe the location and intensity of sources of emitted photons in an attenuating medium whereas transmission X-ray computed tomography (TCT) seeks to determine the distribution of the attenuating medium. A second important difference between ECT and TCT is that of available statistics. ECT statistics are low because each photon without control in emitting direction must be detected and analyzed, not as in TCT. The following sections review the historical development of image reconstruction methods for imaging in nuclear medicine, relevant intrinsic concepts for image reconstruction on ECT, and current status of volume imaging as well as a unique approach on iterative techniques for ECT. (author). 130 refs

Fully three-dimensional image reconstruction in radiology and nuclear medicine. Proceedings

Energy Technology Data Exchange (ETDEWEB)

NONE

2011-07-01

The proceedings of the meeting on ''fully three-dimensional image reconstruction in radiology and nuclear medicine'' covers contributions on the following topics: CT imaging, PET imaging, fidelity; iterative and few-view CT, CT-analytical; PET/SPECT Compton analytical; doses - spectral methods; phase contrast; compressed sensing- sparse reconstruction; special issues; motion - cardiac.

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%.

Recent improvements in Hurricane Imaging Radiometer’s brightness temperature image reconstruction

Directory of Open Access Journals (Sweden)

Sayak K. Biswas

Full Text Available NASA MSFCs airborne Hurricane Imaging Radiometer (HIRAD uses interferometric aperture synthesis to produce high resolution wide swath images of scene brightness temperature (Tb distribution at four discrete C-band microwave frequencies (4.0, 5.0, 6.0 and 6.6 GHz. Images of ocean surface wind speed under heavy precipitation such as in tropical cyclones, is inferred from these measurements. The baseline HIRAD Tb reconstruction algorithm had produced prominent along-track streaks in the Tb images. Particularly the 4.0 GHz channel had been so dominated by the streaks as to be unusable.The loss of a frequency channel had compromised the final wind speed retrievals. During 2016, the HIRAD team made substantial progress in developing a quality controlled signal processing technique for the HIRAD data collected in 2015’s Tropical Cyclone Intensity (TCI experiment and reduced the effect of streaks in all channels including 4.0 GHz. 2000 MSC: 41A05, 41A10, 65D05, 65D17, Keywords: Microwave radiometry, Aperture synthesis, Image reconstruction, Hurricane winds

Visual image reconstruction from human brain activity: A modular decoding approach

International Nuclear Information System (INIS)

Miyawaki, Yoichi; Uchida, Hajime; Yamashita, Okito; Sato, Masa-aki; Kamitani, Yukiyasu; Morito, Yusuke; Tanabe, Hiroki C; Sadato, Norihiro

2009-01-01

Brain activity represents our perceptual experience. But the potential for reading out perceptual contents from human brain activity has not been fully explored. In this study, we demonstrate constraint-free reconstruction of visual images perceived by a subject, from the brain activity pattern. We reconstructed visual images by combining local image bases with multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant voxels and exploiting their correlated patterns. Binary-contrast, 10 x 10-patch images (2 100 possible states), were accurately reconstructed without any image prior by measuring brain activity only for several hundred random images. The results suggest that our approach provides an effective means to read out complex perceptual states from brain activity while discovering information representation in multi-voxel patterns.

Fast MR image reconstruction for partially parallel imaging with arbitrary k-space trajectories.

Science.gov (United States)

Ye, Xiaojing; Chen, Yunmei; Lin, Wei; Huang, Feng

2011-03-01

Both acquisition and reconstruction speed are crucial for magnetic resonance (MR) imaging in clinical applications. In this paper, we present a fast reconstruction algorithm for SENSE in partially parallel MR imaging with arbitrary k-space trajectories. The proposed method is a combination of variable splitting, the classical penalty technique and the optimal gradient method. Variable splitting and the penalty technique reformulate the SENSE model with sparsity regularization as an unconstrained minimization problem, which can be solved by alternating two simple minimizations: One is the total variation and wavelet based denoising that can be quickly solved by several recent numerical methods, whereas the other one involves a linear inversion which is solved by the optimal first order gradient method in our algorithm to significantly improve the performance. Comparisons with several recent parallel imaging algorithms indicate that the proposed method significantly improves the computation efficiency and achieves state-of-the-art reconstruction quality.

