Greedy algorithms for diffuse optical tomography reconstruction

Science.gov (United States)

Dileep, B. P. V.; Das, Tapan; Dutta, Pranab K.

2018-03-01

Diffuse optical tomography (DOT) is a noninvasive imaging modality that reconstructs the optical parameters of a highly scattering medium. However, the inverse problem of DOT is ill-posed and highly nonlinear due to the zig-zag propagation of photons that diffuses through the cross section of tissue. The conventional DOT imaging methods iteratively compute the solution of forward diffusion equation solver which makes the problem computationally expensive. Also, these methods fail when the geometry is complex. Recently, the theory of compressive sensing (CS) has received considerable attention because of its efficient use in biomedical imaging applications. The objective of this paper is to solve a given DOT inverse problem by using compressive sensing framework and various Greedy algorithms such as orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP), and stagewise orthogonal matching pursuit (StOMP), regularized orthogonal matching pursuit (ROMP) and simultaneous orthogonal matching pursuit (S-OMP) have been studied to reconstruct the change in the absorption parameter i.e, Δα from the boundary data. Also, the Greedy algorithms have been validated experimentally on a paraffin wax rectangular phantom through a well designed experimental set up. We also have studied the conventional DOT methods like least square method and truncated singular value decomposition (TSVD) for comparison. One of the main features of this work is the usage of less number of source-detector pairs, which can facilitate the use of DOT in routine applications of screening. The performance metrics such as mean square error (MSE), normalized mean square error (NMSE), structural similarity index (SSIM), and peak signal to noise ratio (PSNR) have been used to evaluate the performance of the algorithms mentioned in this paper. Extensive simulation results confirm that CS based DOT reconstruction outperforms the conventional DOT imaging methods in terms of

Wind reconstruction algorithm for Viking Lander 1

Science.gov (United States)

Kynkäänniemi, Tuomas; Kemppinen, Osku; Harri, Ari-Matti; Schmidt, Walter

2017-06-01

The wind measurement sensors of Viking Lander 1 (VL1) were only fully operational for the first 45 sols of the mission. We have developed an algorithm for reconstructing the wind measurement data after the wind measurement sensor failures. The algorithm for wind reconstruction enables the processing of wind data during the complete VL1 mission. The heater element of the quadrant sensor, which provided auxiliary measurement for wind direction, failed during the 45th sol of the VL1 mission. Additionally, one of the wind sensors of VL1 broke down during sol 378. Regardless of the failures, it was still possible to reconstruct the wind measurement data, because the failed components of the sensors did not prevent the determination of the wind direction and speed, as some of the components of the wind measurement setup remained intact for the complete mission. This article concentrates on presenting the wind reconstruction algorithm and methods for validating the operation of the algorithm. The algorithm enables the reconstruction of wind measurements for the complete VL1 mission. The amount of available sols is extended from 350 to 2245 sols.

Wind reconstruction algorithm for Viking Lander 1

Directory of Open Access Journals (Sweden)

T. Kynkäänniemi

2017-06-01

Full Text Available The wind measurement sensors of Viking Lander 1 (VL1 were only fully operational for the first 45 sols of the mission. We have developed an algorithm for reconstructing the wind measurement data after the wind measurement sensor failures. The algorithm for wind reconstruction enables the processing of wind data during the complete VL1 mission. The heater element of the quadrant sensor, which provided auxiliary measurement for wind direction, failed during the 45th sol of the VL1 mission. Additionally, one of the wind sensors of VL1 broke down during sol 378. Regardless of the failures, it was still possible to reconstruct the wind measurement data, because the failed components of the sensors did not prevent the determination of the wind direction and speed, as some of the components of the wind measurement setup remained intact for the complete mission. This article concentrates on presenting the wind reconstruction algorithm and methods for validating the operation of the algorithm. The algorithm enables the reconstruction of wind measurements for the complete VL1 mission. The amount of available sols is extended from 350 to 2245 sols.

Selected event reconstruction algorithms for the CBM experiment at FAIR

International Nuclear Information System (INIS)

Lebedev, Semen; Höhne, Claudia; Lebedev, Andrey; Ososkov, Gennady

2014-01-01

Development of fast and efficient event reconstruction algorithms is an important and challenging task in the Compressed Baryonic Matter (CBM) experiment at the future FAIR facility. The event reconstruction algorithms have to process terabytes of input data produced in particle collisions. In this contribution, several event reconstruction algorithms are presented. Optimization of the algorithms in the following CBM detectors are discussed: Ring Imaging Cherenkov (RICH) detector, Transition Radiation Detectors (TRD) and Muon Chamber (MUCH). The ring reconstruction algorithm in the RICH is discussed. In TRD and MUCH track reconstruction algorithms are based on track following and Kalman Filter methods. All algorithms were significantly optimized to achieve maximum speed up and minimum memory consumption. Obtained results showed that a significant speed up factor for all algorithms was achieved and the reconstruction efficiency stays at high level.

Multichannel algorithm for fast 3D reconstruction

International Nuclear Information System (INIS)

Rodet, Thomas; Grangeat, Pierre; Desbat, Laurent

2002-01-01

Some recent medical imaging applications such as functional imaging (PET and SPECT) or interventional imaging (CT fluoroscopy) involve increasing amounts of data. In order to reduce the image reconstruction time, we develop a new fast 3D reconstruction algorithm based on a divide and conquer approach. The proposed multichannel algorithm performs an indirect frequential subband decomposition of the image f to be reconstructed (f=Σf j ) through the filtering of the projections Rf. The subband images f j are reconstructed on a downsampled grid without information suppression. In order to reduce the computation time, we do not backproject the null filtered projections and we downsample the number of projections according to the Shannon conditions associated with the subband image. Our algorithm is based on filtering and backprojection operators. Using the same algorithms for these basic operators, our approach is three and a half times faster than a classical FBP algorithm for a 2D image 512x512 and six times faster for a 3D image 32x512x512. (author)

Improved algorithm for solving nonlinear parabolized stability equations

International Nuclear Information System (INIS)

Zhao Lei; Zhang Cun-bo; Liu Jian-xin; Luo Ji-sheng

2016-01-01

Due to its high computational efficiency and ability to consider nonparallel and nonlinear effects, nonlinear parabolized stability equations (NPSE) approach has been widely used to study the stability and transition mechanisms. However, it often diverges in hypersonic boundary layers when the amplitude of disturbance reaches a certain level. In this study, an improved algorithm for solving NPSE is developed. In this algorithm, the mean flow distortion is included into the linear operator instead of into the nonlinear forcing terms in NPSE. An under-relaxation factor for computing the nonlinear terms is introduced during the iteration process to guarantee the robustness of the algorithm. Two case studies, the nonlinear development of stationary crossflow vortices and the fundamental resonance of the second mode disturbance in hypersonic boundary layers, are presented to validate the proposed algorithm for NPSE. Results from direct numerical simulation (DNS) are regarded as the baseline for comparison. Good agreement can be found between the proposed algorithm and DNS, which indicates the great potential of the proposed method on studying the crossflow and streamwise instability in hypersonic boundary layers. (paper)

Advanced reconstruction algorithms for electron tomography: From comparison to combination

Energy Technology Data Exchange (ETDEWEB)

Goris, B. [EMAT, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp (Belgium); Roelandts, T. [Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Batenburg, K.J. [Vision Lab, University of Antwerp, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Centrum Wiskunde and Informatica, Science Park 123, NL-1098XG Amsterdam (Netherlands); Heidari Mezerji, H. [EMAT, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp (Belgium); Bals, S., E-mail: sara.bals@ua.ac.be [EMAT, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp (Belgium)

2013-04-15

In this work, the simultaneous iterative reconstruction technique (SIRT), the total variation minimization (TVM) reconstruction technique and the discrete algebraic reconstruction technique (DART) for electron tomography are compared and the advantages and disadvantages are discussed. Furthermore, we describe how the result of a three dimensional (3D) reconstruction based on TVM can provide objective information that is needed as the input for a DART reconstruction. This approach results in a tomographic reconstruction of which the segmentation is carried out in an objective manner. - Highlights: ► A comparative study between different reconstruction algorithms for tomography is performed. ► Reconstruction algorithms that uses prior knowledge about the specimen have a superior result. ► One reconstruction algorithm can provide the prior knowledge for a second algorithm.

Improved algorithm for solving nonlinear parabolized stability equations

Science.gov (United States)

Zhao, Lei; Zhang, Cun-bo; Liu, Jian-xin; Luo, Ji-sheng

2016-08-01

Due to its high computational efficiency and ability to consider nonparallel and nonlinear effects, nonlinear parabolized stability equations (NPSE) approach has been widely used to study the stability and transition mechanisms. However, it often diverges in hypersonic boundary layers when the amplitude of disturbance reaches a certain level. In this study, an improved algorithm for solving NPSE is developed. In this algorithm, the mean flow distortion is included into the linear operator instead of into the nonlinear forcing terms in NPSE. An under-relaxation factor for computing the nonlinear terms is introduced during the iteration process to guarantee the robustness of the algorithm. Two case studies, the nonlinear development of stationary crossflow vortices and the fundamental resonance of the second mode disturbance in hypersonic boundary layers, are presented to validate the proposed algorithm for NPSE. Results from direct numerical simulation (DNS) are regarded as the baseline for comparison. Good agreement can be found between the proposed algorithm and DNS, which indicates the great potential of the proposed method on studying the crossflow and streamwise instability in hypersonic boundary layers. Project supported by the National Natural Science Foundation of China (Grant Nos. 11332007 and 11402167).

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

A very fast implementation of 2D iterative reconstruction algorithms

DEFF Research Database (Denmark)

Toft, Peter Aundal; Jensen, Peter James

1996-01-01

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

Research on compressive sensing reconstruction algorithm based on total variation model

Science.gov (United States)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

Nonlinear inversion of borehole-radar tomography data to reconstruct velocity and attenuation distribution in earth materials

Science.gov (United States)

Zhou, C.; Liu, L.; Lane, J.W.

2001-01-01

A nonlinear tomographic inversion method that uses first-arrival travel-time and amplitude-spectra information from cross-hole radar measurements was developed to simultaneously reconstruct electromagnetic velocity and attenuation distribution in earth materials. Inversion methods were developed to analyze single cross-hole tomography surveys and differential tomography surveys. Assuming the earth behaves as a linear system, the inversion methods do not require estimation of source radiation pattern, receiver coupling, or geometrical spreading. The data analysis and tomographic inversion algorithm were applied to synthetic test data and to cross-hole radar field data provided by the US Geological Survey (USGS). The cross-hole radar field data were acquired at the USGS fractured-rock field research site at Mirror Lake near Thornton, New Hampshire, before and after injection of a saline tracer, to monitor the transport of electrically conductive fluids in the image plane. Results from the synthetic data test demonstrate the algorithm computational efficiency and indicate that the method robustly can reconstruct electromagnetic (EM) wave velocity and attenuation distribution in earth materials. The field test results outline zones of velocity and attenuation anomalies consistent with the finding of previous investigators; however, the tomograms appear to be quite smooth. Further work is needed to effectively find the optimal smoothness criterion in applying the Tikhonov regularization in the nonlinear inversion algorithms for cross-hole radar tomography. ?? 2001 Elsevier Science B.V. All rights reserved.

A mixed-order nonlinear diffusion compressed sensing MR image reconstruction.

Science.gov (United States)

Joy, Ajin; Paul, Joseph Suresh

2018-03-07

Avoid formation of staircase artifacts in nonlinear diffusion-based MR image reconstruction without compromising computational speed. Whereas second-order diffusion encourages the evolution of pixel neighborhood with uniform intensities, fourth-order diffusion considers smooth region to be not necessarily a uniform intensity region but also a planar region. Therefore, a controlled application of fourth-order diffusivity function is used to encourage second-order diffusion to reconstruct the smooth regions of the image as a plane rather than a group of blocks, while not being strong enough to introduce the undesirable speckle effect. Proposed method is compared with second- and fourth-order nonlinear diffusion reconstruction, total variation (TV), total generalized variation, and higher degree TV using in vivo data sets for different undersampling levels with application to dictionary learning-based reconstruction. It is observed that the proposed technique preserves sharp boundaries in the image while preventing the formation of staircase artifacts in the regions of smoothly varying pixel intensities. It also shows reduced error measures compared with second-order nonlinear diffusion reconstruction or TV and converges faster than TV-based methods. Because nonlinear diffusion is known to be an effective alternative to TV for edge-preserving reconstruction, the crucial aspect of staircase artifact removal is addressed. Reconstruction is found to be stable for the experimentally determined range of fourth-order regularization parameter, and therefore not does not introduce a parameter search. Hence, the computational simplicity of second-order diffusion is retained. © 2018 International Society for Magnetic Resonance in Medicine.

Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm

International Nuclear Information System (INIS)

Chen, Xia; Hu, Hong-li; Liu, Fei; Gao, Xiang Xiang

2011-01-01

The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality

Versatility of the CFR algorithm for limited angle reconstruction

International Nuclear Information System (INIS)

Fujieda, I.; Heiskanen, K.; Perez-Mendez, V.

1990-01-01

The constrained Fourier reconstruction (CFR) algorithm and the iterative reconstruction-reprojection (IRR) algorithm are evaluated based on their accuracy for three types of limited angle reconstruction problems. The cFR algorithm performs better for problems such as Xray CT imaging of a nuclear reactor core with one large data gap due to structural blocking of the source and detector pair. For gated heart imaging by Xray CT, radioisotope distribution imaging by PET or SPECT, using a polygonal array of gamma cameras with insensitive gaps between camera boundaries, the IRR algorithm has a slight advantage over the CFR algorithm but the difference is not significant

Algorithms for non-linear M-estimation

DEFF Research Database (Denmark)

Madsen, Kaj; Edlund, O; Ekblom, H

1997-01-01

In non-linear regression, the least squares method is most often used. Since this estimator is highly sensitive to outliers in the data, alternatives have became increasingly popular during the last decades. We present algorithms for non-linear M-estimation. A trust region approach is used, where...

A new iterative algorithm to reconstruct the refractive index.

Science.gov (United States)

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

2007-06-21

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

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

Science.gov (United States)

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

2014-04-01

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

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.

Tomographic reconstruction of binary fields

International Nuclear Information System (INIS)

Roux, Stéphane; Leclerc, Hugo; Hild, François

2012-01-01

A novel algorithm is proposed for reconstructing binary images from their projection along a set of different orientations. Based on a nonlinear transformation of the projection data, classical back-projection procedures can be used iteratively to converge to the sought image. A multiscale implementation allows for a faster convergence. The algorithm is tested on images up to 1 Mb definition, and an error free reconstruction is achieved with a very limited number of projection data, saving a factor of about 100 on the number of projections required for classical reconstruction algorithms.

Duality reconstruction algorithm for use in electrical impedance tomography

International Nuclear Information System (INIS)

Abdullah, M.Z.; Dickin, F.J.

1996-01-01

A duality reconstruction algorithm for solving the inverse problem in electrical impedance tomography (EIT) is described. In this method, an algorithm based on the Geselowitz compensation (GC) theorem is used first to reconstruct an approximate version of the image. It is then fed as a first guessed data to the modified Newton-Raphson (MNR) algorithm which iteratively correct the image until a final acceptable solution is reached. The implementation of the GC and MNR based algorithms using the finite element method will be discussed. Reconstructed images produced by the algorithm will also be presented. Consideration is also given to the most computationally intensive aspects of the algorithm, namely the inversion of the large and sparse matrices. The methods taken to approximately compute the inverse ot those matrices will be outlined. (author)

Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms

Energy Technology Data Exchange (ETDEWEB)

Helama, S.; Holopainen, J.; Eronen, M. [Department of Geology, University of Helsinki, (Finland); Makarenko, N.G. [Russian Academy of Sciences, St. Petersburg (Russian Federation). Pulkovo Astronomical Observatory; Karimova, L.M.; Kruglun, O.A. [Institute of Mathematics, Almaty (Kazakhstan); Timonen, M. [Finnish Forest Research Institute, Rovaniemi Research Unit (Finland); Merilaeinen, J. [SAIMA Unit of the Savonlinna Department of Teacher Education, University of Joensuu (Finland)

2009-07-01

Tree-rings tell of past climates. To do so, tree-ring chronologies comprising numerous climate-sensitive living-tree and subfossil time-series need to be 'transferred' into palaeoclimate estimates using transfer functions. The purpose of this study is to compare different types of transfer functions, especially linear and nonlinear algorithms. Accordingly, multiple linear regression (MLR), linear scaling (LSC) and artificial neural networks (ANN, nonlinear algorithm) were compared. Transfer functions were built using a regional tree-ring chronology and instrumental temperature observations from Lapland (northern Finland and Sweden). In addition, conventional MLR was compared with a hybrid model whereby climate was reconstructed separately for short- and long-period timescales prior to combining the bands of timescales into a single hybrid model. The fidelity of the different reconstructions was validated against instrumental climate data. The reconstructions by MLR and ANN showed reliable reconstruction capabilities over the instrumental period (AD 1802-1998). LCS failed to reach reasonable verification statistics and did not qualify as a reliable reconstruction: this was due mainly to exaggeration of the low-frequency climatic variance. Over this instrumental period, the reconstructed low-frequency amplitudes of climate variability were rather similar by MLR and ANN. Notably greater differences between the models were found over the actual reconstruction period (AD 802-1801). A marked temperature decline, as reconstructed by MLR, from the Medieval Warm Period (AD 931-1180) to the Little Ice Age (AD 1601-1850), was evident in all the models. This decline was approx. 0.5 C as reconstructed by MLR. Different ANN based palaeotemperatures showed simultaneous cooling of 0.2 to 0.5 C, depending on algorithm. The hybrid MLR did not seem to provide further benefit above conventional MLR in our sample. The robustness of the conventional MLR over the calibration

Iterative concurrent reconstruction algorithms for emission computed tomography

International Nuclear Information System (INIS)

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

1994-01-01

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

Applications and algorithms for mixed integer nonlinear programming

International Nuclear Information System (INIS)

Leyffer, Sven; Munson, Todd; Linderoth, Jeff; Luedtke, James; Miller, Andrew

2009-01-01

The mathematical modeling of systems often requires the use of both nonlinear and discrete components. Discrete decision variables model dichotomies, discontinuities, and general logical relationships. Nonlinear functions are required to accurately represent physical properties such as pressure, stress, temperature, and equilibrium. Problems involving both discrete variables and nonlinear constraint functions are known as mixed-integer nonlinear programs (MINLPs) and are among the most challenging computational optimization problems faced by researchers and practitioners. In this paper, we describe relevant scientific applications that are naturally modeled as MINLPs, we provide an overview of available algorithms and software, and we describe ongoing methodological advances for solving MINLPs. These algorithmic advances are making increasingly larger instances of this important family of problems tractable.

DART: a practical reconstruction algorithm for discrete tomography.

Science.gov (United States)

Batenburg, Kees Joost; Sijbers, Jan

2011-09-01

In this paper, we present an iterative reconstruction algorithm for discrete tomography, called discrete algebraic reconstruction technique (DART). DART can be applied if the scanned object is known to consist of only a few different compositions, each corresponding to a constant gray value in the reconstruction. Prior knowledge of the gray values for each of the compositions is exploited to steer the current reconstruction towards a reconstruction that contains only these gray values. Based on experiments with both simulated CT data and experimental μCT data, it is shown that DART is capable of computing more accurate reconstructions from a small number of projection images, or from a small angular range, than alternative methods. It is also shown that DART can deal effectively with noisy projection data and that the algorithm is robust with respect to errors in the estimation of the gray values.

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

A filtered backprojection reconstruction algorithm for Compton camera

Energy Technology Data Exchange (ETDEWEB)

Lojacono, Xavier; Maxim, Voichita; Peyrin, Francoise; Prost, Remy [Lyon Univ., Villeurbanne (France). CNRS, Inserm, INSA-Lyon, CREATIS, UMR5220; Zoglauer, Andreas [California Univ., Berkeley, CA (United States). Space Sciences Lab.

2011-07-01

In this paper we present a filtered backprojection reconstruction algorithm for Compton Camera detectors of particles. Compared to iterative methods, widely used for the reconstruction of images from Compton camera data, analytical methods are fast, easy to implement and avoid convergence issues. The method we propose is exact for an idealized Compton camera composed of two parallel plates of infinite dimension. We show that it copes well with low number of detected photons simulated from a realistic device. Images reconstructed from both synthetic data and realistic ones obtained with Monte Carlo simulations demonstrate the efficiency of the algorithm. (orig.)

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

Science.gov (United States)

Duan, Jizhong; Liu, Yu; Jing, Peiguang

2018-02-01

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

A reconstruction algorithms for helical cone-beam SPECT

International Nuclear Information System (INIS)

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

1993-01-01

Cone-beam SPECT provides improved sensitivity for imaging small organs like the brain and heart. However, current cone-beam tomography with the focal point traversing a planar orbit does not acquire sufficient data to give an accurate reconstruction. In this paper, the authors employ a data-acquisition method which obtains complete data for cone-beam SPECT by simultaneously rotating the gamma camera and translating the patient bed, so that cone-beam projections can be obtained with the focal point traversing a helix surrounding the patient. An implementation of Grangeat's algorithm for helical cone-beam projections is developed. The algorithm requires a rebinning step to convert cone-beam data to parallel-beam data which are then reconstructed using the 3D Radon inversion. A fast new rebinning scheme is developed which uses all of the detected data to reconstruct the image and properly normalizes any multiply scanned data. This algorithm is shown to produce less artifacts than the commonly used Feldkamp algorithm when applied to either a circular planar orbit or a helical orbit acquisition. The algorithm can easily be extended to any arbitrary orbit

Numerical nonlinear complex geometrical optics algorithm for the 3D Calderón problem

DEFF Research Database (Denmark)

Delbary, Fabrice; Knudsen, Kim

2014-01-01

to the generalized Laplace equation. The 3D problem was solved in theory in late 1980s using complex geometrical optics solutions and a scattering transform. Several approximations to the reconstruction method have been suggested and implemented numerically in the literature, but here, for the first time, a complete...... computer implementation of the full nonlinear algorithm is given. First a boundary integral equation is solved by a Nystrom method for the traces of the complex geometrical optics solutions, second the scattering transform is computed and inverted using fast Fourier transform, and finally a boundary value...

Neural network algorithm for image reconstruction using the grid friendly projections

International Nuclear Information System (INIS)

Cierniak, R.

2011-01-01

Full text: The presented paper describes a development of original approach to the reconstruction problem using a recurrent neural network. Particularly, the 'grid-friendly' angles of performed projections are selected according to the discrete Radon transform (DRT) concept to decrease the number of projections required. The methodology of our approach is consistent with analytical reconstruction algorithms. Reconstruction problem is reformulated in our approach to optimization problem. This problem is solved in present concept using method based on the maximum likelihood methodology. The reconstruction algorithm proposed in this work is consequently adapted for more practical discrete fan beam projections. Computer simulation results show that the neural network reconstruction algorithm designed to work in this way improves obtained results and outperforms conventional methods in reconstructed image quality. (author)

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.)

Cone-beam and fan-beam image reconstruction algorithms based on spherical and circular harmonics

International Nuclear Information System (INIS)

Zeng, Gengsheng L; Gullberg, Grant T

2004-01-01

A cone-beam image reconstruction algorithm using spherical harmonic expansions is proposed. The reconstruction algorithm is in the form of a summation of inner products of two discrete arrays of spherical harmonic expansion coefficients at each cone-beam point of acquisition. This form is different from the common filtered backprojection algorithm and the direct Fourier reconstruction algorithm. There is no re-sampling of the data, and spherical harmonic expansions are used instead of Fourier expansions. As a special case, a new fan-beam image reconstruction algorithm is also derived in terms of a circular harmonic expansion. Computer simulation results for both cone-beam and fan-beam algorithms are presented for circular planar orbit acquisitions. The algorithms give accurate reconstructions; however, the implementation of the cone-beam reconstruction algorithm is computationally intensive. A relatively efficient algorithm is proposed for reconstructing the central slice of the image when a circular scanning orbit is used

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.

A fast iterative soft-thresholding algorithm for few-view CT reconstruction

Energy Technology Data Exchange (ETDEWEB)

Wu, Junfeng; Mou, Xuanqin; Zhang, Yanbo [Jiaotong Univ., Xi' an (China). Inst. of Image Processing and Pattern Recognition

2011-07-01

Iterative soft-thresholding algorithms with total variation regularization can produce high-quality reconstructions from few views and even in the presence of noise. However, these algorithms are known to converge quite slowly, with a proven theoretically global convergence rate O(1/k), where k is iteration number. In this paper, we present a fast iterative soft-thresholding algorithm for few-view fan beam CT reconstruction with a global convergence rate O(1/k{sup 2}), which is significantly faster than the iterative soft-thresholding algorithm. Simulation results demonstrate the superior performance of the proposed algorithm in terms of convergence speed and reconstruction quality. (orig.)