An efficient simultaneous reconstruction technique for tomographic particle image velocimetry

Science.gov (United States)

Atkinson, Callum; Soria, Julio

2009-10-01

To date, Tomo-PIV has involved the use of the multiplicative algebraic reconstruction technique (MART), where the intensity of each 3D voxel is iteratively corrected to satisfy one recorded projection, or pixel intensity, at a time. This results in reconstruction times of multiple hours for each velocity field and requires considerable computer memory in order to store the associated weighting coefficients and intensity values for each point in the volume. In this paper, a rapid and less memory intensive reconstruction algorithm is presented based on a multiplicative line-of-sight (MLOS) estimation that determines possible particle locations in the volume, followed by simultaneous iterative correction. Reconstructions of simulated images are presented for two simultaneous algorithms (SART and SMART) as well as the now standard MART algorithm, which indicate that the same accuracy as MART can be achieved 5.5 times faster or 77 times faster with 15 times less memory if the processing and storage of the weighting matrix is considered. Application of MLOS-SMART and MART to a turbulent boundary layer at Re θ = 2200 using a 4 camera Tomo-PIV system with a volume of 1,000 × 1,000 × 160 voxels is discussed. Results indicate improvements in reconstruction speed of 15 times that of MART with precalculated weighting matrix, or 65 times if calculation of the weighting matrix is considered. Furthermore the memory needed to store a large weighting matrix and volume intensity is reduced by almost 40 times in this case.

Bayesian image reconstruction for emission tomography based on median root prior

International Nuclear Information System (INIS)

Alenius, S.

1997-01-01

The aim of the present study was to investigate a new type of Bayesian one-step late reconstruction method which utilizes a median root prior (MRP). The method favours images which have locally monotonous radioactivity concentrations. The new reconstruction algorithm was applied to ideal simulated data, phantom data and some patient examinations with PET. The same projection data were reconstructed with filtered back-projection (FBP) and maximum likelihood-expectation maximization (ML-EM) methods for comparison. The MRP method provided good-quality images with a similar resolution to the FBP method with a ramp filter, and at the same time the noise properties were as good as with Hann-filtered FBP images. The typical artefacts seen in FBP reconstructed images outside of the object were completely removed, as was the grainy noise inside the object. Quantitativley, the resulting average regional radioactivity concentrations in a large region of interest in images produced by the MRP method corresponded to the FBP and ML-EM results but at the pixel by pixel level the MRP method proved to be the most accurate of the tested methods. In contrast to other iterative reconstruction methods, e.g. ML-EM, the MRP method was not sensitive to the number of iterations nor to the adjustment of reconstruction parameters. Only the Bayesian parameter β had to be set. The proposed MRP method is much more simple to calculate than the methods described previously, both with regard to the parameter settings and in terms of general use. The new MRP reconstruction method was shown to produce high-quality quantitative emission images with only one parameter setting in addition to the number of iterations. (orig.)

X-ray computed tomography reconstruction on non-standard trajectories for robotized inspection

International Nuclear Information System (INIS)

Banjak, Hussein

2016-01-01

The number of industrial applications of computed tomography (CT) is large and rapidly increasing with typical areas of use in the aerospace, automotive and transport industry. To support this growth of CT in the industrial field, the identified requirements concern firstly software development to improve the reconstruction algorithms and secondly the automation of the inspection process. Indeed, the use of robots gives more flexibility in the acquisition trajectory and allows the control of large and complex objects, which cannot be inspected using classical CT systems. In this context of new CT trend, a robotic platform has been installed at CEA LIST to better understand and solve specific challenges linked to the robotization of the CT process. The considered system integrates two robots that move the X-ray generator and detector. This thesis aims at achieving this new development. In particular, the objective is to develop and implement analytical and iterative reconstruction algorithms adapted to such robotized trajectories. The main focus of this thesis is concerned with helical-like scanning trajectories. We consider two main problems that could occur during acquisition process: truncated and limited-angle data. We present in this work experimental results for reconstruction on such non-standard trajectories. CIVA software is used to simulate these complex inspections and our developed algorithms are integrated as reconstruction tools. This thesis contains three parts. In the first part, we introduce the basic principles of CT and we present an overview of existing analytical and iterative algorithms for non-standard trajectories. In the second part, we modify the approximate helical FDK algorithm to deal with transversely truncated data and we propose a modified FDK algorithm adapted to reverse helical trajectory with the scan range less than 360 degrees. For iterative reconstruction, we propose two algebraic methods named SART-FISTA-TV and DART