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.

Combined algorithms in nonlinear problems of magnetostatics

International Nuclear Information System (INIS)

Gregus, M.; Khoromskij, B.N.; Mazurkevich, G.E.; Zhidkov, E.P.

1988-01-01

To solve boundary problems of magnetostatics in unbounded two- and three-dimensional regions, we construct combined algorithms based on a combination of the method of boundary integral equations with the grid methods. We study the question of substantiation of the combined method of nonlinear magnetostatic problem without the preliminary discretization of equations and give some results on the convergence of iterative processes that arise in non-linear cases. We also discuss economical iterative processes and algorithms that solve boundary integral equations on certain surfaces. Finally, examples of numerical solutions of magnetostatic problems that arose when modelling the fields of electrophysical installations are given too. 14 refs.; 2 figs.; 1 tab

Concise quantum associative memories with nonlinear search algorithm

International Nuclear Information System (INIS)

Tchapet Njafa, J.P.; Nana Engo, S.G.

2016-01-01

The model of Quantum Associative Memories (QAM) we propose here consists in simplifying and generalizing that of Rigui Zhou et al. [1] which uses the quantum matrix with the binary decision diagram put forth by David Rosenbaum [2] and the Abrams and Lloyd's nonlinear search algorithm [3]. Our model gives the possibility to retrieve one of the sought states in multi-values retrieving scheme when a measurement is done on the first register in O(c-r) time complexity. It is better than Grover's algorithm and its modified form which need O(√((2 n )/(m))) steps when they are used as the retrieval algorithm. n is the number of qubits of the first register and m the number of x values for which f(x) = 1. As the nonlinearity makes the system highly susceptible to the noise, an analysis of the influence of the single qubit noise channels on the Nonlinear Search Algorithm of our model of QAM shows a fidelity of about 0.7 whatever the number of qubits existing in the first register, thus demonstrating the robustness of our model. (copyright 2016 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2015-01-01

A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

KAUST Repository

Desmal, Abdulla

2015-04-13

A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

A Model Predictive Algorithm for Active Control of Nonlinear Noise Processes

Directory of Open Access Journals (Sweden)

Qi-Zhi Zhang

2005-01-01

Full Text Available In this paper, an improved nonlinear Active Noise Control (ANC system is achieved by introducing an appropriate secondary source. For ANC system to be successfully implemented, the nonlinearity of the primary path and time delay of the secondary path must be overcome. A nonlinear Model Predictive Control (MPC strategy is introduced to deal with the time delay in the secondary path and the nonlinearity in the primary path of the ANC system. An overall online modeling technique is utilized for online secondary path and primary path estimation. The secondary path is estimated using an adaptive FIR filter, and the primary path is estimated using a Neural Network (NN. The two models are connected in parallel with the two paths. In this system, the mutual disturbances between the operation of the nonlinear ANC controller and modeling of the secondary can be greatly reduced. The coefficients of the adaptive FIR filter and weight vector of NN are adjusted online. Computer simulations are carried out to compare the proposed nonlinear MPC method with the nonlinear Filter-x Least Mean Square (FXLMS algorithm. The results showed that the convergence speed of the proposed nonlinear MPC algorithm is faster than that of nonlinear FXLMS algorithm. For testing the robust performance of the proposed nonlinear ANC system, the sudden changes in the secondary path and primary path of the ANC system are considered. Results indicated that the proposed nonlinear ANC system can rapidly track the sudden changes in the acoustic paths of the nonlinear ANC system, and ensure the adaptive algorithm stable when the nonlinear ANC system is time variable.

Evaluation of reconstruction algorithms in SPECT neuroimaging: Pt. 1

International Nuclear Information System (INIS)

Heejoung Kim; Zeeberg, B.R.; Reba, R.C.

1993-01-01

In the presence of statistical noise, an iterative reconstruction algorithm (IRA) for the quantitative reconstruction of single-photon-emission computed tomographic (SPECT) brain images overcomes major limitations of applying the standard filtered back projection (FBP) reconstruction algorithm to projection data which have been degraded by convolution of the true radioactivity distribution with a finite-resolution distance-dependent detector response: (a) the non-uniformity within the grey (or white) matter voxels which results even though the true model is uniform within these voxels; (b) a significantly lower ratio of grey/white matter voxel values than in the true model; and (c) an inability to detect an altered radioactivity value within the grey (or white) matter voxels. It is normally expected that an algorithm which improves spatial resolution and quantitative accuracy might also increase the magnitude of the statistical noise in the reconstructed image. However, the noise properties in the IRA images are very similar to those in the FBP images. (Author)

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.

Reconstruction algorithm in compressed sensing based on maximum a posteriori estimation

International Nuclear Information System (INIS)

Takeda, Koujin; Kabashima, Yoshiyuki

2013-01-01

We propose a systematic method for constructing a sparse data reconstruction algorithm in compressed sensing at a relatively low computational cost for general observation matrix. It is known that the cost of ℓ 1 -norm minimization using a standard linear programming algorithm is O(N 3 ). We show that this cost can be reduced to O(N 2 ) by applying the approach of posterior maximization. Furthermore, in principle, the algorithm from our approach is expected to achieve the widest successful reconstruction region, which is evaluated from theoretical argument. We also discuss the relation between the belief propagation-based reconstruction algorithm introduced in preceding works and our approach

First results of genetic algorithm application in ML image reconstruction in emission tomography

International Nuclear Information System (INIS)

Smolik, W.

1999-01-01

This paper concerns application of genetic algorithm in maximum likelihood image reconstruction in emission tomography. The example of genetic algorithm for image reconstruction is presented. The genetic algorithm was based on the typical genetic scheme modified due to the nature of solved problem. The convergence of algorithm was examined. The different adaption functions, selection and crossover methods were verified. The algorithm was tested on simulated SPECT data. The obtained results of image reconstruction are discussed. (author)

Motion Cueing Algorithm Development: Human-Centered Linear and Nonlinear Approaches

Science.gov (United States)

Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.

2005-01-01

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. Prior research identified viable features from two algorithms: the nonlinear "adaptive algorithm", and the "optimal algorithm" that incorporates human vestibular models. A novel approach to motion cueing, the "nonlinear algorithm" is introduced that combines features from both approaches. This algorithm is formulated by optimal control, and incorporates a new integrated perception model that includes both visual and vestibular sensation and the interaction between the stimuli. Using a time-varying control law, the matrix Riccati equation is updated in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. The neurocomputing approach was crucial in that the number of presentations of an input vector could be reduced to meet the real time requirement without degrading the quality of the motion cues.

Quantitative tomography simulations and reconstruction algorithms

International Nuclear Information System (INIS)

Martz, H.E.; Aufderheide, M.B.; Goodman, D.; Schach von Wittenau, A.; Logan, C.; Hall, J.; Jackson, J.; Slone, D.

2000-01-01

X-ray, neutron and proton transmission radiography and computed tomography (CT) are important diagnostic tools that are at the heart of LLNL's effort to meet the goals of the DOE's Advanced Radiography Campaign. This campaign seeks to improve radiographic simulation and analysis so that radiography can be a useful quantitative diagnostic tool for stockpile stewardship. Current radiographic accuracy does not allow satisfactory separation of experimental effects from the true features of an object's tomographically reconstructed image. This can lead to difficult and sometimes incorrect interpretation of the results. By improving our ability to simulate the whole radiographic and CT system, it will be possible to examine the contribution of system components to various experimental effects, with the goal of removing or reducing them. In this project, we are merging this simulation capability with a maximum-likelihood (constrained-conjugate-gradient-CCG) reconstruction technique yielding a physics-based, forward-model image-reconstruction code. In addition, we seek to improve the accuracy of computed tomography from transmission radiographs by studying what physics is needed in the forward model. During FY 2000, an improved version of the LLNL ray-tracing code called HADES has been coupled with a recently developed LLNL CT algorithm known as CCG. The problem of image reconstruction is expressed as a large matrix equation relating a model for the object being reconstructed to its projections (radiographs). Using a constrained-conjugate-gradient search algorithm, a maximum likelihood solution is sought. This search continues until the difference between the input measured radiographs or projections and the simulated or calculated projections is satisfactorily small

Inverse Monte Carlo: a unified reconstruction algorithm for SPECT

International Nuclear Information System (INIS)

Floyd, C.E.; Coleman, R.E.; Jaszczak, R.J.

1985-01-01

Inverse Monte Carlo (IMOC) is presented as a unified reconstruction algorithm for Emission Computed Tomography (ECT) providing simultaneous compensation for scatter, attenuation, and the variation of collimator resolution with depth. The technique of inverse Monte Carlo is used to find an inverse solution to the photon transport equation (an integral equation for photon flux from a specified source) for a parameterized source and specific boundary conditions. The system of linear equations so formed is solved to yield the source activity distribution for a set of acquired projections. For the studies presented here, the equations are solved using the EM (Maximum Likelihood) algorithm although other solution algorithms, such as Least Squares, could be employed. While the present results specifically consider the reconstruction of camera-based Single Photon Emission Computed Tomographic (SPECT) images, the technique is equally valid for Positron Emission Tomography (PET) if a Monte Carlo model of such a system is used. As a preliminary evaluation, experimentally acquired SPECT phantom studies for imaging Tc-99m (140 keV) are presented which demonstrate the quantitative compensation for scatter and attenuation for a two dimensional (single slice) reconstruction. The algorithm may be expanded in a straight forward manner to full three dimensional reconstruction including compensation for out of plane scatter

Performances of new reconstruction algorithms for CT-TDLAS (computer tomography-tunable diode laser absorption spectroscopy)

International Nuclear Information System (INIS)

Jeon, Min-Gyu; Deguchi, Yoshihiro; Kamimoto, Takahiro; Doh, Deog-Hee; Cho, Gyeong-Rae

2017-01-01

Highlights: • The measured data were successfully used for generating absorption spectra. • Four different reconstruction algorithms, ART, MART, SART and SMART were evaluated. • The calculation speed of convergence by the SMART algorithm was the fastest. • SMART was the most reliable algorithm for reconstructing the multiple signals. - Abstract: Recent advent of the tunable lasers made to measure simultaneous temperature and concentration fields of the gases. CT-TDLAS (computed tomography-tunable diode laser absorption spectroscopy) is one the leading techniques for the measurements of temperature and concentration fields of the gases. In CT-TDLAS, the accuracies of the measurement results are strongly dependent upon the reconstruction algorithms. In this study, four different reconstruction algorithms have been tested numerically using experimental data sets measured by thermocouples for combustion fields. Three reconstruction algorithms, MART (multiplicative algebraic reconstruction technique) algorithm, SART (simultaneous algebraic reconstruction technique) algorithm and SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm, are newly proposed for CT-TDLAS in this study. The calculation results obtained by the three algorithms have been compared with previous algorithm, ART (algebraic reconstruction technique) algorithm. Phantom data sets have been generated by the use of thermocouples data obtained in an actual experiment. The data of the Harvard HITRAN table in which the thermo-dynamical properties and the light spectrum of the H_2O are listed were used for the numerical test. The reconstructed temperature and concentration fields were compared with the original HITRAN data, through which the constructed methods are validated. The performances of the four reconstruction algorithms were demonstrated. This method is expected to enhance the practicality of CT-TDLAS.

An Approximate Cone Beam Reconstruction Algorithm for Gantry-Tilted CT Using Tangential Filtering

Directory of Open Access Journals (Sweden)

Ming Yan

2006-01-01

Full Text Available FDK algorithm is a well-known 3D (three-dimensional approximate algorithm for CT (computed tomography image reconstruction and is also known to suffer from considerable artifacts when the scanning cone angle is large. Recently, it has been improved by performing the ramp filtering along the tangential direction of the X-ray source helix for dealing with the large cone angle problem. In this paper, we present an FDK-type approximate reconstruction algorithm for gantry-tilted CT imaging. The proposed method improves the image reconstruction by filtering the projection data along a proper direction which is determined by CT parameters and gantry-tilted angle. As a result, the proposed algorithm for gantry-tilted CT reconstruction can provide more scanning flexibilities in clinical CT scanning and is efficient in computation. The performance of the proposed algorithm is evaluated with turbell clock phantom and thorax phantom and compared with FDK algorithm and a popular 2D (two-dimensional approximate algorithm. The results show that the proposed algorithm can achieve better image quality for gantry-tilted CT image reconstruction.

Volume reconstruction optimization for tomo-PIV algorithms applied to experimental data

Science.gov (United States)

Martins, Fabio J. W. A.; Foucaut, Jean-Marc; Thomas, Lionel; Azevedo, Luis F. A.; Stanislas, Michel

2015-08-01

Tomographic PIV is a three-component volumetric velocity measurement technique based on the tomographic reconstruction of a particle distribution imaged by multiple camera views. In essence, the performance and accuracy of this technique is highly dependent on the parametric adjustment and the reconstruction algorithm used. Although synthetic data have been widely employed to optimize experiments, the resulting reconstructed volumes might not have optimal quality. The purpose of the present study is to offer quality indicators that can be applied to data samples in order to improve the quality of velocity results obtained by the tomo-PIV technique. The methodology proposed can potentially lead to significantly reduction in the time required to optimize a tomo-PIV reconstruction, also leading to better quality velocity results. Tomo-PIV data provided by a six-camera turbulent boundary-layer experiment were used to optimize the reconstruction algorithms according to this methodology. Velocity statistics measurements obtained by optimized BIMART, SMART and MART algorithms were compared with hot-wire anemometer data and velocity measurement uncertainties were computed. Results indicated that BIMART and SMART algorithms produced reconstructed volumes with equivalent quality as the standard MART with the benefit of reduced computational time.

Volume reconstruction optimization for tomo-PIV algorithms applied to experimental data

International Nuclear Information System (INIS)

Martins, Fabio J W A; Foucaut, Jean-Marc; Stanislas, Michel; Thomas, Lionel; Azevedo, Luis F A

2015-01-01

Tomographic PIV is a three-component volumetric velocity measurement technique based on the tomographic reconstruction of a particle distribution imaged by multiple camera views. In essence, the performance and accuracy of this technique is highly dependent on the parametric adjustment and the reconstruction algorithm used. Although synthetic data have been widely employed to optimize experiments, the resulting reconstructed volumes might not have optimal quality. The purpose of the present study is to offer quality indicators that can be applied to data samples in order to improve the quality of velocity results obtained by the tomo-PIV technique. The methodology proposed can potentially lead to significantly reduction in the time required to optimize a tomo-PIV reconstruction, also leading to better quality velocity results. Tomo-PIV data provided by a six-camera turbulent boundary-layer experiment were used to optimize the reconstruction algorithms according to this methodology. Velocity statistics measurements obtained by optimized BIMART, SMART and MART algorithms were compared with hot-wire anemometer data and velocity measurement uncertainties were computed. Results indicated that BIMART and SMART algorithms produced reconstructed volumes with equivalent quality as the standard MART with the benefit of reduced computational time. (paper)

Accurate 3D reconstruction by a new PDS-OSEM algorithm for HRRT

International Nuclear Information System (INIS)

Chen, Tai-Been; Horng-Shing Lu, Henry; Kim, Hang-Keun; Son, Young-Don; Cho, Zang- Hee

2014-01-01

State-of-the-art high resolution research tomography (HRRT) provides high resolution PET images with full 3D human brain scanning. But, a short time frame in dynamic study causes many problems related to the low counts in the acquired data. The PDS-OSEM algorithm was proposed to reconstruct the HRRT image with a high signal-to-noise ratio that provides accurate information for dynamic data. The new algorithm was evaluated by simulated image, empirical phantoms, and real human brain data. Meanwhile, the time activity curve was adopted to validate a reconstructed performance of dynamic data between PDS-OSEM and OP-OSEM algorithms. According to simulated and empirical studies, the PDS-OSEM algorithm reconstructs images with higher quality, higher accuracy, less noise, and less average sum of square error than those of OP-OSEM. The presented algorithm is useful to provide quality images under the condition of low count rates in dynamic studies with a short scan time. - Highlights: • The PDS-OSEM reconstructs PET images with iteratively compensating random and scatter corrections from prompt sinogram. • The PDS-OSEM can reconstruct PET images with low count data and data contaminations. • The PDS-OSEM provides less noise and higher quality of reconstructed images than those of OP-OSEM algorithm in statistical sense

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

International Nuclear Information System (INIS)

Tang Jie; Nett, Brian E; Chen Guanghong

2009-01-01

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

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.

Preconditioned alternating projection algorithms for maximum a posteriori ECT reconstruction

International Nuclear Information System (INIS)

Krol, Andrzej; Li, Si; Shen, Lixin; Xu, Yuesheng

2012-01-01

We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constraint involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the PAPA. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality. (paper)

Parametric boundary reconstruction algorithm for industrial CT metrology application.

Science.gov (United States)

Yin, Zhye; Khare, Kedar; De Man, Bruno

2009-01-01

High-energy X-ray computed tomography (CT) systems have been recently used to produce high-resolution images in various nondestructive testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional information extracted from CT images is rapidly approaching the accuracy achieved with a coordinate measuring machine (CMM), the conventional approach to acquire the metrology information directly. On the other hand, CT systems generate the sinogram which is transformed mathematically to the pixel-based images. The dimensional information of the scanned object is extracted later by performing edge detection on reconstructed CT images. The dimensional accuracy of this approach is limited by the grid size of the pixel-based representation of CT images since the edge detection is performed on the pixel grid. Moreover, reconstructed CT images usually display various artifacts due to the underlying physical process and resulting object boundaries from the edge detection fail to represent the true boundaries of the scanned object. In this paper, a novel algorithm to reconstruct the boundaries of an object with uniform material composition and uniform density is presented. There are three major benefits in the proposed approach. First, since the boundary parameters are reconstructed instead of image pixels, the complexity of the reconstruction algorithm is significantly reduced. The iterative approach, which can be computationally intensive, will be practical with the parametric boundary reconstruction. Second, the object of interest in metrology can be represented more directly and accurately by the boundary parameters instead of the image pixels. By eliminating the extra edge detection step, the overall dimensional accuracy and process time can be improved. Third, since the parametric reconstruction approach shares the boundary representation with other conventional metrology modalities such as CMM, boundary information from other modalities can be directly

Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.

Science.gov (United States)

Chen, Chi-Kan

2017-07-26

-step algorithms can potentially incorporate with different nonlinear differential equation models to reconstruct the GRN.

Search for 'Little Higgs' and reconstruction algorithms developments in Atlas

International Nuclear Information System (INIS)

Rousseau, D.

2007-05-01

This document summarizes developments of framework and reconstruction algorithms for the ATLAS detector at the LHC. A library of reconstruction algorithms has been developed in a more and more complex environment. The reconstruction software originally designed on an optimistic Monte-Carlo simulation, has been confronted with a more detailed 'as-built' simulation. The 'Little Higgs' is an effective theory which can be taken for granted, or as an opportunity to study heavy resonances. In several cases, these resonances can be detected in original channels like tZ, ZH or WH. (author)

Research on Image Reconstruction Algorithms for Tuber Electrical Resistance Tomography System

Directory of Open Access Journals (Sweden)

Jiang Zili

2016-01-01

Full Text Available The application of electrical resistance tomography (ERT technology has been expanded to the field of agriculture, and the concept of TERT (Tuber Electrical Resistance Tomography is proposed. On the basis of the research on the forward and the inverse problems of the TERT system, a hybrid algorithm based on genetic algorithm is proposed, which can be used in TERT system to monitor the growth status of the plant tubers. The image reconstruction of TERT system is different from the conventional ERT system for two phase-flow measurement. Imaging of TERT needs more precision measurement and the conventional ERT cares more about the image reconstruction speed. A variety of algorithms are analyzed and optimized for the purpose of making them suitable for TERT system. For example: linear back projection, modified Newton-Raphson and genetic algorithm. Experimental results showed that the novel hybrid algorithm is superior to other algorithm and it can effectively improve the image reconstruction quality.

On-line reconstruction algorithms for the CBM and ALICE experiments

International Nuclear Information System (INIS)

Gorbunov, Sergey

2013-01-01

This thesis presents various algorithms which have been developed for on-line event reconstruction in the CBM experiment at GSI, Darmstadt and the ALICE experiment at CERN, Geneve. Despite the fact that the experiments are different - CBM is a fixed target experiment with forward geometry, while ALICE has a typical collider geometry - they share common aspects when reconstruction is concerned. The thesis describes: - general modifications to the Kalman filter method, which allows one to accelerate, to improve, and to simplify existing fit algorithms; - developed algorithms for track fit in CBM and ALICE experiment, including a new method for track extrapolation in non-homogeneous magnetic field. - developed algorithms for primary and secondary vertex fit in the both experiments. In particular, a new method of reconstruction of decayed particles is presented. - developed parallel algorithm for the on-line tracking in the CBM experiment. - developed parallel algorithm for the on-line tracking in High Level Trigger of the ALICE experiment. - the realisation of the track finders on modern hardware, such as SIMD CPU registers and GPU accelerators. All the presented methods have been developed by or with the direct participation of the author.

A combination-weighted Feldkamp-based reconstruction algorithm for cone-beam CT

International Nuclear Information System (INIS)

Mori, Shinichiro; Endo, Masahiro; Komatsu, Shuhei; Kandatsu, Susumu; Yashiro, Tomoyasu; Baba, Masayuki

2006-01-01

The combination-weighted Feldkamp algorithm (CW-FDK) was developed and tested in a phantom in order to reduce cone-beam artefacts and enhance cranio-caudal reconstruction coverage in an attempt to improve image quality when utilizing cone-beam computed tomography (CBCT). Using a 256-slice cone-beam CT (256CBCT), image quality (CT-number uniformity and geometrical accuracy) was quantitatively evaluated in phantom and clinical studies, and the results were compared to those obtained with the original Feldkamp algorithm. A clinical study was done in lung cancer patients under breath holding and free breathing. Image quality for the original Feldkamp algorithm is degraded at the edge of the scan region due to the missing volume, commensurate with the cranio-caudal distance between the reconstruction and central planes. The CW-FDK extended the reconstruction coverage to equal the scan coverage and improved reconstruction accuracy, unaffected by the cranio-caudal distance. The extended reconstruction coverage with good image quality provided by the CW-FDK will be clinically investigated for improving diagnostic and radiotherapy applications. In addition, this algorithm can also be adapted for use in relatively wide cone-angle CBCT such as with a flat-panel detector CBCT

Beard reconstruction: A surgical algorithm.

Science.gov (United States)

Ninkovic, M; Heidekrueger, P I; Ehrl, D; von Spiegel, F; Broer, P N

2016-06-01

Facial defects with loss of hair-bearing regions can be caused by trauma, infection, tumor excision, or burn injury. The presented analysis evaluates a series of different surgical approaches with a focus on male beard reconstruction, emphasizing the role of tissue expansion of regional and free flaps. Locoregional and free flap reconstructions were performed in 11 male patients with 14 facial defects affecting the hair-bearing bucco-mandibular or perioral region. In order to minimize donor-site morbidity and obtain large amounts of thin, pliable, hair-bearing tissue, pre-expansion was performed in five of 14 patients. Eight of 14 patients were treated with locoregional flap reconstructions and six with free flap reconstructions. Algorithms regarding pre- and intraoperative decision making are discussed and long-term (mean follow-up 1.5 years) results analyzed. Major complications, including tissue expander infection with the need for removal or exchange, partial or full flap loss, occurred in 0% (0/8) of patients with locoregional flaps and in 17% (1/6) of patients undergoing free flap reconstructions. Secondary refinement surgery was performed in 25% (2/8) of locoregional flaps and in 67% (4/6) of free flaps. Both locoregional and distant tissue transfers play a role in beard reconstruction, while pre-expansion remains an invaluable tool. Paying attention to the presented principles and considering the significance of aesthetic facial subunits, range of motion, aesthetics, and patient satisfaction were improved long term in all our patients while minimizing donor-site morbidity. Copyright © 2016 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM

Institute of Scientific and Technical Information of China (English)

ZHANGQin; TAOBen-zao; ZHAOChao-ying; WANGLi

2005-01-01

Because of the ignored items after linearization, the extended Kalman filter (EKF) becomes a form of suboptimal gradient descent algorithm. The emanative tendency exists in GPS solution when the filter equations are ill-posed. The deviation in the estimation cannot be avoided. Furthermore, the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions. To solve the above problems in GPS dynamic positioning by using EKF, a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American. The method separates the spatial parts from temporal parts during processing the GPS filter problems, and solves the nonlinear GPS dynamic positioning, thus getting stable and reliable dynamic positioning solutions.

A subzone reconstruction algorithm for efficient staggered compatible remapping

Energy Technology Data Exchange (ETDEWEB)

Starinshak, D.P., E-mail: starinshak1@llnl.gov; Owen, J.M., E-mail: mikeowen@llnl.gov

2015-09-01

Staggered-grid Lagrangian hydrodynamics algorithms frequently make use of subzonal discretization of state variables for the purposes of improved numerical accuracy, generality to unstructured meshes, and exact conservation of mass, momentum, and energy. For Arbitrary Lagrangian–Eulerian (ALE) methods using a geometric overlay, it is difficult to remap subzonal variables in an accurate and efficient manner due to the number of subzone–subzone intersections that must be computed. This becomes prohibitive in the case of 3D, unstructured, polyhedral meshes. A new procedure is outlined in this paper to avoid direct subzonal remapping. The new algorithm reconstructs the spatial profile of a subzonal variable using remapped zonal and nodal representations of the data. The reconstruction procedure is cast as an under-constrained optimization problem. Enforcing conservation at each zone and node on the remapped mesh provides the set of equality constraints; the objective function corresponds to a quadratic variation per subzone between the values to be reconstructed and a set of target reference values. Numerical results for various pure-remapping and hydrodynamics tests are provided. Ideas for extending the algorithm to staggered-grid radiation-hydrodynamics are discussed as well as ideas for generalizing the algorithm to include inequality constraints.

A three-dimensional reconstruction algorithm for an inverse-geometry volumetric CT system

International Nuclear Information System (INIS)

Schmidt, Taly Gilat; Fahrig, Rebecca; Pelc, Norbert J.

2005-01-01

An inverse-geometry volumetric computed tomography (IGCT) system has been proposed capable of rapidly acquiring sufficient data to reconstruct a thick volume in one circular scan. The system uses a large-area scanned source opposite a smaller detector. The source and detector have the same extent in the axial, or slice, direction, thus providing sufficient volumetric sampling and avoiding cone-beam artifacts. This paper describes a reconstruction algorithm for the IGCT system. The algorithm first rebins the acquired data into two-dimensional (2D) parallel-ray projections at multiple tilt and azimuthal angles, followed by a 3D filtered backprojection. The rebinning step is performed by gridding the data onto a Cartesian grid in a 4D projection space. We present a new method for correcting the gridding error caused by the finite and asymmetric sampling in the neighborhood of each output grid point in the projection space. The reconstruction algorithm was implemented and tested on simulated IGCT data. Results show that the gridding correction reduces the gridding errors to below one Hounsfield unit. With this correction, the reconstruction algorithm does not introduce significant artifacts or blurring when compared to images reconstructed from simulated 2D parallel-ray projections. We also present an investigation of the noise behavior of the method which verifies that the proposed reconstruction algorithm utilizes cross-plane rays as efficiently as in-plane rays and can provide noise comparable to an in-plane parallel-ray geometry for the same number of photons. Simulations of a resolution test pattern and the modulation transfer function demonstrate that the IGCT system, using the proposed algorithm, is capable of 0.4 mm isotropic resolution. The successful implementation of the reconstruction algorithm is an important step in establishing feasibility of the IGCT system

Quantitatively assessed CT imaging measures of pulmonary interstitial pneumonia: Effects of reconstruction algorithms on histogram parameters

International Nuclear Information System (INIS)

Koyama, Hisanobu; Ohno, Yoshiharu; Yamazaki, Youichi; Nogami, Munenobu; Kusaka, Akiko; Murase, Kenya; Sugimura, Kazuro

2010-01-01

This study aimed the influences of reconstruction algorithm for quantitative assessments in interstitial pneumonia patients. A total of 25 collagen vascular disease patients (nine male patients and 16 female patients; mean age, 57.2 years; age range 32-77 years) underwent thin-section MDCT examinations, and MDCT data were reconstructed with three kinds of reconstruction algorithm (two high-frequencies [A and B] and one standard [C]). In reconstruction algorithm B, the effect of low- and middle-frequency space was suppressed compared with reconstruction algorithm A. As quantitative CT parameters, kurtosis, skewness, and mean lung density (MLD) were acquired from a frequency histogram of the whole lung parenchyma in each reconstruction algorithm. To determine the difference of quantitative CT parameters affected by reconstruction algorithms, these parameters were compared statistically. To determine the relationships with the disease severity, these parameters were correlated with PFTs. In the results, all the histogram parameters values had significant differences each other (p < 0.0001) and those of reconstruction algorithm C were the highest. All MLDs had fair or moderate correlation with all parameters of PFT (-0.64 < r < -0.45, p < 0.05). Though kurtosis and skewness in high-frequency reconstruction algorithm A had significant correlations with all parameters of PFT (-0.61 < r < -0.45, p < 0.05), there were significant correlations only with diffusing capacity of carbon monoxide (DLco) and total lung capacity (TLC) in reconstruction algorithm C and with forced expiratory volume in 1 s (FEV1), DLco and TLC in reconstruction algorithm B. In conclusion, reconstruction algorithm has influence to quantitative assessments on chest thin-section MDCT examination in interstitial pneumonia patients.

Quantitatively assessed CT imaging measures of pulmonary interstitial pneumonia: Effects of reconstruction algorithms on histogram parameters

Energy Technology Data Exchange (ETDEWEB)

Koyama, Hisanobu [Department of Radiology, Hyogo Kaibara Hospital, 5208-1 Kaibara, Kaibara-cho, Tanba 669-3395 (Japan)], E-mail: hisanobu19760104@yahoo.co.jp; Ohno, Yoshiharu [Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe 650-0017 (Japan)], E-mail: yosirad@kobe-u.ac.jp; Yamazaki, Youichi [Department of Medical Physics and Engineering, Faculty of Health Sciences, Graduate School of Medicine, Osaka University, 1-7 Yamadaoka, Suita 565-0871 (Japan)], E-mail: y.yamazk@sahs.med.osaka-u.ac.jp; Nogami, Munenobu [Division of PET, Institute of Biomedical Research and Innovation, 2-2 MInamimachi, Minatojima, Chu0-ku, Kobe 650-0047 (Japan)], E-mail: aznogami@fbri.org; Kusaka, Akiko [Division of Radiology, Kobe University Hospital, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe 650-0017 (Japan)], E-mail: a.kusaka@hosp.kobe-u.ac.jp; Murase, Kenya [Department of Medical Physics and Engineering, Faculty of Health Sciences, Graduate School of Medicine, Osaka University, 1-7 Yamadaoka, Suita 565-0871 (Japan)], E-mail: murase@sahs.med.osaka-u.ac.jp; Sugimura, Kazuro [Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe 650-0017 (Japan)], E-mail: sugimura@med.kobe-u.ac.jp

2010-04-15

This study aimed the influences of reconstruction algorithm for quantitative assessments in interstitial pneumonia patients. A total of 25 collagen vascular disease patients (nine male patients and 16 female patients; mean age, 57.2 years; age range 32-77 years) underwent thin-section MDCT examinations, and MDCT data were reconstructed with three kinds of reconstruction algorithm (two high-frequencies [A and B] and one standard [C]). In reconstruction algorithm B, the effect of low- and middle-frequency space was suppressed compared with reconstruction algorithm A. As quantitative CT parameters, kurtosis, skewness, and mean lung density (MLD) were acquired from a frequency histogram of the whole lung parenchyma in each reconstruction algorithm. To determine the difference of quantitative CT parameters affected by reconstruction algorithms, these parameters were compared statistically. To determine the relationships with the disease severity, these parameters were correlated with PFTs. In the results, all the histogram parameters values had significant differences each other (p < 0.0001) and those of reconstruction algorithm C were the highest. All MLDs had fair or moderate correlation with all parameters of PFT (-0.64 < r < -0.45, p < 0.05). Though kurtosis and skewness in high-frequency reconstruction algorithm A had significant correlations with all parameters of PFT (-0.61 < r < -0.45, p < 0.05), there were significant correlations only with diffusing capacity of carbon monoxide (DLco) and total lung capacity (TLC) in reconstruction algorithm C and with forced expiratory volume in 1 s (FEV1), DLco and TLC in reconstruction algorithm B. In conclusion, reconstruction algorithm has influence to quantitative assessments on chest thin-section MDCT examination in interstitial pneumonia patients.

Performance of the ATLAS primary vertex reconstruction algorithms

CERN Document Server

Zhang, Matt

2017-01-01

The reconstruction of primary vertices in the busy, high pile up environment of the LHC is a challenging task. The challenges and novel methods developed by the ATLAS experiment to reconstruct vertices in such environments will be presented. Such advances in vertex seeding include methods taken from medical imagining, which allow for reconstruction of very nearby vertices will be highlighted. The performance of the current vertexing algorithms using early Run-2 data will be presented and compared to results from simulation.

Fermat's principle and nonlinear traveltime tomography

International Nuclear Information System (INIS)

Berryman, J.G.; Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, New York 10012)

1989-01-01

Fermat's principle shows that a definite convex set of feasible slowness models, depending only on the traveltime data, exists for the fully nonlinear traveltime inversion problem. In a new iterative reconstruction algorithm, the minimum number of nonfeasible ray paths is used as a figure of merit to determine the optimum size of the model correction at each step. The numerical results show that the new algorithm is robust, stable, and produces very good reconstructions even for high contrast materials where standard methods tend to diverge

A maximum-likelihood reconstruction algorithm for tomographic gamma-ray nondestructive assay

International Nuclear Information System (INIS)

Prettyman, T.H.; Estep, R.J.; Cole, R.A.; Sheppard, G.A.

1994-01-01

A new tomographic reconstruction algorithm for nondestructive assay with high resolution gamma-ray spectroscopy (HRGS) is presented. The reconstruction problem is formulated using a maximum-likelihood approach in which the statistical structure of both the gross and continuum measurements used to determine the full-energy response in HRGS is precisely modeled. An accelerated expectation-maximization algorithm is used to determine the optimal solution. The algorithm is applied to safeguards and environmental assays of large samples (for example, 55-gal. drums) in which high continuum levels caused by Compton scattering are routinely encountered. Details of the implementation of the algorithm and a comparative study of the algorithm's performance are presented

New preconditioned conjugate gradient algorithms for nonlinear unconstrained optimization problems

International Nuclear Information System (INIS)

Al-Bayati, A.; Al-Asadi, N.

1997-01-01

This paper presents two new predilection conjugate gradient algorithms for nonlinear unconstrained optimization problems and examines their computational performance. Computational experience shows that the new proposed algorithms generally imp lone the efficiency of Nazareth's [13] preconditioned conjugate gradient algorithm. (authors). 16 refs., 1 tab

Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm

Directory of Open Access Journals (Sweden)

V. Rajinikanth

2012-01-01

Full Text Available An enhanced bacteria foraging optimization (EBFO algorithm-based Proportional + integral + derivative (PID controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.

Parallel MR image reconstruction using augmented Lagrangian methods.

Science.gov (United States)

Ramani, Sathish; Fessler, Jeffrey A

2011-03-01

Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE) requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity-based regularization criteria can improve image quality, but they demand computation-intensive nonlinear optimization. In this paper, we present novel methods for regularized MRI reconstruction from undersampled sensitivity encoded data--SENSE-reconstruction--using the augmented Lagrangian (AL) framework for solving large-scale constrained optimization problems. We first formulate regularized SENSE-reconstruction as an unconstrained optimization task and then convert it to a set of (equivalent) constrained problems using variable splitting. We then attack these constrained versions in an AL framework using an alternating minimization method, leading to algorithms that can be implemented easily. The proposed methods are applicable to a general class of regularizers that includes popular edge-preserving (e.g., total-variation) and sparsity-promoting (e.g., l(1)-norm of wavelet coefficients) criteria and combinations thereof. Numerical experiments with synthetic and in vivo human data illustrate that the proposed AL algorithms converge faster than both general-purpose optimization algorithms such as nonlinear conjugate gradient (NCG) and state-of-the-art MFISTA.

Nonlinear model predictive control theory and algorithms

CERN Document Server

Grüne, Lars

2017-01-01

This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

Spectral CT metal artifact reduction with an optimization-based reconstruction algorithm

Science.gov (United States)

Gilat Schmidt, Taly; Barber, Rina F.; Sidky, Emil Y.

2017-03-01

Metal objects cause artifacts in computed tomography (CT) images. This work investigated the feasibility of a spectral CT method to reduce metal artifacts. Spectral CT acquisition combined with optimization-based reconstruction is proposed to reduce artifacts by modeling the physical effects that cause metal artifacts and by providing the flexibility to selectively remove corrupted spectral measurements in the spectral-sinogram space. The proposed Constrained `One-Step' Spectral CT Image Reconstruction (cOSSCIR) algorithm directly estimates the basis material maps while enforcing convex constraints. The incorporation of constraints on the reconstructed basis material maps is expected to mitigate undersampling effects that occur when corrupted data is excluded from reconstruction. The feasibility of the cOSSCIR algorithm to reduce metal artifacts was investigated through simulations of a pelvis phantom. The cOSSCIR algorithm was investigated with and without the use of a third basis material representing metal. The effects of excluding data corrupted by metal were also investigated. The results demonstrated that the proposed cOSSCIR algorithm reduced metal artifacts and improved CT number accuracy. For example, CT number error in a bright shading artifact region was reduced from 403 HU in the reference filtered backprojection reconstruction to 33 HU using the proposed algorithm in simulation. In the dark shading regions, the error was reduced from 1141 HU to 25 HU. Of the investigated approaches, decomposing the data into three basis material maps and excluding the corrupted data demonstrated the greatest reduction in metal artifacts.

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.

Fast half-sibling population reconstruction: theory and algorithms.

Science.gov (United States)

Dexter, Daniel; Brown, Daniel G

2013-07-12

Kinship inference is the task of identifying genealogically related individuals. Kinship information is important for determining mating structures, notably in endangered populations. Although many solutions exist for reconstructing full sibling relationships, few exist for half-siblings. We consider the problem of determining whether a proposed half-sibling population reconstruction is valid under Mendelian inheritance assumptions. We show that this problem is NP-complete and provide a 0/1 integer program that identifies the minimum number of individuals that must be removed from a population in order for the reconstruction to become valid. We also present SibJoin, a heuristic-based clustering approach based on Mendelian genetics, which is strikingly fast. The software is available at http://github.com/ddexter/SibJoin.git+. Our SibJoin algorithm is reasonably accurate and thousands of times faster than existing algorithms. The heuristic is used to infer a half-sibling structure for a population which was, until recently, too large to evaluate.

Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

Directory of Open Access Journals (Sweden)

Christian L Barrett

2006-05-01

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

Noise propagation in iterative reconstruction algorithms with line searches

International Nuclear Information System (INIS)

Qi, Jinyi

2003-01-01

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

Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

Science.gov (United States)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

A nonlinear filtering algorithm for denoising HR(S)TEM micrographs

International Nuclear Information System (INIS)

Du, Hongchu

2015-01-01

Noise reduction of micrographs is often an essential task in high resolution (scanning) transmission electron microscopy (HR(S)TEM) either for a higher visual quality or for a more accurate quantification. Since HR(S)TEM studies are often aimed at resolving periodic atomistic columns and their non-periodic deviation at defects, it is important to develop a noise reduction algorithm that can simultaneously handle both periodic and non-periodic features properly. In this work, a nonlinear filtering algorithm is developed based on widely used techniques of low-pass filter and Wiener filter, which can efficiently reduce noise without noticeable artifacts even in HR(S)TEM micrographs with contrast of variation of background and defects. The developed nonlinear filtering algorithm is particularly suitable for quantitative electron microscopy, and is also of great interest for beam sensitive samples, in situ analyses, and atomic resolution EFTEM. - Highlights: • A nonlinear filtering algorithm for denoising HR(S)TEM images is developed. • It can simultaneously handle both periodic and non-periodic features properly. • It is particularly suitable for quantitative electron microscopy. • It is of great interest for beam sensitive samples, in situ analyses, and atomic resolution EFTEM

A Linearized Relaxing Algorithm for the Specific Nonlinear Optimization Problem

Directory of Open Access Journals (Sweden)

Mio Horai

2016-01-01

Full Text Available We propose a new method for the specific nonlinear and nonconvex global optimization problem by using a linear relaxation technique. To simplify the specific nonlinear and nonconvex optimization problem, we transform the problem to the lower linear relaxation form, and we solve the linear relaxation optimization problem by the Branch and Bound Algorithm. Under some reasonable assumptions, the global convergence of the algorithm is certified for the problem. Numerical results show that this method is more efficient than the previous methods.

Weighted expectation maximization reconstruction algorithms with application to gated megavoltage tomography

International Nuclear Information System (INIS)

Zhang Jin; Shi Daxin; Anastasio, Mark A; Sillanpaa, Jussi; Chang Jenghwa

2005-01-01

We propose and investigate weighted expectation maximization (EM) algorithms for image reconstruction in x-ray tomography. The development of the algorithms is motivated by the respiratory-gated megavoltage tomography problem, in which the acquired asymmetric cone-beam projections are limited in number and unevenly sampled over view angle. In these cases, images reconstructed by use of the conventional EM algorithm can contain ring- and streak-like artefacts that are attributable to a combination of data inconsistencies and truncation of the projection data. By use of computer-simulated and clinical gated fan-beam megavoltage projection data, we demonstrate that the proposed weighted EM algorithms effectively mitigate such image artefacts. (note)

Time Reversal Reconstruction Algorithm Based on PSO Optimized SVM Interpolation for Photoacoustic Imaging

Directory of Open Access Journals (Sweden)

Mingjian Sun

2015-01-01

Full Text Available Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO optimized support vector machine (SVM interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.

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.

Performance comparison of attitude determination, attitude estimation, and nonlinear observers algorithms

Science.gov (United States)

MOHAMMED, M. A. SI; BOUSSADIA, H.; BELLAR, A.; ADNANE, A.

2017-01-01

This paper presents a brief synthesis and useful performance analysis of different attitude filtering algorithms (attitude determination algorithms, attitude estimation algorithms, and nonlinear observers) applied to Low Earth Orbit Satellite in terms of accuracy, convergence time, amount of memory, and computation time. This latter is calculated in two ways, using a personal computer and also using On-board computer 750 (OBC 750) that is being used in many SSTL Earth observation missions. The use of this comparative study could be an aided design tool to the designer to choose from an attitude determination or attitude estimation or attitude observer algorithms. The simulation results clearly indicate that the nonlinear Observer is the more logical choice.

Nonlinear dynamical system approaches towards neural prosthesis

International Nuclear Information System (INIS)

Torikai, Hiroyuki; Hashimoto, Sho

2011-01-01

An asynchronous discrete-state spiking neurons is a wired system of shift registers that can mimic nonlinear dynamics of an ODE-based neuron model. The control parameter of the neuron is the wiring pattern among the registers and thus they are suitable for on-chip learning. In this paper an asynchronous discrete-state spiking neuron is introduced and its typical nonlinear phenomena are demonstrated. Also, a learning algorithm for a set of neurons is presented and it is demonstrated that the algorithm enables the set of neurons to reconstruct nonlinear dynamics of another set of neurons with unknown parameter values. The learning function is validated by FPGA experiments.

Experimental verification of transient nonlinear acoustical holography.

Science.gov (United States)

Jing, Yun; Cannata, Jonathan; Wang, Tianren

2013-05-01

This paper presents an experimental study on nonlinear transient acoustical holography. The validity and effectiveness of a recently proposed nonlinear transient acoustical holography algorithm is evaluated in the presence of noise. The acoustic field measured on a post-focal plane of a high-intensity focused transducer is backward projected to reconstruct the pressure distributions on the focal and a pre-focal plane, which are shown to be in good agreement with the measurement. In contrast, the conventional linear holography produces erroneous results in this case where the nonlinearity involved is strong. Forward acoustic field projection was also carried out to further verify the algorithm.

Backtracking algorithm for lepton reconstruction with HADES

International Nuclear Information System (INIS)

Sellheim, P

2015-01-01

The High Acceptance Di-Electron Spectrometer (HADES) at the GSI Helmholtzzentrum für Schwerionenforschung investigates dilepton and strangeness production in elementary and heavy-ion collisions. In April - May 2012 HADES recorded 7 billion Au+Au events at a beam energy of 1.23 GeV/u with the highest multiplicities measured so far. The track reconstruction and particle identification in the high track density environment are challenging. The most important detector component for lepton identification is the Ring Imaging Cherenkov detector. Its main purpose is the separation of electrons and positrons from large background of charged hadrons produced in heavy-ion collisions. In order to improve lepton identification this backtracking algorithm was developed. In this contribution we will show the results of the algorithm compared to the currently applied method for e +/- identification. Efficiency and purity of a reconstructed e +/- sample will be discussed as well. (paper)

FPGA Hardware Acceleration of a Phylogenetic Tree Reconstruction with Maximum Parsimony Algorithm

OpenAIRE

BLOCK, Henry; MARUYAMA, Tsutomu

2017-01-01

In this paper, we present an FPGA hardware implementation for a phylogenetic tree reconstruction with a maximum parsimony algorithm. We base our approach on a particular stochastic local search algorithm that uses the Progressive Neighborhood and the Indirect Calculation of Tree Lengths method. This method is widely used for the acceleration of the phylogenetic tree reconstruction algorithm in software. In our implementation, we define a tree structure and accelerate the search by parallel an...

Dual Super-Systolic Core for Real-Time Reconstructive Algorithms of High-Resolution Radar/SAR Imaging Systems

Science.gov (United States)

Atoche, Alejandro Castillo; Castillo, Javier Vázquez

2012-01-01

A high-speed dual super-systolic core for reconstructive signal processing (SP) operations consists of a double parallel systolic array (SA) machine in which each processing element of the array is also conceptualized as another SA in a bit-level fashion. In this study, we addressed the design of a high-speed dual super-systolic array (SSA) core for the enhancement/reconstruction of remote sensing (RS) imaging of radar/synthetic aperture radar (SAR) sensor systems. The selected reconstructive SP algorithms are efficiently transformed in their parallel representation and then, they are mapped into an efficient high performance embedded computing (HPEC) architecture in reconfigurable Xilinx field programmable gate array (FPGA) platforms. As an implementation test case, the proposed approach was aggregated in a HW/SW co-design scheme in order to solve the nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) from a remotely sensed scene. We show how such dual SSA core, drastically reduces the computational load of complex RS regularization techniques achieving the required real-time operational mode. PMID:22736964

Optimization, evaluation, and comparison of standard algorithms for image reconstruction with the VIP-PET.

Science.gov (United States)

Mikhaylova, E; Kolstein, M; De Lorenzo, G; Chmeissani, M

2014-07-01

A novel positron emission tomography (PET) scanner design based on a room-temperature pixelated CdTe solid-state detector is being developed within the framework of the Voxel Imaging PET (VIP) Pathfinder project [1]. The simulation results show a great potential of the VIP to produce high-resolution images even in extremely challenging conditions such as the screening of a human head [2]. With unprecedented high channel density (450 channels/cm 3 ) image reconstruction is a challenge. Therefore optimization is needed to find the best algorithm in order to exploit correctly the promising detector potential. The following reconstruction algorithms are evaluated: 2-D Filtered Backprojection (FBP), Ordered Subset Expectation Maximization (OSEM), List-Mode OSEM (LM-OSEM), and the Origin Ensemble (OE) algorithm. The evaluation is based on the comparison of a true image phantom with a set of reconstructed images obtained by each algorithm. This is achieved by calculation of image quality merit parameters such as the bias, the variance and the mean square error (MSE). A systematic optimization of each algorithm is performed by varying the reconstruction parameters, such as the cutoff frequency of the noise filters and the number of iterations. The region of interest (ROI) analysis of the reconstructed phantom is also performed for each algorithm and the results are compared. Additionally, the performance of the image reconstruction methods is compared by calculating the modulation transfer function (MTF). The reconstruction time is also taken into account to choose the optimal algorithm. The analysis is based on GAMOS [3] simulation including the expected CdTe and electronic specifics.

Computed laminography and reconstruction algorithm

International Nuclear Information System (INIS)

Que Jiemin; Cao Daquan; Zhao Wei; Tang Xiao

2012-01-01

Computed laminography (CL) is an alternative to computed tomography if large objects are to be inspected with high resolution. This is especially true for planar objects. In this paper, we set up a new scanning geometry for CL, and study the algebraic reconstruction technique (ART) for CL imaging. We compare the results of ART with variant weighted functions by computer simulation with a digital phantom. It proves that ART algorithm is a good choice for the CL system. (authors)

A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm.