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

Statistical shape model-based reconstruction of a scaled, patient-specific surface model of the pelvis from a single standard AP x-ray radiograph

Energy Technology Data Exchange (ETDEWEB)

Zheng Guoyan [Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, CH-3014 Bern (Switzerland)

2010-04-15

Purpose: The aim of this article is to investigate the feasibility of using a statistical shape model (SSM)-based reconstruction technique to derive a scaled, patient-specific surface model of the pelvis from a single standard anteroposterior (AP) x-ray radiograph and the feasibility of estimating the scale of the reconstructed surface model by performing a surface-based 3D/3D matching. Methods: Data sets of 14 pelvises (one plastic bone, 12 cadavers, and one patient) were used to validate the single-image based reconstruction technique. This reconstruction technique is based on a hybrid 2D/3D deformable registration process combining a landmark-to-ray registration with a SSM-based 2D/3D reconstruction. The landmark-to-ray registration was used to find an initial scale and an initial rigid transformation between the x-ray image and the SSM. The estimated scale and rigid transformation were used to initialize the SSM-based 2D/3D reconstruction. The optimal reconstruction was then achieved in three stages by iteratively matching the projections of the apparent contours extracted from a 3D model derived from the SSM to the image contours extracted from the x-ray radiograph: Iterative affine registration, statistical instantiation, and iterative regularized shape deformation. The image contours are first detected by using a semiautomatic segmentation tool based on the Livewire algorithm and then approximated by a set of sparse dominant points that are adaptively sampled from the detected contours. The unknown scales of the reconstructed models were estimated by performing a surface-based 3D/3D matching between the reconstructed models and the associated ground truth models that were derived from a CT-based reconstruction method. Such a matching also allowed for computing the errors between the reconstructed models and the associated ground truth models. Results: The technique could reconstruct the surface models of all 14 pelvises directly from the landmark

PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction

International Nuclear Information System (INIS)

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

2013-01-01

Over the course of diagnosis and treatment, it is common for a number of imaging studies to be acquired. Such imaging sequences can provide substantial patient-specific prior knowledge about the anatomy that can be incorporated into a prior-image-based tomographic reconstruction for improved image quality and better dose utilization. We present a general methodology using a model-based reconstruction approach including formulations of the measurement noise that also integrates prior images. This penalized-likelihood technique adopts a sparsity enforcing penalty that incorporates prior information yet allows for change between the current reconstruction and the prior image. Moreover, since prior images are generally not registered with the current image volume, we present a modified model-based approach that seeks a joint registration of the prior image in addition to the reconstruction of projection data. We demonstrate that the combined prior-image- and model-based technique outperforms methods that ignore the prior data or lack a noise model. Moreover, we demonstrate the importance of registration for prior-image-based reconstruction methods and show that the prior-image-registered penalized-likelihood estimation (PIRPLE) approach can maintain a high level of image quality in the presence of noisy and undersampled projection data. (paper)

ℓ0 Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography.

Directory of Open Access Journals (Sweden)

Wei Yu

Full Text Available In medical and industrial applications of computed tomography (CT imaging, limited by the scanning environment and the risk of excessive X-ray radiation exposure imposed to the patients, reconstructing high quality CT images from limited projection data has become a hot topic. X-ray imaging in limited scanning angular range is an effective imaging modality to reduce the radiation dose to the patients. As the projection data available in this modality are incomplete, limited-angle CT image reconstruction is actually an ill-posed inverse problem. To solve the problem, image reconstructed by conventional filtered back projection (FBP algorithm frequently results in conspicuous streak artifacts and gradual changed artifacts nearby edges. Image reconstruction based on total variation minimization (TVM can significantly reduce streak artifacts in few-view CT, but it suffers from the gradual changed artifacts nearby edges in limited-angle CT. To suppress this kind of artifacts, we develop an image reconstruction algorithm based on ℓ0 gradient minimization for limited-angle CT in this paper. The ℓ0-norm of the image gradient is taken as the regularization function in the framework of developed reconstruction model. We transformed the optimization problem into a few optimization sub-problems and then, solved these sub-problems in the manner of alternating iteration. Numerical experiments are performed to validate the efficiency and the feasibility of the developed algorithm. From the statistical analysis results of the performance evaluations peak signal-to-noise ratio (PSNR and normalized root mean square distance (NRMSD, it shows that there are significant statistical differences between different algorithms from different scanning angular ranges (p<0.0001. From the experimental results, it also indicates that the developed algorithm outperforms classical reconstruction algorithms in suppressing the streak artifacts and the gradual changed