Science.gov (United States)

Mascolo-Fortin, Julia; Matenine, Dmitri; Archambault, Louis; Després, Philippe

2018-01-01

Four-dimensional cone beam computed tomography allows for temporally resolved imaging with useful applications in radiotherapy, but raises particular challenges in terms of image quality and computation time. The purpose of this work is to develop a fast and accurate 4D algorithm by adapting a GPU-accelerated ordered subsets convex algorithm (OSC), combined with the total variation minimization regularization technique (TV). Different initialization schemes were studied to adapt the OSC-TV algorithm to 4D reconstruction: each respiratory phase was initialized either with a 3D reconstruction or a blank image. Reconstruction algorithms were tested on a dynamic numerical phantom and on a clinical dataset. 4D iterations were implemented for a cluster of 8 GPUs. All developed methods allowed for an adequate visualization of the respiratory movement and compared favorably to the McKinnon-Bates and adaptive steepest descent projection onto convex sets algorithms, while the 4D reconstructions initialized from a prior 3D reconstruction led to better overall image quality. The most suitable adaptation of OSC-TV to 4D CBCT was found to be a combination of a prior FDK reconstruction and a 4D OSC-TV reconstruction with a reconstruction time of 4.5 minutes. This relatively short reconstruction time could facilitate a clinical use.

Accurate 3D reconstruction by a new PDS-OSEM algorithm for HRRT

Science.gov (United States)

Chen, Tai-Been; Horng-Shing Lu, Henry; Kim, Hang-Keun; Son, Young-Don; Cho, Zang-Hee

2014-03-01

State-of-the-art high resolution research tomography (HRRT) provides high resolution PET images with full 3D human brain scanning. But, a short time frame in dynamic study causes many problems related to the low counts in the acquired data. The PDS-OSEM algorithm was proposed to reconstruct the HRRT image with a high signal-to-noise ratio that provides accurate information for dynamic data. The new algorithm was evaluated by simulated image, empirical phantoms, and real human brain data. Meanwhile, the time activity curve was adopted to validate a reconstructed performance of dynamic data between PDS-OSEM and OP-OSEM algorithms. According to simulated and empirical studies, the PDS-OSEM algorithm reconstructs images with higher quality, higher accuracy, less noise, and less average sum of square error than those of OP-OSEM. The presented algorithm is useful to provide quality images under the condition of low count rates in dynamic studies with a short scan time.

Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.

Science.gov (United States)

Ullah, Azmat; Malik, Suheel Abdullah; Alimgeer, Khurram Saleem

2018-01-01

In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA) with Interior Point Algorithm (IPA) is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.

Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.

Directory of Open Access Journals (Sweden)

Azmat Ullah

Full Text Available In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA with Interior Point Algorithm (IPA is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.

A combined reconstruction-classification method for diffuse optical tomography

Energy Technology Data Exchange (ETDEWEB)

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

2009-11-07

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

Axially symmetric reconstruction of plasma emission and absorption coefficients

International Nuclear Information System (INIS)

Yang Lixin; Jia Hui; Yang Jiankun; Li Xiujian; Chen Shaorong; Liu Xishun

2013-01-01

A layered structure imaging model is developed in order to reconstruct emission coefficients and absorption coefficients simultaneously, in laser fusion core plasma diagnostics. A novel axially symmetric reconstruction method that utilizes the LM (Levenberg-Marquardt) nonlinear least squares minimization algorithm is proposed based on the layered structure. Numerical simulation results demonstrate that the proposed method is sufficiently accurate to reconstruct emission coefficients and absorption coefficients, and when the standard deviation of noise is 0.01, the errors of emission coefficients and absorption coefficients are 0.17, 0.22, respectively. Furthermore, this method could perform much better on reconstruction effect compared with traditional inverse Abel transform algorithms. (authors)

Algorithm of hadron energy reconstruction for combined calorimeters in the DELPHI detector

International Nuclear Information System (INIS)

Gotra, Yu.N.; Tsyganov, E.N.; Zimin, N.I.; Zinchenko, A.I.

1989-01-01

The algorithm of hadron energy reconstruction from responses of electromagnetic and hadron calorimeters is described. The investigations have been carried out using the full-scale prototype of the hadron calorimeter cylindrical part modules. The supposed algorithm allows one to improve energy resolution by 5-7% with conserving the linearly of reconstructed hadron energy. 5 refs.; 4 figs.; 1 tab

Comparison study of reconstruction algorithms for prototype digital breast tomosynthesis using various breast phantoms.

Science.gov (United States)

Kim, Ye-seul; Park, Hye-suk; Lee, Haeng-Hwa; Choi, Young-Wook; Choi, Jae-Gu; Kim, Hak Hee; Kim, Hee-Joung

2016-02-01

Digital breast tomosynthesis (DBT) is a recently developed system for three-dimensional imaging that offers the potential to reduce the false positives of mammography by preventing tissue overlap. Many qualitative evaluations of digital breast tomosynthesis were previously performed by using a phantom with an unrealistic model and with heterogeneous background and noise, which is not representative of real breasts. The purpose of the present work was to compare reconstruction algorithms for DBT by using various breast phantoms; validation was also performed by using patient images. DBT was performed by using a prototype unit that was optimized for very low exposures and rapid readout. Three algorithms were compared: a back-projection (BP) algorithm, a filtered BP (FBP) algorithm, and an iterative expectation maximization (EM) algorithm. To compare the algorithms, three types of breast phantoms (homogeneous background phantom, heterogeneous background phantom, and anthropomorphic breast phantom) were evaluated, and clinical images were also reconstructed by using the different reconstruction algorithms. The in-plane image quality was evaluated based on the line profile and the contrast-to-noise ratio (CNR), and out-of-plane artifacts were evaluated by means of the artifact spread function (ASF). Parenchymal texture features of contrast and homogeneity were computed based on reconstructed images of an anthropomorphic breast phantom. The clinical images were studied to validate the effect of reconstruction algorithms. The results showed that the CNRs of masses reconstructed by using the EM algorithm were slightly higher than those obtained by using the BP algorithm, whereas the FBP algorithm yielded much lower CNR due to its high fluctuations of background noise. The FBP algorithm provides the best conspicuity for larger calcifications by enhancing their contrast and sharpness more than the other algorithms; however, in the case of small-size and low

Statistical image reconstruction for transmission tomography using relaxed ordered subset algorithms

International Nuclear Information System (INIS)

Kole, J S

2005-01-01

Statistical reconstruction methods offer possibilities for improving image quality as compared to analytical methods, but current reconstruction times prohibit routine clinical applications in x-ray computed tomography (CT). To reduce reconstruction times, we have applied (under) relaxation to ordered subset algorithms. This enables us to use subsets consisting of only single projection angle, effectively increasing the number of image updates within an entire iteration. A second advantage of applying relaxation is that it can help improve convergence by removing the limit cycle behaviour of ordered subset algorithms, which normally do not converge to an optimal solution but rather a suboptimal limit cycle consisting of as many points as there are subsets. Relaxation suppresses the limit cycle behaviour by decreasing the stepsize for approaching the solution. A simulation study for a 2D mathematical phantom and three different ordered subset algorithms shows that all three algorithms benefit from relaxation: equal noise-to-resolution trade-off can be achieved using fewer iterations than the conventional algorithms, while a lower minimal normalized mean square error (NMSE) clearly indicates a better convergence. Two different schemes for setting the relaxation parameter are studied, and both schemes yield approximately the same minimal NMSE

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

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

International Nuclear Information System (INIS)

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

2009-01-01

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

Linear array implementation of the EM algorithm for PET image reconstruction

International Nuclear Information System (INIS)

Rajan, K.; Patnaik, L.M.; Ramakrishna, J.

1995-01-01

The PET image reconstruction based on the EM algorithm has several attractive advantages over the conventional convolution back projection algorithms. However, the PET image reconstruction based on the EM algorithm is computationally burdensome for today's single processor systems. In addition, a large memory is required for the storage of the image, projection data, and the probability matrix. Since the computations are easily divided into tasks executable in parallel, multiprocessor configurations are the ideal choice for fast execution of the EM algorithms. In tis study, the authors attempt to overcome these two problems by parallelizing the EM algorithm on a multiprocessor systems. The parallel EM algorithm on a linear array topology using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PE's) has been implemented. The performance of the EM algorithm on a 386/387 machine, IBM 6000 RISC workstation, and on the linear array system is discussed and compared. The results show that the computational speed performance of a linear array using 8 DSP chips as PE's executing the EM image reconstruction algorithm is about 15.5 times better than that of the IBM 6000 RISC workstation. The novelty of the scheme is its simplicity. The linear array topology is expandable with a larger number of PE's. The architecture is not dependant on the DSP chip chosen, and the substitution of the latest DSP chip is straightforward and could yield better speed performance

Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms

Science.gov (United States)

Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr

2017-12-01

There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of the image reconstruction parameters, for which there are no well-established criteria. This paper presents a comprehensive evaluation of parameter selection in a number of major TV-based reconstruction algorithms. An appropriate way of selecting the values for each individual parameter has been suggested. Finally, a new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented, which implements the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows significant robustness compared to three other existing algorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with fewer sensitive parameters to tune.

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

Science.gov (United States)

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

2010-09-01

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

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.

On Secret Sharing with Nonlinear Product Reconstruction

DEFF Research Database (Denmark)

Cascudo Pueyo, Ignacio; Cramer, Ronald; Mirandola, Diego

2015-01-01

Multiplicative linear secret sharing is a fundamental notion in the area of secure multiparty computation and, since recently, in the area of two-party cryptography as well. In a nutshell, this notion guarantees that the product of two secrets is obtained as a linear function of the vector......-necessarily-linear “product reconstruction function.” Is the resulting notion equivalent to multiplicative linear secret sharing? We show the (perhaps somewhat counterintuitive) result that this relaxed notion is strictly more general. Concretely, fix a finite field ${\\mathbb F}_q$ as the base field over which linear secret...... sharing is considered. Then we show there exists an (exotic) linear secret sharing scheme with an unbounded number of players $n$ such that it has $t$-privacy with $t = \\Omega(n)$ and such that it does admit a product reconstruction function, yet this function is necessarily nonlinear. In addition, we...

The AOLI Non-Linear Curvature Wavefront Sensor: High sensitivity reconstruction for low-order AO

Science.gov (United States)

Crass, Jonathan; King, David; Mackay, Craig

2013-12-01

Many adaptive optics (AO) systems in use today require bright reference objects to determine the effects of atmospheric distortions on incoming wavefronts. This requirement is because Shack Hartmann wavefront sensors (SHWFS) distribute incoming light from reference objects into a large number of sub-apertures. Bright natural reference objects occur infrequently across the sky leading to the use of laser guide stars which add complexity to wavefront measurement systems. The non-linear curvature wavefront sensor as described by Guyon et al. has been shown to offer a significant increase in sensitivity when compared to a SHWFS. This facilitates much greater sky coverage using natural guide stars alone. This paper describes the current status of the non-linear curvature wavefront sensor being developed as part of an adaptive optics system for the Adaptive Optics Lucky Imager (AOLI) project. The sensor comprises two photon-counting EMCCD detectors from E2V Technologies, recording intensity at four near-pupil planes. These images are used with a reconstruction algorithm to determine the phase correction to be applied by an ALPAO 241-element deformable mirror. The overall system is intended to provide low-order correction for a Lucky Imaging based multi CCD imaging camera. We present the current optical design of the instrument including methods to minimise inherent optical effects, principally chromaticity. Wavefront reconstruction methods are discussed and strategies for their optimisation to run at the required real-time speeds are introduced. Finally, we discuss laboratory work with a demonstrator setup of the system.

PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems

Science.gov (United States)

Liu, Haopeng; Zhu, Yunpeng; Luo, Zhong; Han, Qingkai

2017-09-01

In response to the identification problem concerning multi-degree of freedom (MDOF) nonlinear systems, this study presents the extended forward orthogonal regression (EFOR) based on predicted residual sums of squares (PRESS) to construct a nonlinear dynamic parametrical model. The proposed parametrical model is based on the non-linear autoregressive with exogenous inputs (NARX) model and aims to explicitly reveal the physical design parameters of the system. The PRESS-based EFOR algorithm is proposed to identify such a model for MDOF systems. By using the algorithm, we built a common-structured model based on the fundamental concept of evaluating its generalization capability through cross-validation. The resulting model aims to prevent over-fitting with poor generalization performance caused by the average error reduction ratio (AERR)-based EFOR algorithm. Then, a functional relationship is established between the coefficients of the terms and the design parameters of the unified model. Moreover, a 5-DOF nonlinear system is taken as a case to illustrate the modeling of the proposed algorithm. Finally, a dynamic parametrical model of a cantilever beam is constructed from experimental data. Results indicate that the dynamic parametrical model of nonlinear systems, which depends on the PRESS-based EFOR, can accurately predict the output response, thus providing a theoretical basis for the optimal design of modeling methods for MDOF nonlinear systems.

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

Science.gov (United States)

PourkamaliAnaraki, Maryam; Sadeghi, Mehdi

2016-03-01

Reconstructing haplotypes from SNP fragments is an important problem in computational biology. There have been a lot of interests in this field because haplotypes have been shown to contain promising data for disease association research. It is proved that haplotype reconstruction in Minimum Error Correction model is an NP-hard problem. Therefore, several methods such as clustering techniques, evolutionary algorithms, neural networks and swarm intelligence approaches have been proposed in order to solve this problem in appropriate time. In this paper, we have focused on various evolutionary clustering techniques and try to find an efficient technique for solving haplotype reconstruction problem. It can be referred from our experiments that the clustering methods relying on the behaviour of honey bee colony in nature, specifically bees algorithm and artificial bee colony methods, are expected to result in more efficient solutions. An application program of the methods is available at the following link. http://www.bioinf.cs.ipm.ir/software/haprs/

A quadratic approximation-based algorithm for the solution of multiparametric mixed-integer nonlinear programming problems

KAUST Repository

Domí nguez, Luis F.; Pistikopoulos, Efstratios N.

2012-01-01

An algorithm for the solution of convex multiparametric mixed-integer nonlinear programming problems arising in process engineering problems under uncertainty is introduced. The proposed algorithm iterates between a multiparametric nonlinear

Flaw characterization through nonlinear ultrasonics and wavelet cross-correlation algorithms

Science.gov (United States)

Bunget, Gheorghe; Yee, Andrew; Stewart, Dylan; Rogers, James; Henley, Stanley; Bugg, Chris; Cline, John; Webster, Matthew; Farinholt, Kevin; Friedersdorf, Fritz

2018-04-01

Ultrasonic measurements have become increasingly important non-destructive techniques to characterize flaws found within various in-service industrial components. The prediction of remaining useful life based on fracture analysis depends on the accurate estimation of flaw size and orientation. However, amplitude-based ultrasonic measurements are not able to estimate the plastic zones that exist ahead of crack tips. Estimating the size of the plastic zone is an advantage since some flaws may propagate faster than others. This paper presents a wavelet cross-correlation (WCC) algorithm that was applied to nonlinear analysis of ultrasonically guided waves (GW). By using this algorithm, harmonics present in the waveforms were extracted and nonlinearity parameters were used to indicate both the tip of the cracks and size of the plastic zone. B-scans performed with the quadratic nonlinearities were sensitive to micro-damage specific to plastic zones.

TauFinder: A Reconstruction Algorithm for τ Leptons at Linear Colliders

CERN Document Server

Muennich, A

2010-01-01

An algorithm to find and reconstruct τ leptons was developed, which targets τs that produce high energetic, low multiplicity jets as can be observed at multi TeV e+e− collisions. However, it makes no assumption about the decay of the τ candidate thus finding hadronic as well as leptonic decays. The algorithm delivers a reconstructed τ as seen by the detector. This note provides an overview of the algorithm, the cuts used and gives some evaluation of the performance. A first implementation is available within the ILC software framework as a MAR- LIN processor . Appendix A is intended as a short user manual.

Rapid maximum likelihood ancestral state reconstruction of continuous characters: A rerooting-free algorithm.

Science.gov (United States)

Goolsby, Eric W

2017-04-01

Ancestral state reconstruction is a method used to study the evolutionary trajectories of quantitative characters on phylogenies. Although efficient methods for univariate ancestral state reconstruction under a Brownian motion model have been described for at least 25 years, to date no generalization has been described to allow more complex evolutionary models, such as multivariate trait evolution, non-Brownian models, missing data, and within-species variation. Furthermore, even for simple univariate Brownian motion models, most phylogenetic comparative R packages compute ancestral states via inefficient tree rerooting and full tree traversals at each tree node, making ancestral state reconstruction extremely time-consuming for large phylogenies. Here, a computationally efficient method for fast maximum likelihood ancestral state reconstruction of continuous characters is described. The algorithm has linear complexity relative to the number of species and outperforms the fastest existing R implementations by several orders of magnitude. The described algorithm is capable of performing ancestral state reconstruction on a 1,000,000-species phylogeny in fewer than 2 s using a standard laptop, whereas the next fastest R implementation would take several days to complete. The method is generalizable to more complex evolutionary models, such as phylogenetic regression, within-species variation, non-Brownian evolutionary models, and multivariate trait evolution. Because this method enables fast repeated computations on phylogenies of virtually any size, implementation of the described algorithm can drastically alleviate the computational burden of many otherwise prohibitively time-consuming tasks requiring reconstruction of ancestral states, such as phylogenetic imputation of missing data, bootstrapping procedures, Expectation-Maximization algorithms, and Bayesian estimation. The described ancestral state reconstruction algorithm is implemented in the Rphylopars

Super-Relaxed ( -Proximal Point Algorithms, Relaxed ( -Proximal Point Algorithms, Linear Convergence Analysis, and Nonlinear Variational Inclusions

Directory of Open Access Journals (Sweden)

Agarwal RaviP

2009-01-01

Full Text Available We glance at recent advances to the general theory of maximal (set-valued monotone mappings and their role demonstrated to examine the convex programming and closely related field of nonlinear variational inequalities. We focus mostly on applications of the super-relaxed ( -proximal point algorithm to the context of solving a class of nonlinear variational inclusion problems, based on the notion of maximal ( -monotonicity. Investigations highlighted in this communication are greatly influenced by the celebrated work of Rockafellar (1976, while others have played a significant part as well in generalizing the proximal point algorithm considered by Rockafellar (1976 to the case of the relaxed proximal point algorithm by Eckstein and Bertsekas (1992. Even for the linear convergence analysis for the overrelaxed (or super-relaxed ( -proximal point algorithm, the fundamental model for Rockafellar's case does the job. Furthermore, we attempt to explore possibilities of generalizing the Yosida regularization/approximation in light of maximal ( -monotonicity, and then applying to first-order evolution equations/inclusions.

Reconstruction of a uniform star object from interior x-ray data: uniqueness, stability and algorithm

International Nuclear Information System (INIS)

Van Gompel, Gert; Batenburg, K Joost; Defrise, Michel

2009-01-01

In this paper we consider the problem of reconstructing a two-dimensional star-shaped object of uniform density from truncated projections of the object. In particular, we prove that such an object is uniquely determined by its parallel projections sampled over a full π angular range with a detector that only covers an interior field-of-view, even if the density of the object is not known a priori. We analyze the stability of this reconstruction problem and propose a reconstruction algorithm. Simulation experiments demonstrate that the algorithm is capable of reconstructing a star-shaped object from interior data, even if the interior region is much smaller than the size of the object. In addition, we present results for a heuristic reconstruction algorithm called DART, that was recently proposed. The heuristic method is shown to yield accurate reconstructions if the density is known in advance, and to have a very good stability in the presence of noisy projection data. Finally, the performance of the DBP and DART algorithms is illustrated for the reconstruction of real micro-CT data of a diamond

A reconstruction algorithm for coherent scatter computed tomography based on filtered back-projection

International Nuclear Information System (INIS)

Stevendaal, U. van; Schlomka, J.-P.; Harding, A.; Grass, M.

2003-01-01

Coherent scatter computed tomography (CSCT) is a reconstructive x-ray imaging technique that yields the spatially resolved coherent-scatter form factor of the investigated object. Reconstruction from coherently scattered x-rays is commonly done using algebraic reconstruction techniques (ART). In this paper, we propose an alternative approach based on filtered back-projection. For the first time, a three-dimensional (3D) filtered back-projection technique using curved 3D back-projection lines is applied to two-dimensional coherent scatter projection data. The proposed algorithm is tested with simulated projection data as well as with projection data acquired with a demonstrator setup similar to a multi-line CT scanner geometry. While yielding comparable image quality as ART reconstruction, the modified 3D filtered back-projection algorithm is about two orders of magnitude faster. In contrast to iterative reconstruction schemes, it has the advantage that subfield-of-view reconstruction becomes feasible. This allows a selective reconstruction of the coherent-scatter form factor for a region of interest. The proposed modified 3D filtered back-projection algorithm is a powerful reconstruction technique to be implemented in a CSCT scanning system. This method gives coherent scatter CT the potential of becoming a competitive modality for medical imaging or nondestructive testing

Microstructure Reconstruction of Sheet Molding Composite Using a Random Chips Packing Algorithm

Energy Technology Data Exchange (ETDEWEB)

Huang, Tianyu; Xu, Hongyi; Chen, Wei

2017-04-06

Fiber-reinforced polymer composites are strong candidates for structural materials to replace steel and light alloys in lightweight vehicle design because of their low density and relatively high strength. In the integrated computational materials engineering (ICME) development of carbon fiber composites, microstructure reconstruction algorithms are needed to generate material microstructure representative volume element (RVE) based on the material processing information. The microstructure RVE reconstruction enables the material property prediction by finite element analysis (FEA)This paper presents an algorithm to reconstruct the microstructure of a chopped carbon fiber/epoxy laminate material system produced by compression molding, normally known as sheet molding compounds (SMC). The algorithm takes the result from material’s manufacturing process as inputs, such as the orientation tensor of fibers, the chopped fiber sheet geometry, and the fiber volume fraction. The chopped fiber sheets are treated as deformable rectangle chips and a random packing algorithm is developed to pack these chips into a square plate. The RVE is built in a layer-by-layer fashion until the desired number of lamina is reached, then a fine tuning process is applied to finalize the reconstruction. Compared to the previous methods, this new approach has the ability to model bended fibers by allowing limited amount of overlaps of rectangle chips. Furthermore, the method does not need SMC microstructure images, for which the image-based characterization techniques have not been mature enough, as inputs. Case studies are performed and the results show that the statistics of the reconstructed microstructures generated by the algorithm matches well with the target input parameters from processing.

Reconstruction of a digital core containing clay minerals based on a clustering algorithm.

Science.gov (United States)

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K-means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

Reconstruction of a digital core containing clay minerals based on a clustering algorithm

Science.gov (United States)

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K -means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

A new simple iterative reconstruction algorithm for SPECT transmission measurement

International Nuclear Information System (INIS)

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

2005-01-01

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

Direct cone-beam cardiac reconstruction algorithm with cardiac banding artifact correction

International Nuclear Information System (INIS)

Taguchi, Katsuyuki; Chiang, Beshan S.; Hein, Ilmar A.

2006-01-01

Multislice helical computed tomography (CT) is a promising noninvasive technique for coronary artery imaging. Various factors can cause inconsistencies in cardiac CT data, which can result in degraded image quality. These inconsistencies may be the result of the patient physiology (e.g., heart rate variations), the nature of the data (e.g., cone-angle), or the reconstruction algorithm itself. An algorithm which provides the best temporal resolution for each slice, for example, often provides suboptimal image quality for the entire volume since the cardiac temporal resolution (TRc) changes from slice to slice. Such variations in TRc can generate strong banding artifacts in multi-planar reconstruction images or three-dimensional images. Discontinuous heart walls and coronary arteries may compromise the accuracy of the diagnosis. A β-blocker is often used to reduce and stabilize patients' heart rate but cannot eliminate the variation. In order to obtain robust and optimal image quality, a software solution that increases the temporal resolution and decreases the effect of heart rate is highly desirable. This paper proposes an ECG-correlated direct cone-beam reconstruction algorithm (TCOT-EGR) with cardiac banding artifact correction (CBC) and disconnected projections redundancy compensation technique (DIRECT). First the theory and analytical model of the cardiac temporal resolution is outlined. Next, the performance of the proposed algorithms is evaluated by using computer simulations as well as patient data. It will be shown that the proposed algorithms enhance the robustness of the image quality against inconsistencies by guaranteeing smooth transition of heart cycles used in reconstruction

Optimization of reconstruction algorithms using Monte Carlo simulation

International Nuclear Information System (INIS)

Hanson, K.M.

1989-01-01

A method for optimizing reconstruction algorithms is presented that is based on how well a specified task can be performed using the reconstructed images. Task performance is numerically assessed by a Monte Carlo simulation of the complete imaging process including the generation of scenes appropriate to the desired application, subsequent data taking, reconstruction, and performance of the stated task based on the final image. The use of this method is demonstrated through the optimization of the Algebraic Reconstruction Technique (ART), which reconstructs images from their projections by a iterative procedure. The optimization is accomplished by varying the relaxation factor employed in the updating procedure. In some of the imaging situations studied, it is found that the optimization of constrained ART, in which a nonnegativity constraint is invoked, can vastly increase the detectability of objects. There is little improvement attained for unconstrained ART. The general method presented may be applied to the problem of designing neutron-diffraction spectrometers. 11 refs., 6 figs., 2 tabs

Optimization of reconstruction algorithms using Monte Carlo simulation

International Nuclear Information System (INIS)

Hanson, K.M.

1989-01-01

A method for optimizing reconstruction algorithms is presented that is based on how well a specified task can be performed using the reconstructed images. Task performance is numerically assessed by a Monte Carlo simulation of the complete imaging process including the generation of scenes appropriate to the desired application, subsequent data taking, reconstruction, and performance of the stated task based on the final image. The use of this method is demonstrated through the optimization of the Algebraic Reconstruction Technique (ART), which reconstructs images from their projections by an iterative procedure. The optimization is accomplished by varying the relaxation factor employed in the updating procedure. In some of the imaging situations studied, it is found that the optimization of constrained ART, in which a non-negativity constraint is invoked, can vastly increase the detectability of objects. There is little improvement attained for unconstrained ART. The general method presented may be applied to the problem of designing neutron-diffraction spectrometers. (author)

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

Purpose: Compton camera imaging (CCI) systems are currently under investigation for radiotherapy dose reconstruction and verification. The ability of such a system to provide real-time images during dose delivery will be limited by the computational speed of the image reconstruction algorithm. In this work, the authors present a fast and simple method by which to generate an initial back-projected image from acquired CCI data, suitable for use in a filtered back-projection algorithm or as a starting point for iterative reconstruction algorithms, and compare its performance to the current state of the art. Methods: Each detector event in a CCI system describes a conical surface that includes the true point of origin of the detected photon. Numerical image reconstruction algorithms require, as a first step, the back-projection of each of these conical surfaces into an image space. The algorithm presented here first generates a solution matrix for each slice of the image space by solving the intersection of the conical surface with the image plane. Each element of the solution matrix is proportional to the distance of the corresponding voxel from the true intersection curve. A threshold function was developed to extract those pixels sufficiently close to the true intersection to generate a binary intersection curve. This process is repeated for each image plane for each CCI detector event, resulting in a three-dimensional back-projection image. The performance of this algorithm was tested against a marching algorithm known for speed and accuracy. Results: The threshold-based algorithm was found to be approximately four times faster than the current state of the art with minimal deficit to image quality, arising from the fact that a generically applicable threshold function cannot provide perfect results in all situations. The algorithm fails to extract a complete intersection curve in image slices near the detector surface for detector event cones having axes nearly

High-speed computation of the EM algorithm for PET image reconstruction

International Nuclear Information System (INIS)

Rajan, K.; Patnaik, L.M.; Ramakrishna, J.

1994-01-01

The PET image reconstruction based on the EM algorithm has several attractive advantages over the conventional convolution backprojection algorithms. However, two major drawbacks have impeded the routine use of the EM algorithm, namely, the long computational time due to slow convergence and the large memory required for the storage of the image, projection data and the probability matrix. In this study, the authors attempts to solve these two problems by parallelizing the EM algorithm on a multiprocessor system. The authors have implemented an extended hypercube (EH) architecture for the high-speed computation of the EM algorithm using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PEs). The authors discuss and compare the performance of the EM algorithm on a 386/387 machine, CD 4360 mainframe, and on the EH system. The results show that the computational speed performance of an EH using DSP chips as PEs executing the EM image reconstruction algorithm is about 130 times better than that of the CD 4360 mainframe. The EH topology is expandable with more number of PEs

Direct 2-D reconstructions of conductivity and permittivity from EIT data on a human chest.

Science.gov (United States)

Herrera, Claudia N L; Vallejo, Miguel F M; Mueller, Jennifer L; Lima, Raul G

2015-01-01

A novel direct D-bar reconstruction algorithm is presented for reconstructing a complex conductivity distribution from 2-D EIT data. The method is applied to simulated data and archival human chest data. Permittivity reconstructions with the aforementioned method and conductivity reconstructions with the previously existing nonlinear D-bar method for real-valued conductivities depicting ventilation and perfusion in the human chest are presented. This constitutes the first fully nonlinear D-bar reconstructions of human chest data and the first D-bar permittivity reconstructions of experimental data. The results of the human chest data reconstructions are compared on a circular domain versus a chest-shaped domain.

Angle Statistics Reconstruction: a robust reconstruction algorithm for Muon Scattering Tomography

Science.gov (United States)

Stapleton, M.; Burns, J.; Quillin, S.; Steer, C.

2014-11-01

Muon Scattering Tomography (MST) is a technique for using the scattering of cosmic ray muons to probe the contents of enclosed volumes. As a muon passes through material it undergoes multiple Coulomb scattering, where the amount of scattering is dependent on the density and atomic number of the material as well as the path length. Hence, MST has been proposed as a means of imaging dense materials, for instance to detect special nuclear material in cargo containers. Algorithms are required to generate an accurate reconstruction of the material density inside the volume from the muon scattering information and some have already been proposed, most notably the Point of Closest Approach (PoCA) and Maximum Likelihood/Expectation Maximisation (MLEM) algorithms. However, whilst PoCA-based algorithms are easy to implement, they perform rather poorly in practice. Conversely, MLEM is a complicated algorithm to implement and computationally intensive and there is currently no published, fast and easily-implementable algorithm that performs well in practice. In this paper, we first provide a detailed analysis of the source of inaccuracy in PoCA-based algorithms. We then motivate an alternative method, based on ideas first laid out by Morris et al, presenting and fully specifying an algorithm that performs well against simulations of realistic scenarios. We argue this new algorithm should be adopted by developers of Muon Scattering Tomography as an alternative to PoCA.

A quasi-Newton algorithm for large-scale nonlinear equations

Directory of Open Access Journals (Sweden)

Linghua Huang

2017-02-01

Full Text Available Abstract In this paper, the algorithm for large-scale nonlinear equations is designed by the following steps: (i a conjugate gradient (CG algorithm is designed as a sub-algorithm to obtain the initial points of the main algorithm, where the sub-algorithm’s initial point does not have any restrictions; (ii a quasi-Newton algorithm with the initial points given by sub-algorithm is defined as main algorithm, where a new nonmonotone line search technique is presented to get the step length α k $\\alpha_{k}$ . The given nonmonotone line search technique can avoid computing the Jacobian matrix. The global convergence and the 1 + q $1+q$ -order convergent rate of the main algorithm are established under suitable conditions. Numerical results show that the proposed method is competitive with a similar method for large-scale problems.

Convergence of Algorithms for Reconstructing Convex Bodies and Directional Measures

DEFF Research Database (Denmark)

Gardner, Richard; Kiderlen, Markus; Milanfar, Peyman

2006-01-01

We investigate algorithms for reconstructing a convex body K in Rn from noisy measurements of its support function or its brightness function in k directions u1, . . . , uk. The key idea of these algorithms is to construct a convex polytope Pk whose support function (or brightness function) best...

A practical exact maximum compatibility algorithm for reconstruction of recent evolutionary history

OpenAIRE

Cherry, Joshua L.

2017-01-01

Background Maximum compatibility is a method of phylogenetic reconstruction that is seldom applied to molecular sequences. It may be ideal for certain applications, such as reconstructing phylogenies of closely-related bacteria on the basis of whole-genome sequencing. Results Here I present an algorithm that rapidly computes phylogenies according to a compatibility criterion. Although based on solutions to the maximum clique problem, this algorithm deals properly with ambiguities in the data....

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT

International Nuclear Information System (INIS)

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A.; Pan Xiaochuan

2010-01-01

Purpose: Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. Methods: The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredbackprojection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. Results: The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. Conclusions: They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.

Novel image reconstruction algorithm for multi-phase flow tomography system using γ ray method

International Nuclear Information System (INIS)

Hao Kuihong; Wang Huaxiang; Gao Mei

2007-01-01

After analyzing the reason of image reconstructed algorithm by using the conventional back projection (IBP) is prone to produce spurious line, and considering the characteristic of multi-phase flow tomography, a novel image reconstruction algorithm is proposed, which carries out the intersection calculation using back projection data. This algorithm can obtain a perfect system point spread function, and can eliminate spurious line better. Simulating results show that the algorithm is effective for identifying multi-phase flow pattern. (authors)

On Inverse Coefficient Heat-Conduction Problems on Reconstruction of Nonlinear Components of the Thermal-Conductivity Tensor of Anisotropic Bodies

Science.gov (United States)

Formalev, V. F.; Kolesnik, S. A.

2017-11-01

The authors are the first to present a closed procedure for numerical solution of inverse coefficient problems of heat conduction in anisotropic materials used as heat-shielding ones in rocket and space equipment. The reconstructed components of the thermal-conductivity tensor depend on temperature (are nonlinear). The procedure includes the formation of experimental data, the implicit gradient-descent method, the economical absolutely stable method of numerical solution of parabolic problems containing mixed derivatives, the parametric identification, construction, and numerical solution of the problem for elements of sensitivity matrices, the development of a quadratic residual functional and regularizing functionals, and also the development of algorithms and software systems. The implicit gradient-descent method permits expanding the quadratic functional in a Taylor series with retention of the linear terms for the increments of the sought functions. This substantially improves the exactness and stability of solution of the inverse problems. Software systems are developed with account taken of the errors in experimental data and disregarding them. On the basis of a priori assumptions of the qualitative behavior of the functional dependences of the components of the thermal-conductivity tensor on temperature, regularizing functionals are constructed by means of which one can reconstruct the components of the thermal-conductivity tensor with an error no higher than the error of the experimental data. Results of the numerical solution of the inverse coefficient problems on reconstruction of nonlinear components of the thermal-conductivity tensor have been obtained and are discussed.

A novel standalone track reconstruction algorithm for the LHCb upgrade

CERN Multimedia

Quagliani, Renato

2018-01-01

During the LHC Run III, starting in 2020, the instantaneous luminosity of LHCb will be increased up to 2×1033 cm−2 s−1, five times larger than in Run II. The LHCb detector will then have to be upgraded in 2019. In fact, a full software event reconstruction will be performed at the full bunch crossing rate by the trigger, in order to profit of the higher instantaneous luminosity provided by the accelerator. In addition, all the tracking devices will be replaced and, in particular, a scintillating fiber tracker (SciFi) will be installed after the magnet, allowing to cope with the higher occupancy. The new running conditions, and the tighter timing constraints in the software trigger, represent a big challenge for the track reconstruction. This talk presents the design and performance of a novel algorithm that has been developed to reconstruct track segments using solely hits from the SciFi. This algorithm is crucial for the reconstruction of tracks originating from long-lived particles such as KS and Λ. ...

Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging.

Science.gov (United States)

Yi, Tianzhu; He, Zhihua; He, Feng; Dong, Zhen; Wu, Manqing

2017-11-07

This paper presents a modified approach for high-resolution, highly squint synthetic aperture radar (SAR) data processing. Several nonlinear chirp scaling (NLCS) algorithms have been proposed to solve the azimuth variance of the frequency modulation rates that are caused by the linear range walk correction (LRWC). However, the azimuth depth of focusing (ADOF) is not handled well by these algorithms. The generalized nonlinear chirp scaling (GNLCS) algorithm that is proposed in this paper uses the method of series reverse (MSR) to improve the ADOF and focusing precision. It also introduces a high order processing kernel to avoid the range block processing. Simulation results show that the GNLCS algorithm can enlarge the ADOF and focusing precision for high-resolution highly squint SAR data.

Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging

Directory of Open Access Journals (Sweden)

Tianzhu Yi

2017-11-01

Full Text Available This paper presents a modified approach for high-resolution, highly squint synthetic aperture radar (SAR data processing. Several nonlinear chirp scaling (NLCS algorithms have been proposed to solve the azimuth variance of the frequency modulation rates that are caused by the linear range walk correction (LRWC. However, the azimuth depth of focusing (ADOF is not handled well by these algorithms. The generalized nonlinear chirp scaling (GNLCS algorithm that is proposed in this paper uses the method of series reverse (MSR to improve the ADOF and focusing precision. It also introduces a high order processing kernel to avoid the range block processing. Simulation results show that the GNLCS algorithm can enlarge the ADOF and focusing precision for high-resolution highly squint SAR data.

TV-constrained incremental algorithms for low-intensity CT image reconstruction

DEFF Research Database (Denmark)

Rose, Sean D.; Andersen, Martin S.; Sidky, Emil Y.

2015-01-01

constraint can be guided by an image reconstructed by filtered backprojection (FBP). We apply our algorithm to low-dose synchrotron X-ray CT data from the Advanced Photon Source (APS) at Argonne National Labs (ANL) to demonstrate its potential utility. We find that the algorithm provides a means of edge-preserving...

A new algorithm for 3D reconstruction from support functions

DEFF Research Database (Denmark)

Gardner, Richard; Kiderlen, Markus

2009-01-01

We introduce a new algorithm for reconstructing an unknown shape from a finite number of noisy measurements of its support function. The algorithm, based on a least squares procedure, is very easy to program in standard software such as Matlab and allows, for the first time, good 3D reconstructio...

An improved energy conserving implicit time integration algorithm for nonlinear dynamic structural analysis

International Nuclear Information System (INIS)

Haug, E.; Rouvray, A.L. de; Nguyen, Q.S.

1977-01-01

This study proposes a general nonlinear algorithm stability criterion; it introduces a nonlinear algorithm, easily implemented in existing incremental/iterative codes, and it applies the new scheme beneficially to problems of linear elastic dynamic snap buckling. Based on the concept of energy conservation, the paper outlines an algorithm which degenerates into the trapezoidal rule, if applied to linear systems. The new algorithm conserves energy in systems having elastic potentials up to the fourth order in the displacements. This is true in the important case of nonlinear total Lagrange formulations where linear elastic material properties are substituted. The scheme is easily implemented in existing incremental-iterative codes with provisions for stiffness reformation and containing the basic Newmark scheme. Numerical analyses of dynamic stability can be dramatically sensitive to amplitude errors, because damping algorithms may mask, and overestimating schemes may numerically trigger, the physical instability. The newly proposed scheme has been applied with larger time steps and less cost to the dynamic snap buckling of simple one and multi degree-of-freedom structures for various initial conditions

3D High Resolution l1-SPIRiT Reconstruction on Gadgetron based Cloud

DEFF Research Database (Denmark)

Xue, Hui; Kelmann, Peter; Inati, Souheil

framework to support distributed computing in a cloud environment. This extension is named GT-Plus. A cloud version of 3D l1-SPIRiT was implemented on the GT-Plus framework. We demonstrate that a 3mins reconstruction could be achieved for 1mm3 isotropic resolution neuro scans with significantly improved......Applying non-linear reconstruction to high resolution 3D MRI is challenging because of the lengthy computing time needed for those iterative algorithms. To achieve practical processing duration to enable clinical usage of non-linear reconstruction, we have extended previously published Gadgetron...

A First-Order Primal-Dual Reconstruction Algorithm for Few-View SPECT

DEFF Research Database (Denmark)

Wolf, Paul; Jørgensen, Jakob Heide; Gilat-Schmidt, Taly

2012-01-01

A sparsity-exploiting algorithm intended for few-view Single Photon Emission Computed Tomography (SPECT) reconstruction is proposed and characterized. The algorithm models the object as piecewise constant subject to a blurring operation. Monte Carlo simulations were performed to provide more proj...

New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks.

Science.gov (United States)

Bouchard, M

2001-01-01

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.

Level-set reconstruction algorithm for ultrafast limited-angle X-ray computed tomography of two-phase flows.

Science.gov (United States)

Bieberle, M; Hampel, U

2015-06-13

Tomographic image reconstruction is based on recovering an object distribution from its projections, which have been acquired from all angular views around the object. If the angular range is limited to less than 180° of parallel projections, typical reconstruction artefacts arise when using standard algorithms. To compensate for this, specialized algorithms using a priori information about the object need to be applied. The application behind this work is ultrafast limited-angle X-ray computed tomography of two-phase flows. Here, only a binary distribution of the two phases needs to be reconstructed, which reduces the complexity of the inverse problem. To solve it, a new reconstruction algorithm (LSR) based on the level-set method is proposed. It includes one force function term accounting for matching the projection data and one incorporating a curvature-dependent smoothing of the phase boundary. The algorithm has been validated using simulated as well as measured projections of known structures, and its performance has been compared to the algebraic reconstruction technique and a binary derivative of it. The validation as well as the application of the level-set reconstruction on a dynamic two-phase flow demonstrated its applicability and its advantages over other reconstruction algorithms. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

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

Geometric reconstruction methods for electron tomography

Energy Technology Data Exchange (ETDEWEB)

Alpers, Andreas, E-mail: alpers@ma.tum.de [Zentrum Mathematik, Technische Universität München, D-85747 Garching bei München (Germany); Gardner, Richard J., E-mail: Richard.Gardner@wwu.edu [Department of Mathematics, Western Washington University, Bellingham, WA 98225-9063 (United States); König, Stefan, E-mail: koenig@ma.tum.de [Zentrum Mathematik, Technische Universität München, D-85747 Garching bei München (Germany); Pennington, Robert S., E-mail: robert.pennington@uni-ulm.de [Center for Electron Nanoscopy, Technical University of Denmark, DK-2800 Kongens Lyngby (Denmark); Boothroyd, Chris B., E-mail: ChrisBoothroyd@cantab.net [Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons and Peter Grünberg Institute, Forschungszentrum Jülich, D-52425 Jülich (Germany); Houben, Lothar, E-mail: l.houben@fz-juelich.de [Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons and Peter Grünberg Institute, Forschungszentrum Jülich, D-52425 Jülich (Germany); Dunin-Borkowski, Rafal E., E-mail: rdb@fz-juelich.de [Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons and Peter Grünberg Institute, Forschungszentrum Jülich, D-52425 Jülich (Germany); Joost Batenburg, Kees, E-mail: Joost.Batenburg@cwi.nl [Centrum Wiskunde and Informatica, NL-1098XG, Amsterdam, The Netherlands and Vision Lab, Department of Physics, University of Antwerp, B-2610 Wilrijk (Belgium)

2013-05-15

Electron tomography is becoming an increasingly important tool in materials science for studying the three-dimensional morphologies and chemical compositions of nanostructures. The image quality obtained by many current algorithms is seriously affected by the problems of missing wedge artefacts and non-linear projection intensities due to diffraction effects. The former refers to the fact that data cannot be acquired over the full 180° tilt range; the latter implies that for some orientations, crystalline structures can show strong contrast changes. To overcome these problems we introduce and discuss several algorithms from the mathematical fields of geometric and discrete tomography. The algorithms incorporate geometric prior knowledge (mainly convexity and homogeneity), which also in principle considerably reduces the number of tilt angles required. Results are discussed for the reconstruction of an InAs nanowire. - Highlights: ► Four algorithms for electron tomography are introduced that utilize prior knowledge. ► Objects are assumed to be homogeneous; convexity and regularity is also discussed. ► We are able to reconstruct slices of a nanowire from as few as four projections. ► Algorithms should be selected based on the specific reconstruction task at hand.

Geometric reconstruction methods for electron tomography

International Nuclear Information System (INIS)

Alpers, Andreas; Gardner, Richard J.; König, Stefan; Pennington, Robert S.; Boothroyd, Chris B.; Houben, Lothar; Dunin-Borkowski, Rafal E.; Joost Batenburg, Kees

2013-01-01

Electron tomography is becoming an increasingly important tool in materials science for studying the three-dimensional morphologies and chemical compositions of nanostructures. The image quality obtained by many current algorithms is seriously affected by the problems of missing wedge artefacts and non-linear projection intensities due to diffraction effects. The former refers to the fact that data cannot be acquired over the full 180° tilt range; the latter implies that for some orientations, crystalline structures can show strong contrast changes. To overcome these problems we introduce and discuss several algorithms from the mathematical fields of geometric and discrete tomography. The algorithms incorporate geometric prior knowledge (mainly convexity and homogeneity), which also in principle considerably reduces the number of tilt angles required. Results are discussed for the reconstruction of an InAs nanowire. - Highlights: ► Four algorithms for electron tomography are introduced that utilize prior knowledge. ► Objects are assumed to be homogeneous; convexity and regularity is also discussed. ► We are able to reconstruct slices of a nanowire from as few as four projections. ► Algorithms should be selected based on the specific reconstruction task at hand

On Newton-Raphson formulation and algorithm for displacement based structural dynamics problem with quadratic damping nonlinearity

Directory of Open Access Journals (Sweden)

Koh Kim Jie

2017-01-01

Full Text Available Quadratic damping nonlinearity is challenging for displacement based structural dynamics problem as the problem is nonlinear in time derivative of the primitive variable. For such nonlinearity, the formulation of tangent stiffness matrix is not lucid in the literature. Consequently, ambiguity related to kinematics update arises when implementing the time integration-iterative algorithm. In present work, an Euler-Bernoulli beam vibration problem with quadratic damping nonlinearity is addressed as the main source of quadratic damping nonlinearity arises from drag force estimation, which is generally valid only for slender structures. Employing Newton-Raphson formulation, tangent stiffness components associated with quadratic damping nonlinearity requires velocity input for evaluation purpose. For this reason, two mathematically equivalent algorithm structures with different kinematics arrangement are tested. Both algorithm structures result in the same accuracy and convergence characteristic of solution.

ADART: an adaptive algebraic reconstruction algorithm for discrete tomography.

Science.gov (United States)

Maestre-Deusto, F Javier; Scavello, Giovanni; Pizarro, Joaquín; Galindo, Pedro L

2011-08-01

In this paper we suggest an algorithm based on the Discrete Algebraic Reconstruction Technique (DART) which is capable of computing high quality reconstructions from substantially fewer projections than required for conventional continuous tomography. Adaptive DART (ADART) goes a step further than DART on the reduction of the number of unknowns of the associated linear system achieving a significant reduction in the pixel error rate of reconstructed objects. The proposed methodology automatically adapts the border definition criterion at each iteration, resulting in a reduction of the number of pixels belonging to the border, and consequently of the number of unknowns in the general algebraic reconstruction linear system to be solved, being this reduction specially important at the final stage of the iterative process. Experimental results show that reconstruction errors are considerably reduced using ADART when compared to original DART, both in clean and noisy environments.

New reconstruction algorithm in helical-volume CT

International Nuclear Information System (INIS)

Toki, Y.; Rifu, T.; Aradate, H.; Hirao, Y.; Ohyama, N.

1990-01-01

This paper reports on helical scanning that is an application of continuous scanning CT to acquire volume data in a short time for three-dimensional study. In a helical scan, the patient couch sustains movement during continuous-rotation scanning and then the acquired data is processed to synthesize a projection data set of vertical section by interpolation. But the synthesized section is not thin enough; also, the image may have artifacts caused by couch movement. A new reconstruction algorithm that helps resolve such problems has been developed and compared with the ordinary algorithm. The authors constructed a helical scan system based on TCT-900S, which can perform 1-second rotation continuously for 30 seconds. The authors measured section thickness using both algorithms on an AAPM phantom, and we also compared degree of artifacts on clinical data

An Efficient Augmented Lagrangian Method for Statistical X-Ray CT Image Reconstruction.

Science.gov (United States)

Li, Jiaojiao; Niu, Shanzhou; Huang, Jing; Bian, Zhaoying; Feng, Qianjin; Yu, Gaohang; Liang, Zhengrong; Chen, Wufan; Ma, Jianhua

2015-01-01

Statistical iterative reconstruction (SIR) for X-ray computed tomography (CT) under the penalized weighted least-squares criteria can yield significant gains over conventional analytical reconstruction from the noisy measurement. However, due to the nonlinear expression of the objective function, most exiting algorithms related to the SIR unavoidably suffer from heavy computation load and slow convergence rate, especially when an edge-preserving or sparsity-based penalty or regularization is incorporated. In this work, to address abovementioned issues of the general algorithms related to the SIR, we propose an adaptive nonmonotone alternating direction algorithm in the framework of augmented Lagrangian multiplier method, which is termed as "ALM-ANAD". The algorithm effectively combines an alternating direction technique with an adaptive nonmonotone line search to minimize the augmented Lagrangian function at each iteration. To evaluate the present ALM-ANAD algorithm, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present ALM-ANAD algorithm can achieve noticeable gains over the classical nonlinear conjugate gradient algorithm and state-of-the-art split Bregman algorithm in terms of noise reduction, contrast-to-noise ratio, convergence rate, and universal quality index metrics.

High resolution reconstruction of PET images using the iterative OSEM algorithm

International Nuclear Information System (INIS)

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

2004-01-01

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

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

Science.gov (United States)

Liu, Li; Lin, Weikai; Jin, Mingwu

2015-01-01

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

New human-centered linear and nonlinear motion cueing algorithms for control of simulator motion systems

Science.gov (United States)

Telban, Robert J.