3-D Reconstruction From Satellite Images

DEFF Research Database (Denmark)

Denver, Troelz

1999-01-01

of planetary surfaces, but other purposes is considered as well. The system performance is measured with respect to the precision and the time consumption.The reconstruction process is divided into four major areas: Acquisition, calibration, matching/reconstruction and presentation. Each of these areas...... are treated individually. A detailed treatment of various lens distortions is required, in order to correct for these problems. This subject is included in the acquisition part. In the calibration part, the perspective distortion is removed from the images. Most attention has been paid to the matching problem...

Digital filtering and reconstruction of coded aperture images

International Nuclear Information System (INIS)

Tobin, K.W. Jr.

1987-01-01

The real-time neutron radiography facility at the University of Virginia has been used for both transmission radiography and computed tomography. Recently, a coded aperture system has been developed to permit the extraction of three dimensional information from a low intensity field of radiation scattered by an extended object. Short wave-length radiations (e.g. neutrons) are not easily image because of the difficulties in achieving diffraction and refraction with a conventional lens imaging system. By using a coded aperture approach, an imaging system has been developed that records and reconstructs an object from an intensity distribution. This system has a signal-to-noise ratio that is proportional to the total open area of the aperture making it ideal for imaging with a limiting intensity radiation field. The main goal of this research was to develope and implement the digital methods and theory necessary for the reconstruction process. Several real-time video systems, attached to an Intellect-100 image processor, a DEC PDP-11 micro-computer, and a Convex-1 parallel processing mainframe were employed. This system, coupled with theoretical extensions and improvements, allowed for retrieval of information previously unobtainable by earlier optical methods. The effect of thermal noise, shot noise, and aperture related artifacts were examined so that new digital filtering techniques could be constructed and implemented. Results of image data filtering prior to and following the reconstruction process are reported. Improvements related to the different signal processing methods are emphasized. The application and advantages of this imaging technique to the field of non-destructive testing are also discussed

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.

The research of Digital Holographic Object Wave Field Reconstruction in Image and Object Space

Institute of Scientific and Technical Information of China (English)

LI Jun-Chang; PENG Zu-Jie; FU Yun-Chang

2011-01-01

@@ For conveniently detecting objects of different sizes using digital holography, usual measurements employ the object wave transformed by an optical system with different magnifications to fit charge coupled devices (CCDs), then the object field reconstruction involves the diffraction calculation of the optic wave passing through the optical system.We propose two methods to reconstruct the object field.The one is that, when the object is imaging in an image space in which we reconstruct the image of the object field, the object field can be expressed according to the object-image relationship.The other is that, when the object field reaching CCD is imaged in an object space in which we reconstruct the object field, the optical system is described by introducing matrix optics in this paper.The reconstruction formulae which easily use classic diffraction integral are derived.Finally, experimental verifications are also accomplished.%For conveniently detecting objects of different sizes using digital holography, usual measurements employ the object wave transformed by an optical system with different magnifications to fit charge coupled devices (CCDs), then the object Reid reconstruction involves the diffraction calculation of the optic wave passing through the optical system. We propose two methods to reconstruct the object field. The one is that, when the object is imaging in an image space in which we reconstruct the image of the object field, the object field can be expressed according to the object-image relationship. The other is that, when the object field reaching CCD is imaged in an object space in which we reconstruct the object field, the optical system is described by introducing matrix optics in this paper. The reconstruction formulae which easily use classic diffraction integral are derived. Finally, experimental verifications are also accomplished.

Optoelectronic Computer Architecture Development for Image Reconstruction