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. To address this, new human-centered motion cueing algorithms were developed. A revised "optimal algorithm" uses time-invariant filters developed by optimal control, incorporating human vestibular system models. The "nonlinear algorithm" is a novel approach that is also formulated by optimal control, but can also be updated in real time. It incorporates a new integrated visual-vestibular perception model that includes both visual and vestibular sensation and the interaction between the stimuli. A time-varying control law requires the matrix Riccati equation to be solved in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. As a result of unsatisfactory sensation, an augmented turbulence cue was added to the vertical mode for both the optimal and nonlinear algorithms. The relative effectiveness of the algorithms, in simulating aircraft maneuvers, was assessed with an eleven-subject piloted performance test conducted on the NASA Langley Visual Motion Simulator (VMS). Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input

A theoretically exact reconstruction algorithm for helical cone-beam differential phase-contrast computed tomography

International Nuclear Information System (INIS)

Li Jing; Sun Yi; Zhu Peiping

2013-01-01

Differential phase-contrast computed tomography (DPC-CT) reconstruction problems are usually solved by using parallel-, fan- or cone-beam algorithms. For rod-shaped objects, the x-ray beams cannot recover all the slices of the sample at the same time. Thus, if a rod-shaped sample is required to be reconstructed by the above algorithms, one should alternately perform translation and rotation on this sample, which leads to lower efficiency. The helical cone-beam CT may significantly improve scanning efficiency for rod-shaped objects over other algorithms. In this paper, we propose a theoretically exact filter-backprojection algorithm for helical cone-beam DPC-CT, which can be applied to reconstruct the refractive index decrement distribution of the samples directly from two-dimensional differential phase-contrast images. Numerical simulations are conducted to verify the proposed algorithm. Our work provides a potential solution for inspecting the rod-shaped samples using DPC-CT, which may be applicable with the evolution of DPC-CT equipments. (paper)

Superiorized algorithm for reconstruction of CT images from sparse-view and limited-angle polyenergetic data

Science.gov (United States)

Humphries, T.; Winn, J.; Faridani, A.

2017-08-01

Recent work in CT image reconstruction has seen increasing interest in the use of total variation (TV) and related penalties to regularize problems involving reconstruction from undersampled or incomplete data. Superiorization is a recently proposed heuristic which provides an automatic procedure to ‘superiorize’ an iterative image reconstruction algorithm with respect to a chosen objective function, such as TV. Under certain conditions, the superiorized algorithm is guaranteed to find a solution that is as satisfactory as any found by the original algorithm with respect to satisfying the constraints of the problem; this solution is also expected to be superior with respect to the chosen objective. Most work on superiorization has used reconstruction algorithms which assume a linear measurement model, which in the case of CT corresponds to data generated from a monoenergetic x-ray beam. Many CT systems generate x-rays from a polyenergetic spectrum, however, in which the measured data represent an integral of object attenuation over all energies in the spectrum. This inconsistency with the linear model produces the well-known beam hardening artifacts, which impair analysis of CT images. In this work we superiorize an iterative algorithm for reconstruction from polyenergetic data, using both TV and an anisotropic TV (ATV) penalty. We apply the superiorized algorithm in numerical phantom experiments modeling both sparse-view and limited-angle scenarios. In our experiments, the superiorized algorithm successfully finds solutions which are as constraints-compatible as those found by the original algorithm, with significantly reduced TV and ATV values. The superiorized algorithm thus produces images with greatly reduced sparse-view and limited angle artifacts, which are also largely free of the beam hardening artifacts that would be present if a superiorized version of a monoenergetic algorithm were used.

Fast algorithm of track reconstruction for the Delphy TPC

International Nuclear Information System (INIS)

Maillard, J.

1984-01-01

We describe a simple geometrical method (polar inversion) to reconstruct tracks. When the magnetic field is constant in magnitude and direction. This method uses geometrical properties of the trajectories. In the case of the DELPHI apparatus, the track reconstruction is done using TPC informations. After explaining the algorithm, we give results on ''GEANT'' simulated events using the ''Lund'' generator. Today we get a computer time of the order of 1.2 milliseconds on a CDC 7600 and an efficiency of 98% [fr

High resolution electron exit wave reconstruction from a diffraction pattern using Gaussian basis decomposition

International Nuclear Information System (INIS)

Borisenko, Konstantin B; Kirkland, Angus I

2014-01-01

We describe an algorithm to reconstruct the electron exit wave of a weak-phase object from single diffraction pattern. The algorithm uses analytic formulations describing the diffraction intensities through a representation of the object exit wave in a Gaussian basis. The reconstruction is achieved by solving an overdetermined system of non-linear equations using an easily parallelisable global multi-start search with Levenberg-Marquard optimisation and analytic derivatives

Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle–Pock algorithm

DEFF Research Database (Denmark)

Sidky, Emil Y.; Jørgensen, Jakob Heide; Pan, Xiaochuan

2012-01-01

The primal–dual optimization algorithm developed in Chambolle and Pock (CP) (2011 J. Math. Imag. Vis. 40 1–26) is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems...... for the purpose of designing iterative image reconstruction algorithms for CT. The primal–dual algorithm is briefly summarized in this paper, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application...

Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction

Directory of Open Access Journals (Sweden)

Dong Zhang

2017-01-01

Full Text Available Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.

A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks

NARCIS (Netherlands)

K.T. Huber; L.J.J. van Iersel (Leo); S.M. Kelk (Steven); R. Suchecki

2010-01-01

htmlabstractRecently much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic trees in order to accommodate complex evolutionary processes. Here we present an efficient, practical algorithm for reconstructing level-1 phylogenetic networks - a type of

A practical algorithm for reconstructing level-1 phylogenetic networks

NARCIS (Netherlands)

Huber, K.T.; Iersel, van L.J.J.; Kelk, S.M.; Suchecki, R.

2011-01-01

Recently, much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic trees in order to accommodate complex evolutionary processes. Here, we present an efficient, practical algorithm for reconstructing level-1 phylogenetic networks-a type of network

A conjugate gradients/trust regions algorithms for training multilayer perceptrons for nonlinear mapping

Science.gov (United States)

Madyastha, Raghavendra K.; Aazhang, Behnaam; Henson, Troy F.; Huxhold, Wendy L.

1992-01-01

This paper addresses the issue of applying a globally convergent optimization algorithm to the training of multilayer perceptrons, a class of Artificial Neural Networks. The multilayer perceptrons are trained towards the solution of two highly nonlinear problems: (1) signal detection in a multi-user communication network, and (2) solving the inverse kinematics for a robotic manipulator. The research is motivated by the fact that a multilayer perceptron is theoretically capable of approximating any nonlinear function to within a specified accuracy. The algorithm that has been employed in this study combines the merits of two well known optimization algorithms, the Conjugate Gradients and the Trust Regions Algorithms. The performance is compared to a widely used algorithm, the Backpropagation Algorithm, that is basically a gradient-based algorithm, and hence, slow in converging. The performances of the two algorithms are compared with the convergence rate. Furthermore, in the case of the signal detection problem, performances are also benchmarked by the decision boundaries drawn as well as the probability of error obtained in either case.

A general algorithm for the reconstruction of jet events in e+e- annihilation

International Nuclear Information System (INIS)

Goddard, M.C.

1981-01-01

A general method is described to reconstruct a predetermined number of jets. It can reconstruct the jet axes as accurately as any existing algorithm and is up to one hundred times faster. Results are shown from the reconstruction of 2-jet, 3-jet and 4-jet Monte Carlo events. (author)

The Reach-and-Evolve Algorithm for Reachability Analysis of Nonlinear Dynamical Systems

NARCIS (Netherlands)

P.J. Collins (Pieter); A. Goldsztejn

2008-01-01

htmlabstractThis paper introduces a new algorithm dedicated to the rigorous reachability analysis of nonlinear dynamical systems. The algorithm is initially presented in the context of discrete time dynamical systems, and then extended to continuous time dynamical systems driven by ODEs. In

New algorithms for parallel MRI

International Nuclear Information System (INIS)

Anzengruber, S; Ramlau, R; Bauer, F; Leitao, A

2008-01-01

Magnetic Resonance Imaging with parallel data acquisition requires algorithms for reconstructing the patient's image from a small number of measured lines of the Fourier domain (k-space). In contrast to well-known algorithms like SENSE and GRAPPA and its flavors we consider the problem as a non-linear inverse problem. However, in order to avoid cost intensive derivatives we will use Landweber-Kaczmarz iteration and in order to improve the overall results some additional sparsity constraints.

Practical algorithms for simulation and reconstruction of digital in-line holograms.

Science.gov (United States)

Latychevskaia, Tatiana; Fink, Hans-Werner

2015-03-20

Here we present practical methods for simulation and reconstruction of in-line digital holograms recorded with plane and spherical waves. The algorithms described here are applicable to holographic imaging of an object exhibiting absorption as well as phase-shifting properties. Optimal parameters, related to distances, sampling rate, and other factors for successful simulation and reconstruction of holograms are evaluated and criteria for the achievable resolution are worked out. Moreover, we show that the numerical procedures for the reconstruction of holograms recorded with plane and spherical waves are identical under certain conditions. Experimental examples of holograms and their reconstructions are also discussed.

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

A Fourier reconstruction algorithm with constant attenuation compensation using 1800 acquisition data for SPECT

International Nuclear Information System (INIS)

Tang Qiulin; Zeng, Gengsheng L; Gullberg, Grant T

2007-01-01

In this paper, we develop an approximate analytical reconstruction algorithm that compensates for uniform attenuation in 2D parallel-beam SPECT with a 180 0 acquisition. This new algorithm is in the form of a direct Fourier reconstruction. The complex variable central slice theorem is used to derive this algorithm. The image is reconstructed with the following steps: first, the attenuated projection data acquired over 180 deg. are extended to 360 deg. and the value for the uniform attenuator is changed to a negative value. The Fourier transform (FT) of the image in polar coordinates is obtained from the Fourier transform of an analytic function interpolated from an extension of the projection data according to the complex central slice theorem. Finally, the image is obtained by performing a 2D inverse Fourier transform. Computer simulations and comparison studies with a 360 deg. full-scan algorithm are provided

The SRT reconstruction algorithm for semiquantification in PET imaging

Energy Technology Data Exchange (ETDEWEB)

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

A Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks

Directory of Open Access Journals (Sweden)

Hamidreza Mousavi

2017-01-01

Full Text Available Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN. In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple fault conditions. The algorithm uses a calibration model based on AANN. AANN can reconstruct the faulty sensor using non-faulty sensors due to correlation between the process variables, and mean of the difference between reconstructed and original data determines which sensors are faulty. The algorithms are tested on a Dimerization process. The simulation results show that the S-AANN can isolate multiple faulty sensors with low computational time that make the algorithm appropriate candidate for online applications.

Predictable nonlinear dynamics: Advances and limitations

International Nuclear Information System (INIS)

Anosov, L.A.; Butkovskii, O.Y.; Kravtsov, Y.A.; Surovyatkina, E.D.

1996-01-01

Methods for reconstruction chaotic dynamical system structure directly from experimental time series are described. Effectiveness of general methods is illustrated with the results of numerical simulation. It is of common interest that from the single time series it is possible to reconstruct a set of interconnected variables. Predictive power of dynamical models, provided by the nonlinear dynamics inverse problem solution, is limited firstly by the noise level in the system under study and is characterized by the horizon of predictability. New physical results are presented, concerning nonstationary and bifurcation nonlinear systems: (1) algorithms for revealing of nonstationarity in random-like chaotic time-series are suggested based on discriminant analysis with nonlinear discriminant function; (2) an opportunity is established to predict the final state in bifurcation system with quickly varying control parameters; (3) hysteresis is founded out in bifurcation system with quickly varying parameters; (4) delayed correlation left-angle noise-prediction error right-angle in chaotic systems is revealed. copyright 1996 American Institute of Physics

Blind spectrum reconstruction algorithm with L0-sparse representation

International Nuclear Information System (INIS)

Liu, Hai; Zhang, Zhaoli; Liu, Sanyan; Shu, Jiangbo; Liu, Tingting; Zhang, Tianxu

2015-01-01

Raman spectrum often suffers from band overlap and Poisson noise. This paper presents a new blind Poissonian Raman spectrum reconstruction method, which incorporates the L 0 -sparse prior together with the total variation constraint into the maximum a posteriori framework. Furthermore, the greedy analysis pursuit algorithm is adopted to solve the L 0 -based minimization problem. Simulated and real spectrum experimental results show that the proposed method can effectively preserve spectral structure and suppress noise. The reconstructed Raman spectra are easily used for interpreting unknown chemical mixtures. (paper)

Development of Image Reconstruction Algorithms in electrical Capacitance Tomography

International Nuclear Information System (INIS)

Fernandez Marron, J. L.; Alberdi Primicia, J.; Barcala Riveira, J. M.

2007-01-01

The Electrical Capacitance Tomography (ECT) has not obtained a good development in order to be used at industrial level. That is due first to difficulties in the measurement of very little capacitances (in the range of femto farads) and second to the problem of reconstruction on- line of the images. This problem is due also to the small numbers of electrodes (maximum 16), that made the usual algorithms of reconstruction has many errors. In this work it is described a new purely geometrical method that could be used for this purpose. (Author) 4 refs

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

Science.gov (United States)

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)

New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers.

Science.gov (United States)

Rodriguez-Ruiz, Alejandro; Teuwen, Jonas; Vreemann, Suzan; Bouwman, Ramona W; van Engen, Ruben E; Karssemeijer, Nico; Mann, Ritse M; Gubern-Merida, Albert; Sechopoulos, Ioannis

2017-01-01

Background The image quality of digital breast tomosynthesis (DBT) volumes depends greatly on the reconstruction algorithm. Purpose To compare two DBT reconstruction algorithms used by the Siemens Mammomat Inspiration system, filtered back projection (FBP), and FBP with iterative optimizations (EMPIRE), using qualitative analysis by human readers and detection performance of machine learning algorithms. Material and Methods Visual grading analysis was performed by four readers specialized in breast imaging who scored 100 cases reconstructed with both algorithms (70 lesions). Scoring (5-point scale: 1 = poor to 5 = excellent quality) was performed on presence of noise and artifacts, visualization of skin-line and Cooper's ligaments, contrast, and image quality, and, when present, lesion visibility. In parallel, a three-dimensional deep-learning convolutional neural network (3D-CNN) was trained (n = 259 patients, 51 positives with BI-RADS 3, 4, or 5 calcifications) and tested (n = 46 patients, nine positives), separately with FBP and EMPIRE volumes, to discriminate between samples with and without calcifications. The partial area under the receiver operating characteristic curve (pAUC) of each 3D-CNN was used for comparison. Results EMPIRE reconstructions showed better contrast (3.23 vs. 3.10, P = 0.010), image quality (3.22 vs. 3.03, P algorithm provides DBT volumes with better contrast and image quality, fewer artifacts, and improved visibility of calcifications for human observers, as well as improved detection performance with deep-learning algorithms.

Analytical fan-beam and cone-beam reconstruction algorithms with uniform attenuation correction for SPECT

International Nuclear Information System (INIS)

Tang Qiulin; Zeng, Gengsheng L; Gullberg, Grant T

2005-01-01

In this paper, we developed an analytical fan-beam reconstruction algorithm that compensates for uniform attenuation in SPECT. The new fan-beam algorithm is in the form of backprojection first, then filtering, and is mathematically exact. The algorithm is based on three components. The first one is the established generalized central-slice theorem, which relates the 1D Fourier transform of a set of arbitrary data and the 2D Fourier transform of the backprojected image. The second one is the fact that the backprojection of the fan-beam measurements is identical to the backprojection of the parallel measurements of the same object with the same attenuator. The third one is the stable analytical reconstruction algorithm for uniformly attenuated Radon data, developed by Metz and Pan. The fan-beam algorithm is then extended into a cone-beam reconstruction algorithm, where the orbit of the focal point of the cone-beam imaging geometry is a circle. This orbit geometry does not satisfy Tuy's condition and the obtained cone-beam algorithm is an approximation. In the cone-beam algorithm, the cone-beam data are first backprojected into the 3D image volume; then a slice-by-slice filtering is performed. This slice-by-slice filtering procedure is identical to that of the fan-beam algorithm. Both the fan-beam and cone-beam algorithms are efficient, and computer simulations are presented. The new cone-beam algorithm is compared with Bronnikov's cone-beam algorithm, and it is shown to have better performance with noisy projections

A fully three-dimensional reconstruction algorithm with the nonstationary filter for improved single-orbit cone beam SPECT

International Nuclear Information System (INIS)

Cao, Z.J.; Tsui, B.M.

1993-01-01

Conventional single-orbit cone beam tomography presents special problems. They include incomplete sampling and inadequate three-dimensional (3D) reconstruction algorithm. The commonly used Feldkamp reconstruction algorithm simply extends the two-dimensional (2D) fan beam algorithm to 3D cone beam geometry. A truly 3D reconstruction formulation has been derived for the single-orbit cone beam SPECT based on the 3D Fourier slice theorem. In the formulation, a nonstationary filter which depends on the distance from the central plane of the cone beam was derived. The filter is applied to the 2D projection data in directions along and normal to the axis-of-rotation. The 3D reconstruction algorithm with the nonstationary filter was evaluated using both computer simulation and experimental measurements. Significant improvement in image quality was demonstrated in terms of decreased artifacts and distortions in cone beam reconstructed images. However, compared with the Feldkamp algorithm, a five-fold increase in processing time is required. Further improvement in image quality needs complete sampling in frequency space

Incorporation of local dependent reliability information into the Prior Image Constrained Compressed Sensing (PICCS) reconstruction algorithm

International Nuclear Information System (INIS)

Vaegler, Sven; Sauer, Otto; Stsepankou, Dzmitry; Hesser, Juergen

2015-01-01

The reduction of dose in cone beam computer tomography (CBCT) arises from the decrease of the tube current for each projection as well as from the reduction of the number of projections. In order to maintain good image quality, sophisticated image reconstruction techniques are required. The Prior Image Constrained Compressed Sensing (PICCS) incorporates prior images into the reconstruction algorithm and outperforms the widespread used Feldkamp-Davis-Kress-algorithm (FDK) when the number of projections is reduced. However, prior images that contain major variations are not appropriately considered so far in PICCS. We therefore propose the partial-PICCS (pPICCS) algorithm. This framework is a problem-specific extension of PICCS and enables the incorporation of the reliability of the prior images additionally. We assumed that the prior images are composed of areas with large and small deviations. Accordingly, a weighting matrix considered the assigned areas in the objective function. We applied our algorithm to the problem of image reconstruction from few views by simulations with a computer phantom as well as on clinical CBCT projections from a head-and-neck case. All prior images contained large local variations. The reconstructed images were compared to the reconstruction results by the FDK-algorithm, by Compressed Sensing (CS) and by PICCS. To show the gain of image quality we compared image details with the reference image and used quantitative metrics (root-mean-square error (RMSE), contrast-to-noise-ratio (CNR)). The pPICCS reconstruction framework yield images with substantially improved quality even when the number of projections was very small. The images contained less streaking, blurring and inaccurately reconstructed structures compared to the images reconstructed by FDK, CS and conventional PICCS. The increased image quality is also reflected in large RMSE differences. We proposed a modification of the original PICCS algorithm. The pPICCS algorithm

3D noise power spectrum applied on clinical MDCT scanners: effects of reconstruction algorithms and reconstruction filters

Science.gov (United States)

Miéville, Frédéric A.; Bolard, Gregory; Benkreira, Mohamed; Ayestaran, Paul; Gudinchet, François; Bochud, François; Verdun, Francis R.

2011-03-01

The noise power spectrum (NPS) is the reference metric for understanding the noise content in computed tomography (CT) images. To evaluate the noise properties of clinical multidetector (MDCT) scanners, local 2D and 3D NPSs were computed for different acquisition reconstruction parameters. A 64- and a 128-MDCT scanners were employed. Measurements were performed on a water phantom in axial and helical acquisition modes. CT dose index was identical for both installations. Influence of parameters such as the pitch, the reconstruction filter (soft, standard and bone) and the reconstruction algorithm (filtered-back projection (FBP), adaptive statistical iterative reconstruction (ASIR)) were investigated. Images were also reconstructed in the coronal plane using a reformat process. Then 2D and 3D NPS methods were computed. In axial acquisition mode, the 2D axial NPS showed an important magnitude variation as a function of the z-direction when measured at the phantom center. In helical mode, a directional dependency with lobular shape was observed while the magnitude of the NPS was kept constant. Important effects of the reconstruction filter, pitch and reconstruction algorithm were observed on 3D NPS results for both MDCTs. With ASIR, a reduction of the NPS magnitude and a shift of the NPS peak to the low frequency range were visible. 2D coronal NPS obtained from the reformat images was impacted by the interpolation when compared to 2D coronal NPS obtained from 3D measurements. The noise properties of volume measured in last generation MDCTs was studied using local 3D NPS metric. However, impact of the non-stationarity noise effect may need further investigations.

Improved iterative image reconstruction algorithm for the exterior problem of computed tomography

International Nuclear Information System (INIS)

Guo, Yumeng; Zeng, Li

2017-01-01

In industrial applications that are limited by the angle of a fan-beam and the length of a detector, the exterior problem of computed tomography (CT) uses only the projection data that correspond to the external annulus of the objects to reconstruct an image. Because the reconstructions are not affected by the projection data that correspond to the interior of the objects, the exterior problem is widely applied to detect cracks in the outer wall of large-sized objects, such as in-service pipelines. However, image reconstruction in the exterior problem is still a challenging problem due to truncated projection data and beam-hardening, both of which can lead to distortions and artifacts. Thus, developing an effective algorithm and adopting a scanning trajectory suited for the exterior problem may be valuable. In this study, an improved iterative algorithm that combines total variation minimization (TVM) with a region scalable fitting (RSF) model was developed for a unilateral off-centered scanning trajectory and can be utilized to inspect large-sized objects for defects. Experiments involving simulated phantoms and real projection data were conducted to validate the practicality of our algorithm. Furthermore, comparative experiments show that our algorithm outperforms others in suppressing the artifacts caused by truncated projection data and beam-hardening.

Improved iterative image reconstruction algorithm for the exterior problem of computed tomography

Energy Technology Data Exchange (ETDEWEB)

Guo, Yumeng [Chongqing University, College of Mathematics and Statistics, Chongqing 401331 (China); Chongqing University, ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing 400044 (China); Zeng, Li, E-mail: drlizeng@cqu.edu.cn [Chongqing University, College of Mathematics and Statistics, Chongqing 401331 (China); Chongqing University, ICT Research Center, Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing 400044 (China)

2017-01-11

In industrial applications that are limited by the angle of a fan-beam and the length of a detector, the exterior problem of computed tomography (CT) uses only the projection data that correspond to the external annulus of the objects to reconstruct an image. Because the reconstructions are not affected by the projection data that correspond to the interior of the objects, the exterior problem is widely applied to detect cracks in the outer wall of large-sized objects, such as in-service pipelines. However, image reconstruction in the exterior problem is still a challenging problem due to truncated projection data and beam-hardening, both of which can lead to distortions and artifacts. Thus, developing an effective algorithm and adopting a scanning trajectory suited for the exterior problem may be valuable. In this study, an improved iterative algorithm that combines total variation minimization (TVM) with a region scalable fitting (RSF) model was developed for a unilateral off-centered scanning trajectory and can be utilized to inspect large-sized objects for defects. Experiments involving simulated phantoms and real projection data were conducted to validate the practicality of our algorithm. Furthermore, comparative experiments show that our algorithm outperforms others in suppressing the artifacts caused by truncated projection data and beam-hardening.

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)

Sparse Reconstruction Schemes for Nonlinear Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla

2016-03-01

Electromagnetic imaging is the problem of determining material properties from scattered fields measured away from the domain under investigation. Solving this inverse problem is a challenging task because (i) it is ill-posed due to the presence of (smoothing) integral operators used in the representation of scattered fields in terms of material properties, and scattered fields are obtained at a finite set of points through noisy measurements; and (ii) it is nonlinear simply due the fact that scattered fields are nonlinear functions of the material properties. The work described in this thesis tackles the ill-posedness of the electromagnetic imaging problem using sparsity-based regularization techniques, which assume that the scatterer(s) occupy only a small fraction of the investigation domain. More specifically, four novel imaging methods are formulated and implemented. (i) Sparsity-regularized Born iterative method iteratively linearizes the nonlinear inverse scattering problem and each linear problem is regularized using an improved iterative shrinkage algorithm enforcing the sparsity constraint. (ii) Sparsity-regularized nonlinear inexact Newton method calls for the solution of a linear system involving the Frechet derivative matrix of the forward scattering operator at every iteration step. For faster convergence, the solution of this matrix system is regularized under the sparsity constraint and preconditioned by leveling the matrix singular values. (iii) Sparsity-regularized nonlinear Tikhonov method directly solves the nonlinear minimization problem using Landweber iterations, where a thresholding function is applied at every iteration step to enforce the sparsity constraint. (iv) This last scheme is accelerated using a projected steepest descent method when it is applied to three-dimensional investigation domains. Projection replaces the thresholding operation and enforces the sparsity constraint. Numerical experiments, which are carried out using

An FDK-like cone-beam SPECT reconstruction algorithm for non-uniform attenuated projections acquired using a circular trajectory

International Nuclear Information System (INIS)

Huang, Q; Zeng, G L; You, J; Gullberg, G T

2005-01-01

In this paper, Novikov's inversion formula of the attenuated two-dimensional (2D) Radon transform is applied to the reconstruction of attenuated fan-beam projections acquired with equal detector spacing and of attenuated cone-beam projections acquired with a flat planar detector and circular trajectory. The derivation of the fan-beam algorithm is obtained by transformation from parallel-beam coordinates to fan-beam coordinates. The cone-beam reconstruction algorithm is an extension of the fan-beam reconstruction algorithm using Feldkamp-Davis-Kress's (FDK) method. Computer simulations indicate that the algorithm is efficient and is accurate in reconstructing slices close to the central slice of the cone-beam orbit plane. When the attenuation map is set to zero the implementation is equivalent to the FDK method. Reconstructed images are also shown for noise corrupted projections

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.

Performance of 3DOSEM and MAP algorithms for reconstructing low count SPECT acquisitions

Energy Technology Data Exchange (ETDEWEB)

Grootjans, Willem [Radboud Univ. Medical Center, Nijmegen (Netherlands). Dept. of Radiology and Nuclear Medicine; Leiden Univ. Medical Center (Netherlands). Dept. of Radiology; Meeuwis, Antoi P.W.; Gotthardt, Martin; Visser, Eric P. [Radboud Univ. Medical Center, Nijmegen (Netherlands). Dept. of Radiology and Nuclear Medicine; Slump, Cornelis H. [Univ. Twente, Enschede (Netherlands). MIRA Inst. for Biomedical Technology and Technical Medicine; Geus-Oei, Lioe-Fee de [Radboud Univ. Medical Center, Nijmegen (Netherlands). Dept. of Radiology and Nuclear Medicine; Univ. Twente, Enschede (Netherlands). MIRA Inst. for Biomedical Technology and Technical Medicine; Leiden Univ. Medical Center (Netherlands). Dept. of Radiology

2016-07-01

Low count single photon emission computed tomography (SPECT) is becoming more important in view of whole body SPECT and reduction of radiation dose. In this study, we investigated the performance of several 3D ordered subset expectation maximization (3DOSEM) and maximum a posteriori (MAP) algorithms for reconstructing low count SPECT images. Phantom experiments were conducted using the National Electrical Manufacturers Association (NEMA) NU2 image quality (IQ) phantom. The background compartment of the phantom was filled with varying concentrations of pertechnetate and indiumchloride, simulating various clinical imaging conditions. Images were acquired using a hybrid SPECT/CT scanner and reconstructed with 3DOSEM and MAP reconstruction algorithms implemented in Siemens Syngo MI.SPECT (Flash3D) and Hermes Hybrid Recon Oncology (Hyrid Recon 3DOSEM and MAP). Image analysis was performed by calculating the contrast recovery coefficient (CRC),percentage background variability (N%), and contrast-to-noise ratio (CNR), defined as the ratio between CRC and N%. Furthermore, image distortion is characterized by calculating the aspect ratio (AR) of ellipses fitted to the hot spheres. Additionally, the performance of these algorithms to reconstruct clinical images was investigated. Images reconstructed with 3DOSEM algorithms demonstrated superior image quality in terms of contrast and resolution recovery when compared to images reconstructed with filtered-back-projection (FBP), OSEM and 2DOSEM. However, occurrence of correlated noise patterns and image distortions significantly deteriorated the quality of 3DOSEM reconstructed images. The mean AR for the 37, 28, 22, and 17 mm spheres was 1.3, 1.3, 1.6, and 1.7 respectively. The mean N% increase in high and low count Flash3D and Hybrid Recon 3DOSEM from 5.9% and 4.0% to 11.1% and 9.0%, respectively. Similarly, the mean CNR decreased in high and low count Flash3D and Hybrid Recon 3DOSEM from 8.7 and 8.8 to 3.6 and 4

The born approximation and Calderón's method for reconstruction of conductivities in 3-D

DEFF Research Database (Denmark)

Knudsen, Kim; Mueller, Jennifer L.

2011-01-01

Two algorithms for the direct reconstruction of conductivities in a bounded domain in [\\mathbb{R}^3] from surface measurements of the solutions to the conductivity equation are presented. The algorithms are based on complex geometrical optics solutions and a nonlinear scattering transform. We test...

Sequential reconstruction of driving-forces from nonlinear nonstationary dynamics

Science.gov (United States)

Güntürkün, Ulaş

2010-07-01

This paper describes a functional analysis-based method for the estimation of driving-forces from nonlinear dynamic systems. The driving-forces account for the perturbation inputs induced by the external environment or the secular variations in the internal variables of the system. The proposed algorithm is applicable to the problems for which there is too little or no prior knowledge to build a rigorous mathematical model of the unknown dynamics. We derive the estimator conditioned on the differentiability of the unknown system’s mapping, and smoothness of the driving-force. The proposed algorithm is an adaptive sequential realization of the blind prediction error method, where the basic idea is to predict the observables, and retrieve the driving-force from the prediction error. Our realization of this idea is embodied by predicting the observables one-step into the future using a bank of echo state networks (ESN) in an online fashion, and then extracting the raw estimates from the prediction error and smoothing these estimates in two adaptive filtering stages. The adaptive nature of the algorithm enables to retrieve both slowly and rapidly varying driving-forces accurately, which are illustrated by simulations. Logistic and Moran-Ricker maps are studied in controlled experiments, exemplifying chaotic state and stochastic measurement models. The algorithm is also applied to the estimation of a driving-force from another nonlinear dynamic system that is stochastic in both state and measurement equations. The results are judged by the posterior Cramer-Rao lower bounds. The method is finally put into test on a real-world application; extracting sun’s magnetic flux from the sunspot time series.

A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

Science.gov (United States)

Wu, Wei; Zhao, Dewei; Zhang, Huan

2015-12-01

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

Optimization of CT image reconstruction algorithms for the lung tissue research consortium (LTRC)

Science.gov (United States)

McCollough, Cynthia; Zhang, Jie; Bruesewitz, Michael; Bartholmai, Brian

2006-03-01

To create a repository of clinical data, CT images and tissue samples and to more clearly understand the pathogenetic features of pulmonary fibrosis and emphysema, the National Heart, Lung, and Blood Institute (NHLBI) launched a cooperative effort known as the Lung Tissue Resource Consortium (LTRC). The CT images for the LTRC effort must contain accurate CT numbers in order to characterize tissues, and must have high-spatial resolution to show fine anatomic structures. This study was performed to optimize the CT image reconstruction algorithms to achieve these criteria. Quantitative analyses of phantom and clinical images were conducted. The ACR CT accreditation phantom containing five regions of distinct CT attenuations (CT numbers of approximately -1000 HU, -80 HU, 0 HU, 130 HU and 900 HU), and a high-contrast spatial resolution test pattern, was scanned using CT systems from two manufacturers (General Electric (GE) Healthcare and Siemens Medical Solutions). Phantom images were reconstructed using all relevant reconstruction algorithms. Mean CT numbers and image noise (standard deviation) were measured and compared for the five materials. Clinical high-resolution chest CT images acquired on a GE CT system for a patient with diffuse lung disease were reconstructed using BONE and STANDARD algorithms and evaluated by a thoracic radiologist in terms of image quality and disease extent. The clinical BONE images were processed with a 3 x 3 x 3 median filter to simulate a thicker slice reconstructed in smoother algorithms, which have traditionally been proven to provide an accurate estimation of emphysema extent in the lungs. Using a threshold technique, the volume of emphysema (defined as the percentage of lung voxels having a CT number lower than -950 HU) was computed for the STANDARD, BONE, and BONE filtered. The CT numbers measured in the ACR CT Phantom images were accurate for all reconstruction kernels for both manufacturers. As expected, visual evaluation of the

On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning.

Science.gov (United States)

Mizutani, Eiji; Demmel, James W

2003-01-01

This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).

A modified sparse reconstruction method for three-dimensional synthetic aperture radar image

Science.gov (United States)

Zhang, Ziqiang; Ji, Kefeng; Song, Haibo; Zou, Huanxin

2018-03-01

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l0 norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l0 norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

Direct numerical reconstruction of conductivities in three dimensions using scattering transforms

DEFF Research Database (Denmark)

Bikowski, Jutta; Knudsen, Kim; Mueller, Jennifer L

2011-01-01

A direct three-dimensional EIT reconstruction algorithm based on complex geometrical optics solutions and a nonlinear scattering transform is presented and implemented for spherically symmetric conductivity distributions. The scattering transform is computed both with a Born approximation and from...

An efficient reconstruction algorithm for differential phase-contrast tomographic images from a limited number of views

International Nuclear Information System (INIS)

Sunaguchi, Naoki; Yuasa, Tetsuya; Gupta, Rajiv; Ando, Masami

2015-01-01

The main focus of this paper is reconstruction of tomographic phase-contrast image from a set of projections. We propose an efficient reconstruction algorithm for differential phase-contrast computed tomography that can considerably reduce the number of projections required for reconstruction. The key result underlying this research is a projection theorem that states that the second derivative of the projection set is linearly related to the Laplacian of the tomographic image. The proposed algorithm first reconstructs the Laplacian image of the phase-shift distribution from the second-derivative of the projections using total variation regularization. The second step is to obtain the phase-shift distribution by solving a Poisson equation whose source is the Laplacian image previously reconstructed under the Dirichlet condition. We demonstrate the efficacy of this algorithm using both synthetically generated simulation data and projection data acquired experimentally at a synchrotron. The experimental phase data were acquired from a human coronary artery specimen using dark-field-imaging optics pioneered by our group. Our results demonstrate that the proposed algorithm can reduce the number of projections to approximately 33% as compared with the conventional filtered backprojection method, without any detrimental effect on the image quality

Layer-oriented multigrid wavefront reconstruction algorithms for multi-conjugate adaptive optics

Science.gov (United States)

Gilles, Luc; Ellerbroek, Brent L.; Vogel, Curtis R.

2003-02-01

Multi-conjugate adaptive optics (MCAO) systems with 104-105 degrees of freedom have been proposed for future giant telescopes. Using standard matrix methods to compute, optimize, and implement wavefront control algorithms for these systems is impractical, since the number of calculations required to compute and apply the reconstruction matrix scales respectively with the cube and the square of the number of AO degrees of freedom. In this paper, we develop an iterative sparse matrix implementation of minimum variance wavefront reconstruction for telescope diameters up to 32m with more than 104 actuators. The basic approach is the preconditioned conjugate gradient method, using a multigrid preconditioner incorporating a layer-oriented (block) symmetric Gauss-Seidel iterative smoothing operator. We present open-loop numerical simulation results to illustrate algorithm convergence.

An efficient control algorithm for nonlinear systems

International Nuclear Information System (INIS)

Sinha, S.

1990-12-01

We suggest a scheme to step up the efficiency of a recently proposed adaptive control algorithm, which is remarkably effective for regulating nonlinear systems. The technique involves monitoring of the ''stiffness of control'' to get maximum gain while maintaining a predetermined accuracy. The success of the procedure is demonstrated for the case of the logistic map, where we show that the improvement in performance is often factors of tens, and for small control stiffness, even factors of hundreds. (author). 4 refs, 1 fig., 1 tab

A three-dimensional-weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT-helical scanning

International Nuclear Information System (INIS)

Tang Xiangyang; Hsieh Jiang; Nilsen, Roy A; Dutta, Sandeep; Samsonov, Dmitry; Hagiwara, Akira

2006-01-01

Based on the structure of the original helical FDK algorithm, a three-dimensional (3D)-weighted cone beam filtered backprojection (CB-FBP) algorithm is proposed for image reconstruction in volumetric CT under helical source trajectory. In addition to its dependence on view and fan angles, the 3D weighting utilizes the cone angle dependency of a ray to improve reconstruction accuracy. The 3D weighting is ray-dependent and the underlying mechanism is to give a favourable weight to the ray with the smaller cone angle out of a pair of conjugate rays but an unfavourable weight to the ray with the larger cone angle out of the conjugate ray pair. The proposed 3D-weighted helical CB-FBP reconstruction algorithm is implemented in the cone-parallel geometry that can improve noise uniformity and image generation speed significantly. Under the cone-parallel geometry, the filtering is naturally carried out along the tangential direction of the helical source trajectory. By exploring the 3D weighting's dependence on cone angle, the proposed helical 3D-weighted CB-FBP reconstruction algorithm can provide significantly improved reconstruction accuracy at moderate cone angle and high helical pitches. The 3D-weighted CB-FBP algorithm is experimentally evaluated by computer-simulated phantoms and phantoms scanned by a diagnostic volumetric CT system with a detector dimension of 64 x 0.625 mm over various helical pitches. The computer simulation study shows that the 3D weighting enables the proposed algorithm to reach reconstruction accuracy comparable to that of exact CB reconstruction algorithms, such as the Katsevich algorithm, under a moderate cone angle (4 deg.) and various helical pitches. Meanwhile, the experimental evaluation using the phantoms scanned by a volumetric CT system shows that the spatial resolution along the z-direction and noise characteristics of the proposed 3D-weighted helical CB-FBP reconstruction algorithm are maintained very well in comparison to the FDK

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

International Nuclear Information System (INIS)

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

1990-01-01

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

A tracking algorithm for the reconstruction of the daughters of long-lived particles in LHCb

CERN Document Server

Dendek, Adam Mateusz

2018-01-01

A tracking algorithm for the reconstruction of the daughters of long-lived particles in LHCb 5 Jun 2018, 16:00 1h 30m Library, Centro San Domenico () LHC experiments Posters session Speaker Katharina Mueller (Universitaet Zuerich (CH)) Description The LHCb experiment at CERN operates a high precision and robust tracking system to reach its physics goals, including precise measurements of CP-violation phenomena in the heavy flavour quark sector and searches for New Physics beyond the Standard Model. The track reconstruction procedure is performed by a number of algorithms. One of these, PatLongLivedTracking, is optimised to reconstruct "downstream tracks", which are tracks originating from decays outside the LHCb vertex detector of long-lived particles, such as Ks or Λ0. After an overview of the LHCb tracking system, we provide a detailed description of the LHCb downstream track reconstruction algorithm. Its computational intelligence part is described in details, including the adaptation of the employed...

An improved cone-beam filtered backprojection reconstruction algorithm based on x-ray angular correction and multiresolution analysis

International Nuclear Information System (INIS)

Sun, Y.; Hou, Y.; Yan, Y.

2004-01-01

With the extensive application of industrial computed tomography in the field of non-destructive testing, how to improve the quality of the reconstructed image is receiving more and more concern. It is well known that in the existing cone-beam filtered backprojection reconstruction algorithms the cone angle is controlled within a narrow range. The reason of this limitation is the incompleteness of projection data when the cone angle increases. Thus the size of the tested workpiece is limited. Considering the characteristic of X-ray cone angle, an improved cone-beam filtered back-projection reconstruction algorithm taking account of angular correction is proposed in this paper. The aim of our algorithm is to correct the cone-angle effect resulted from the incompleteness of projection data in the conventional algorithm. The basis of the correction is the angular relationship among X-ray source, tested workpiece and the detector. Thus the cone angle is not strictly limited and this algorithm may be used to detect larger workpiece. Further more, adaptive wavelet filter is used to make multiresolution analysis, which can modify the wavelet decomposition series adaptively according to the demand for resolution of local reconstructed area. Therefore the computation and the time of reconstruction can be reduced, and the quality of the reconstructed image can also be improved. (author)

Study on the effects of sample selection on spectral reflectance reconstruction based on the algorithm of compressive sensing

International Nuclear Information System (INIS)

Zhang, Leihong; Liang, Dong

2016-01-01

In order to solve the problem that reconstruction efficiency and precision is not high, in this paper different samples are selected to reconstruct spectral reflectance, and a new kind of spectral reflectance reconstruction method based on the algorithm of compressive sensing is provided. Four different color numbers of matte color cards such as the ColorChecker Color Rendition Chart and Color Checker SG, the copperplate paper spot color card of Panton, and the Munsell colors card are chosen as training samples, the spectral image is reconstructed respectively by the algorithm of compressive sensing and pseudo-inverse and Wiener, and the results are compared. These methods of spectral reconstruction are evaluated by root mean square error and color difference accuracy. The experiments show that the cumulative contribution rate and color difference of the Munsell colors card are better than those of the other three numbers of color cards in the same conditions of reconstruction, and the accuracy of the spectral reconstruction will be affected by the training sample of different numbers of color cards. The key technology of reconstruction means that the uniformity and representation of the training sample selection has important significance upon reconstruction. In this paper, the influence of the sample selection on the spectral image reconstruction is studied. The precision of the spectral reconstruction based on the algorithm of compressive sensing is higher than that of the traditional algorithm of spectral reconstruction. By the MATLAB simulation results, it can be seen that the spectral reconstruction precision and efficiency are affected by the different color numbers of the training sample. (paper)

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

Identifying Septal Support Reconstructions for Saddle Nose Deformity: The Cakmak Algorithm.

Science.gov (United States)

Cakmak, Ozcan; Emre, Ismet Emrah; Ozkurt, Fazil Emre

2015-01-01

The saddle nose deformity is one of the most challenging problems in nasal surgery with a less predictable and reproducible result than other nasal procedures. The main feature of this deformity is loss of septal support with both functional and aesthetic implications. Most reports on saddle nose have focused on aesthetic improvement and neglected the reestablishment of septal support to improve airway. To explain how the Cakmak algorithm, an algorithm that describes various fixation techniques and grafts in different types of saddle nose deformities, aids in identifying saddle nose reconstructions that restore supportive nasal framework and provide the aesthetic improvements typically associated with procedures to correct saddle nose deformities. This algorithm presents septal support reconstruction of patients with saddle nose deformity based on the experience of the senior author in 206 patients with saddle nose deformity. Preoperative examination, intraoperative assessment, reconstruction techniques, graft materials, and patient evaluation of aesthetic success were documented, and 4 different types of saddle nose deformities were defined. The Cakmak algorithm classifies varying degrees of saddle nose deformity from type 0 to type 4 and helps identify the most appropriate surgical procedure to restore the supportive nasal framework and aesthetic dorsum. Among the 206 patients, 110 women and 96 men, mean (range) age was 39.7 years (15-68 years), and mean (range) of follow-up was 32 months (6-148 months). All but 12 patients had a history of previous nasal surgeries. Application of the Cakmak algorithm resulted in 36 patients categorized with type 0 saddle nose deformities; 79, type 1; 50, type 2; 20, type 3a; 7, type 3b; and 14, type 4. Postoperative photographs showed improvement of deformities, and patient surveys revealed aesthetic improvement in 201 patients and improvement in nasal breathing in 195 patients. Three patients developed postoperative infection

Algorithm for three dimension reconstruction of magnetic resonance tomographs and X-ray images based on Fast Fourier Transform

International Nuclear Information System (INIS)

Bueno, Josiane M.; Traina, Agma Juci M.; Cruvinel, Paulo E.

1995-01-01

This work presents an algorithm for three-dimensional digital image reconstruction. Such algorithms based on the combination of both a Fast Fourier Transform method with Hamming Window and the use of a tri-linear interpolation function. The algorithm allows not only the generation of three-dimensional spatial spin distribution maps for Magnetic Resonance Tomography data but also X and Y-rays linear attenuation coefficient maps for CT scanners. Results demonstrates the usefulness of the algorithm in three-dimensional image reconstruction by doing first two-dimensional reconstruction and rather after interpolation. The algorithm was developed in C++ language, and there are two available versions: one under the DOS environment, and the other under the UNIX/Sun environment. (author)

Stack emission monitoring using non-dispersive infrared spectroscopy with an optimized nonlinear absorption cross interference correction algorithm

Directory of Open Access Journals (Sweden)

Y. W. Sun

2013-08-01

Full Text Available In this paper, we present an optimized analysis algorithm for non-dispersive infrared (NDIR to in situ monitor stack emissions. The proposed algorithm simultaneously compensates for nonlinear absorption and cross interference among different gases. We present a mathematical derivation for the measurement error caused by variations in interference coefficients when nonlinear absorption occurs. The proposed algorithm is derived from a classical one and uses interference functions to quantify cross interference. The interference functions vary proportionally with the nonlinear absorption. Thus, interference coefficients among different gases can be modeled by the interference functions whether gases are characterized by linear or nonlinear absorption. In this study, the simultaneous analysis of two components (CO2 and CO serves as an example for the validation of the proposed algorithm. The interference functions in this case can be obtained by least-squares fitting with third-order polynomials. Experiments show that the results of cross interference correction are improved significantly by utilizing the fitted interference functions when nonlinear absorptions occur. The dynamic measurement ranges of CO2 and CO are improved by about a factor of 1.8 and 3.5, respectively. A commercial analyzer with high accuracy was used to validate the CO and CO2 measurements derived from the NDIR analyzer prototype in which the new algorithm was embedded. The comparison of the two analyzers show that the prototype works well both within the linear and nonlinear ranges.

Convergence of iterative image reconstruction algorithms for Digital Breast Tomosynthesis

DEFF Research Database (Denmark)

Sidky, Emil; Jørgensen, Jakob Heide; Pan, Xiaochuan

2012-01-01

Most iterative image reconstruction algorithms are based on some form of optimization, such as minimization of a data-fidelity term plus an image regularizing penalty term. While achieving the solution of these optimization problems may not directly be clinically relevant, accurate optimization s...

Genetic algorithms applied to nonlinear and complex domains

International Nuclear Information System (INIS)

Barash, D; Woodin, A E

1999-01-01

The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a ''final'' result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means

Algorithms of estimation for nonlinear systems a differential and algebraic viewpoint

CERN Document Server

Martínez-Guerra, Rafael

2017-01-01

This book acquaints readers with recent developments in dynamical systems theory and its applications, with a strong focus on the control and estimation of nonlinear systems. Several algorithms are proposed and worked out for a set of model systems, in particular so-called input-affine or bilinear systems, which can serve to approximate a wide class of nonlinear control systems. These can either take the form of state space models or be represented by an input-output equation. The approach taken here further highlights the role of modern mathematical and conceptual tools, including differential algebraic theory, observer design for nonlinear systems and generalized canonical forms.

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....

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

International Nuclear Information System (INIS)

Quan, E; Lalush, D S

2009-01-01

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

Improved Wallis Dodging Algorithm for Large-Scale Super-Resolution Reconstruction Remote Sensing Images

Directory of Open Access Journals (Sweden)

Chong Fan

2017-03-01

Full Text Available A sub-block algorithm is usually applied in the super-resolution (SR reconstruction of images because of limitations in computer memory. However, the sub-block SR images can hardly achieve a seamless image mosaicking because of the uneven distribution of brightness and contrast among these sub-blocks. An effectively improved weighted Wallis dodging algorithm is proposed, aiming at the characteristic that SR reconstructed images are gray images with the same size and overlapping region. This algorithm can achieve consistency of image brightness and contrast. Meanwhile, a weighted adjustment sequence is presented to avoid the spatial propagation and accumulation of errors and the loss of image information caused by excessive computation. A seam line elimination method can share the partial dislocation in the seam line to the entire overlapping region with a smooth transition effect. Subsequently, the improved method is employed to remove the uneven illumination for 900 SR reconstructed images of ZY-3. Then, the overlapping image mosaic method is adopted to accomplish a seamless image mosaic based on the optimal seam line.

Optimization of Proton CT Detector System and Image Reconstruction Algorithm for On-Line Proton Therapy.

Directory of Open Access Journals (Sweden)

Chae Young Lee

Full Text Available The purposes of this study were to optimize a proton computed tomography system (pCT for proton range verification and to confirm the pCT image reconstruction algorithm based on projection images generated with optimized parameters. For this purpose, we developed a new pCT scanner using the Geometry and Tracking (GEANT 4.9.6 simulation toolkit. GEANT4 simulations were performed to optimize the geometric parameters representing the detector thickness and the distance between the detectors for pCT. The system consisted of four silicon strip detectors for particle tracking and a calorimeter to measure the residual energies of the individual protons. The optimized pCT system design was then adjusted to ensure that the solution to a CS-based convex optimization problem would converge to yield the desired pCT images after a reasonable number of iterative corrections. In particular, we used a total variation-based formulation that has been useful in exploiting prior knowledge about the minimal variations of proton attenuation characteristics in the human body. Examinations performed using our CS algorithm showed that high-quality pCT images could be reconstructed using sets of 72 projections within 20 iterations and without any streaks or noise, which can be caused by under-sampling and proton starvation. Moreover, the images yielded by this CS algorithm were found to be of higher quality than those obtained using other reconstruction algorithms. The optimized pCT scanner system demonstrated the potential to perform high-quality pCT during on-line image-guided proton therapy, without increasing the imaging dose, by applying our CS based proton CT reconstruction algorithm. Further, we make our optimized detector system and CS-based proton CT reconstruction algorithm potentially useful in on-line proton therapy.

A reconstruction algorithm for three-dimensional object-space data using spatial-spectral multiplexing

Science.gov (United States)

Wu, Zhejun; Kudenov, Michael W.

2017-05-01

This paper presents a reconstruction algorithm for the Spatial-Spectral Multiplexing (SSM) optical system. The goal of this algorithm is to recover the three-dimensional spatial and spectral information of a scene, given that a one-dimensional spectrometer array is used to sample the pupil of the spatial-spectral modulator. The challenge of the reconstruction is that the non-parametric representation of the three-dimensional spatial and spectral object requires a large number of variables, thus leading to an underdetermined linear system that is hard to uniquely recover. We propose to reparameterize the spectrum using B-spline functions to reduce the number of unknown variables. Our reconstruction algorithm then solves the improved linear system via a least- square optimization of such B-spline coefficients with additional spatial smoothness regularization. The ground truth object and the optical model for the measurement matrix are simulated with both spatial and spectral assumptions according to a realistic field of view. In order to test the robustness of the algorithm, we add Poisson noise to the measurement and test on both two-dimensional and three-dimensional spatial and spectral scenes. Our analysis shows that the root mean square error of the recovered results can be achieved within 5.15%.

A compressed sensing based reconstruction algorithm for synchrotron source propagation-based X-ray phase contrast computed tomography

Energy Technology Data Exchange (ETDEWEB)

Melli, Seyed Ali, E-mail: sem649@mail.usask.ca [Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK (Canada); Wahid, Khan A. [Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK (Canada); Babyn, Paul [Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK (Canada); Montgomery, James [College of Medicine, University of Saskatchewan, Saskatoon, SK (Canada); Snead, Elisabeth [Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK (Canada); El-Gayed, Ali [College of Medicine, University of Saskatchewan, Saskatoon, SK (Canada); Pettitt, Murray; Wolkowski, Bailey [College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK (Canada); Wesolowski, Michal [Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK (Canada)

2016-01-11

Synchrotron source propagation-based X-ray phase contrast computed tomography is increasingly used in pre-clinical imaging. However, it typically requires a large number of projections, and subsequently a large radiation dose, to produce high quality images. To improve the applicability of this imaging technique, reconstruction algorithms that can reduce the radiation dose and acquisition time without degrading image quality are needed. The proposed research focused on using a novel combination of Douglas–Rachford splitting and randomized Kaczmarz algorithms to solve large-scale total variation based optimization in a compressed sensing framework to reconstruct 2D images from a reduced number of projections. Visual assessment and quantitative performance evaluations of a synthetic abdomen phantom and real reconstructed image of an ex-vivo slice of canine prostate tissue demonstrate that the proposed algorithm is competitive in reconstruction process compared with other well-known algorithms. An additional potential benefit of reducing the number of projections would be reduction of time for motion artifact to occur if the sample moves during image acquisition. Use of this reconstruction algorithm to reduce the required number of projections in synchrotron source propagation-based X-ray phase contrast computed tomography is an effective form of dose reduction that may pave the way for imaging of in-vivo samples.

A quadratic approximation-based algorithm for the solution of multiparametric mixed-integer nonlinear programming problems

KAUST Repository

Domínguez, Luis F.

2012-06-25

An algorithm for the solution of convex multiparametric mixed-integer nonlinear programming problems arising in process engineering problems under uncertainty is introduced. The proposed algorithm iterates between a multiparametric nonlinear programming subproblem and a mixed-integer nonlinear programming subproblem to provide a series of parametric upper and lower bounds. The primal subproblem is formulated by fixing the integer variables and solved through a series of multiparametric quadratic programming (mp-QP) problems based on quadratic approximations of the objective function, while the deterministic master subproblem is formulated so as to provide feasible integer solutions for the next primal subproblem. To reduce the computational effort when infeasibilities are encountered at the vertices of the critical regions (CRs) generated by the primal subproblem, a simplicial approximation approach is used to obtain CRs that are feasible at each of their vertices. The algorithm terminates when there does not exist an integer solution that is better than the one previously used by the primal problem. Through a series of examples, the proposed algorithm is compared with a multiparametric mixed-integer outer approximation (mp-MIOA) algorithm to demonstrate its computational advantages. © 2012 American Institute of Chemical Engineers (AIChE).

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

International Nuclear Information System (INIS)

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

2014-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2014-04-15

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

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning.

Science.gov (United States)

Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-02-22

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.

An improved muon reconstruction algorithm for INO-ICAL experiment

International Nuclear Information System (INIS)

Bhattacharya, Kolahal; MandaI, Naba K.

2013-01-01

The charge current interaction of neutrino in INO-ICAL detector will be identified with a muon (μ ± ) in the detector whose kinematics is related with the kinematics of the neutrino. So, muon reconstruction is a very important step in achieving INO physics goals. The existing muon reconstruction package for INO-ICAL has poor performance in specific regimes of experimental interest: (a) for larger zenith angle (θ > 50°), (b) for lower energies (E < 1 GeV); mainly due to poor error propagation scheme insensitive to energy E, angle (θ, φ) and inhomogeneous magnetic field along the muon track. Since, a significant fraction of muons from atmospheric neutrino interactions will have initial energy < 1 GeV and almost uniform distribution in cosθ a robust package for muon reconstruction is essential. We have implemented higher order correction terms in the propagation of the state and error covariance matrices of the Kalman Iter. The algorithm ensures track element merging in most cases and also increases reconstruction efficiency. The performance of this package will be presented in comparison with the previous one. (author)

A practical exact maximum compatibility algorithm for reconstruction of recent evolutionary history.

Science.gov (United States)

Cherry, Joshua L

2017-02-23

Maximum compatibility is a method of phylogenetic reconstruction that is seldom applied to molecular sequences. It may be ideal for certain applications, such as reconstructing phylogenies of closely-related bacteria on the basis of whole-genome sequencing. Here I present an algorithm that rapidly computes phylogenies according to a compatibility criterion. Although based on solutions to the maximum clique problem, this algorithm deals properly with ambiguities in the data. The algorithm is applied to bacterial data sets containing up to nearly 2000 genomes with several thousand variable nucleotide sites. Run times are several seconds or less. Computational experiments show that maximum compatibility is less sensitive than maximum parsimony to the inclusion of nucleotide data that, though derived from actual sequence reads, has been identified as likely to be misleading. Maximum compatibility is a useful tool for certain phylogenetic problems, such as inferring the relationships among closely-related bacteria from whole-genome sequence data. The algorithm presented here rapidly solves fairly large problems of this type, and provides robustness against misleading characters than can pollute large-scale sequencing data.

An integer optimization algorithm for robust identification of non-linear gene regulatory networks

Directory of Open Access Journals (Sweden)

Chemmangattuvalappil Nishanth

2012-09-01

Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

A new hybrid-FBP inversion algorithm with inverse distance backprojection weight for CT reconstruction

Energy Technology Data Exchange (ETDEWEB)

Narasimhadhan, A.V.; Rajgopal, Kasi

2011-07-01

This paper presents a new hybrid filtered backprojection (FBP) algorithm for fan-beam and cone-beam scan. The hybrid reconstruction kernel is the sum of the ramp and Hilbert filters. We modify the redundancy weighting function to reduce the inverse square distance weighting in the backprojection to inverse distance weight. The modified weight also eliminates the derivative associated with the Hilbert filter kernel. Thus, the proposed reconstruction algorithm has the advantages of the inverse distance weight in the backprojection. We evaluate the performance of the new algorithm in terms of the magnitude level and uniformity in noise for the fan-beam geometry. The computer simulations show that the spatial resolution is nearly identical to the standard fan-beam ramp filtered algorithm while the noise is spatially uniform and the noise variance is reduced. (orig.)

Efficient reconstruction of contaminant release history

Energy Technology Data Exchange (ETDEWEB)

Alezander, Francis [Los Alamos National Laboratory; Anghel, Marian [Los Alamos National Laboratory; Gulbahce, Natali [NON LANL; Tartakovsky, Daniel [NON LANL

2009-01-01

We present a generalized hybrid Monte Carlo (GHMC) method for fast, statistically optimal reconstruction of release histories of reactive contaminants. The approach is applicable to large-scale, strongly nonlinear systems with parametric uncertainties and data corrupted by measurement errors. The use of discrete adjoint equations facilitates numerical implementation of GHMC, without putting any restrictions on the degree of nonlinearity of advection-dispersion-reaction equations that are used to described contaminant transport in the subsurface. To demonstrate the salient features of the proposed algorithm, we identify the spatial extent of a distributed source of contamination from concentration measurements of a reactive solute.

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 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

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.

Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features

Directory of Open Access Journals (Sweden)

Hui Huang

2017-01-01

Full Text Available According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.

An efficient polyenergetic SART (pSART) reconstruction algorithm for quantitative myocardial CT perfusion

Energy Technology Data Exchange (ETDEWEB)

Lin, Yuan, E-mail: yuan.lin@duke.edu; Samei, Ehsan [Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705 (United States)

2014-02-15

Purpose: In quantitative myocardial CT perfusion imaging, beam hardening effect due to dense bone and high concentration iodinated contrast agent can result in visible artifacts and inaccurate CT numbers. In this paper, an efficient polyenergetic Simultaneous Algebraic Reconstruction Technique (pSART) was presented to eliminate the beam hardening artifacts and to improve the CT quantitative imaging ability. Methods: Our algorithm made threea priori assumptions: (1) the human body is composed of several base materials (e.g., fat, breast, soft tissue, bone, and iodine); (2) images can be coarsely segmented to two types of regions, i.e., nonbone regions and noniodine regions; and (3) each voxel can be decomposed into a mixture of two most suitable base materials according to its attenuation value and its corresponding region type information. Based on the above assumptions, energy-independent accumulated effective lengths of all base materials can be fast computed in the forward ray-tracing process and be used repeatedly to obtain accurate polyenergetic projections, with which a SART-based equation can correctly update each voxel in the backward projecting process to iteratively reconstruct artifact-free images. This approach effectively reduces the influence of polyenergetic x-ray sources and it further enables monoenergetic images to be reconstructed at any arbitrarily preselected target energies. A series of simulation tests were performed on a size-variable cylindrical phantom and a realistic anthropomorphic thorax phantom. In addition, a phantom experiment was also performed on a clinical CT scanner to further quantitatively validate the proposed algorithm. Results: The simulations with the cylindrical phantom and the anthropomorphic thorax phantom showed that the proposed algorithm completely eliminated beam hardening artifacts and enabled quantitative imaging across different materials, phantom sizes, and spectra, as the absolute relative errors were reduced

An efficient polyenergetic SART (pSART) reconstruction algorithm for quantitative myocardial CT perfusion

International Nuclear Information System (INIS)

Lin, Yuan; Samei, Ehsan

2014-01-01

Purpose: In quantitative myocardial CT perfusion imaging, beam hardening effect due to dense bone and high concentration iodinated contrast agent can result in visible artifacts and inaccurate CT numbers. In this paper, an efficient polyenergetic Simultaneous Algebraic Reconstruction Technique (pSART) was presented to eliminate the beam hardening artifacts and to improve the CT quantitative imaging ability. Methods: Our algorithm made threea priori assumptions: (1) the human body is composed of several base materials (e.g., fat, breast, soft tissue, bone, and iodine); (2) images can be coarsely segmented to two types of regions, i.e., nonbone regions and noniodine regions; and (3) each voxel can be decomposed into a mixture of two most suitable base materials according to its attenuation value and its corresponding region type information. Based on the above assumptions, energy-independent accumulated effective lengths of all base materials can be fast computed in the forward ray-tracing process and be used repeatedly to obtain accurate polyenergetic projections, with which a SART-based equation can correctly update each voxel in the backward projecting process to iteratively reconstruct artifact-free images. This approach effectively reduces the influence of polyenergetic x-ray sources and it further enables monoenergetic images to be reconstructed at any arbitrarily preselected target energies. A series of simulation tests were performed on a size-variable cylindrical phantom and a realistic anthropomorphic thorax phantom. In addition, a phantom experiment was also performed on a clinical CT scanner to further quantitatively validate the proposed algorithm. Results: The simulations with the cylindrical phantom and the anthropomorphic thorax phantom showed that the proposed algorithm completely eliminated beam hardening artifacts and enabled quantitative imaging across different materials, phantom sizes, and spectra, as the absolute relative errors were reduced

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

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms

Science.gov (United States)

Ott, Julien G.; Becce, Fabio; Monnin, Pascal; Schmidt, Sabine; Bochud, François O.; Verdun, Francis R.

2014-08-01

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Advanced Emergency Braking Control Based on a Nonlinear Model Predictive Algorithm for Intelligent Vehicles

Directory of Open Access Journals (Sweden)

Ronghui Zhang

2017-05-01

Full Text Available Focusing on safety, comfort and with an overall aim of the comprehensive improvement of a vision-based intelligent vehicle, a novel Advanced Emergency Braking System (AEBS is proposed based on Nonlinear Model Predictive Algorithm. Considering the nonlinearities of vehicle dynamics, a vision-based longitudinal vehicle dynamics model is established. On account of the nonlinear coupling characteristics of the driver, surroundings, and vehicle itself, a hierarchical control structure is proposed to decouple and coordinate the system. To avoid or reduce the collision risk between the intelligent vehicle and collision objects, a coordinated cost function of tracking safety, comfort, and fuel economy is formulated. Based on the terminal constraints of stable tracking, a multi-objective optimization controller is proposed using the theory of non-linear model predictive control. To quickly and precisely track control target in a finite time, an electronic brake controller for AEBS is designed based on the Nonsingular Fast Terminal Sliding Mode (NFTSM control theory. To validate the performance and advantages of the proposed algorithm, simulations are implemented. According to the simulation results, the proposed algorithm has better integrated performance in reducing the collision risk and improving the driving comfort and fuel economy of the smart car compared with the existing single AEBS.

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

Science.gov (United States)

Zhang, Shang; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-01-01

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. PMID:29470406

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

Science.gov (United States)

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

2013-02-01

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

SINS/CNS Nonlinear Integrated Navigation Algorithm for Hypersonic Vehicle

Directory of Open Access Journals (Sweden)

Yong-jun Yu

2015-01-01

Full Text Available Celestial Navigation System (CNS has characteristics of accurate orientation and strong autonomy and has been widely used in Hypersonic Vehicle. Since the CNS location and orientation mainly depend upon the inertial reference that contains errors caused by gyro drifts and other error factors, traditional Strap-down Inertial Navigation System (SINS/CNS positioning algorithm setting the position error between SINS and CNS as measurement is not effective. The model of altitude azimuth, platform error angles, and horizontal position is designed, and the SINS/CNS tightly integrated algorithm is designed, in which CNS altitude azimuth is set as measurement information. GPF (Gaussian particle filter is introduced to solve the problem of nonlinear filtering. The results of simulation show that the precision of SINS/CNS algorithm which reaches 130 m using three stars is improved effectively.

Reconstructing a nonlinear dynamical framework for testing quantum mechanics

International Nuclear Information System (INIS)

Jordan, T.F.

1993-01-01

The nonlinear generalization of quantum dynamics constructed by Weinberg as a basis for experimental tests is reconstructed in terms of density-matrix elements to allow independent dynamics for subsystems. Dynamics is generated with a Lie bracket and a nonlinear Hamiltonian function. It takes density matrices to density matrices and pure states to pure states. Each density matrix has a Hamiltonian operator that makes its evolution for an infinitesimal time, but the Hamiltonian operator may be different for different density matrices and may change in time as the density matrix changes. A Hamiltonian function for a subsystem serves also for the entire system. Independence of separate subsystems is confirmed by seeing that brackets are zero for functions from different subsystems and by looking at the Hamiltonian operator for each density matrix. Scaling properties of Hamiltonian functions are found to be important in connection with locality. An example of all this is obtained from every one of the local nonlinear Schroedinger equations described by Bialynicki-Birula and Mycielski. Examples are worked out for spins coupled together or to fields, demonstrating Hamiltonian functions and equations of motion written directly in terms of physical mean values. Observables and states are taken to be the same as in ordinary quantum mechanics. An attempt to find nonlinear representations of observables by characterizing propositions as functions equal to their squares yields a negative result. Sharper interpretation of mixed states is proposed. In a mixture of parts that are prepared separately, time dependence must be calculated separately for each part so different mixtures that yield the same density matrix can be distinguished. No criticism has shown that a consistent interpretation cannot be made this way. Thus, nonlinearity remains a viable hypothesis for experimental tests. 16 refs

An evolutionary algorithm for tomographic reconstructions in limited data sets problems

International Nuclear Information System (INIS)

Turcanu, Catrinel; Craciunescu, Teddy

2000-01-01

The paper proposes a new method for tomographic reconstructions. Unlike nuclear medicine applications, in physical science problems we are often confronted with limited data sets: constraints in the number of projections or limited angle views. The problem of image reconstruction from projections may be considered as a problem of finding an image (solution) having projections that match the experimental ones. In our approach, we choose a statistical correlation coefficient to evaluate the fitness of any potential solution. The optimization process is carried out by an evolutionary algorithm. Our algorithm has some problem-oriented characteristics. One of them is that a chromosome, representing a potential solution, is not linear but coded as a matrix of pixels corresponding to a two-dimensional image. This kind of internal representation reflects the genuine manifestation and slight differences between two points situated in the original problem space give rise to similar differences once they become coded. Another particular feature is a newly built crossover operator: the grid-based crossover, suitable for high dimension two-dimensional chromosomes. Except for the population size and the dimension of the cutting grid for the grid-based crossover, all the other parameters of the algorithm are independent of the geometry of the tomographic reconstruction. The performances of the method are evaluated in comparison with a traditional tomographic method, based on the maximization of the entropy of the image, that proved to work well with limited data sets. The test phantom is typical for an application with limited data sets: the determination of the neutron energy spectra with time resolution in case of short-pulsed neutron emission. The qualitative judgement and also the quantitative one, based on some figures of merit, point out that the proposed method ensures an improved reconstruction of shapes, sizes and resolution in the image, even in the presence of noise

High performance graphics processor based computed tomography reconstruction algorithms for nuclear and other large scale applications.

Energy Technology Data Exchange (ETDEWEB)

Jimenez, Edward S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Orr, Laurel J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Thompson, Kyle R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2013-09-01

The goal of this work is to develop a fast computed tomography (CT) reconstruction algorithm based on graphics processing units (GPU) that achieves significant improvement over traditional central processing unit (CPU) based implementations. The main challenge in developing a CT algorithm that is capable of handling very large datasets is parallelizing the algorithm in such a way that data transfer does not hinder performance of the reconstruction algorithm. General Purpose Graphics Processing (GPGPU) is a new technology that the Science and Technology (S&T) community is starting to adopt in many fields where CPU-based computing is the norm. GPGPU programming requires a new approach to algorithm development that utilizes massively multi-threaded environments. Multi-threaded algorithms in general are difficult to optimize since performance bottlenecks occur that are non-existent in single-threaded algorithms such as memory latencies. If an efficient GPU-based CT reconstruction algorithm can be developed; computational times could be improved by a factor of 20. Additionally, cost benefits will be realized as commodity graphics hardware could potentially replace expensive supercomputers and high-end workstations. This project will take advantage of the CUDA programming environment and attempt to parallelize the task in such a way that multiple slices of the reconstruction volume are computed simultaneously. This work will also take advantage of the GPU memory by utilizing asynchronous memory transfers, GPU texture memory, and (when possible) pinned host memory so that the memory transfer bottleneck inherent to GPGPU is amortized. Additionally, this work will take advantage of GPU-specific hardware (i.e. fast texture memory, pixel-pipelines, hardware interpolators, and varying memory hierarchy) that will allow for additional performance improvements.

A chaos-based evolutionary algorithm for general nonlinear programming problems

International Nuclear Information System (INIS)

El-Shorbagy, M.A.; Mousa, A.A.; Nasr, S.M.

2016-01-01

In this paper we present a chaos-based evolutionary algorithm (EA) for solving nonlinear programming problems named chaotic genetic algorithm (CGA). CGA integrates genetic algorithm (GA) and chaotic local search (CLS) strategy to accelerate the optimum seeking operation and to speed the convergence to the global solution. The integration of global search represented in genetic algorithm and CLS procedures should offer the advantages of both optimization methods while offsetting their disadvantages. By this way, it is intended to enhance the global convergence and to prevent to stick on a local solution. The inherent characteristics of chaos can enhance optimization algorithms by enabling it to escape from local solutions and increase the convergence to reach to the global solution. Twelve chaotic maps have been analyzed in the proposed approach. The simulation results using the set of CEC’2005 show that the application of chaotic mapping may be an effective strategy to improve the performances of EAs.

Reconstruction algorithms in the Super-Kamiokande large water Cherenkov detector

CERN Document Server

Shiozawa, M

1999-01-01

The Super-Kamiokande experiment, using a large underground water Cherenkov detector, has started its operation since first April, 1996. One of the main physics goals of this experiment is to measure the atmospheric neutrinos. Proton decay search is also an important topic. For these analyses, all measurement of physical quantities of an event such as vertex position, the number of Cherenkov rings, momentum, particle type and the number of decay electrons, is automatically performed by reconstruction algorithms. We attain enough quality of the analyses using these algorithms and several impressive results have been addressed.

Reconstruction algorithms in the Super-Kamiokande large water Cherenkov detector

International Nuclear Information System (INIS)

Shiozawa, M.

1999-01-01

The Super-Kamiokande experiment, using a large underground water Cherenkov detector, has started its operation since first April, 1996. One of the main physics goals of this experiment is to measure the atmospheric neutrinos. Proton decay search is also an important topic. For these analyses, all measurement of physical quantities of an event such as vertex position, the number of Cherenkov rings, momentum, particle type and the number of decay electrons, is automatically performed by reconstruction algorithms. We attain enough quality of the analyses using these algorithms and several impressive results have been addressed

Embedded algorithms within an FPGA-based system to process nonlinear time series data

Science.gov (United States)

Jones, Jonathan D.; Pei, Jin-Song; Tull, Monte P.

2008-03-01

This paper presents some preliminary results of an ongoing project. A pattern classification algorithm is being developed and embedded into a Field-Programmable Gate Array (FPGA) and microprocessor-based data processing core in this project. The goal is to enable and optimize the functionality of onboard data processing of nonlinear, nonstationary data for smart wireless sensing in structural health monitoring. Compared with traditional microprocessor-based systems, fast growing FPGA technology offers a more powerful, efficient, and flexible hardware platform including on-site (field-programmable) reconfiguration capability of hardware. An existing nonlinear identification algorithm is used as the baseline in this study. The implementation within a hardware-based system is presented in this paper, detailing the design requirements, validation, tradeoffs, optimization, and challenges in embedding this algorithm. An off-the-shelf high-level abstraction tool along with the Matlab/Simulink environment is utilized to program the FPGA, rather than coding the hardware description language (HDL) manually. The implementation is validated by comparing the simulation results with those from Matlab. In particular, the Hilbert Transform is embedded into the FPGA hardware and applied to the baseline algorithm as the centerpiece in processing nonlinear time histories and extracting instantaneous features of nonstationary dynamic data. The selection of proper numerical methods for the hardware execution of the selected identification algorithm and consideration of the fixed-point representation are elaborated. Other challenges include the issues of the timing in the hardware execution cycle of the design, resource consumption, approximation accuracy, and user flexibility of input data types limited by the simplicity of this preliminary design. Future work includes making an FPGA and microprocessor operate together to embed a further developed algorithm that yields better

Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

Directory of Open Access Journals (Sweden)

Xiaobo Guo

Full Text Available Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs. It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC curve and the precision-recall (PR curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

An Algorithmic Approach for the Reconstruction of Nasal Skin Defects: Retrospective Analysis of 130 Cases

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Berrak Akşam

2016-06-01

Full Text Available Objective: Most of the malignant cutaneous carcinomas are seen in the nasal region. Reconstruction of nasal defects is challenging because of the unique anatomic properties and complex structure of this region. In this study, we present our algorithm for the nasal skin defects that occurred after malignant skin tumor excisions. Material and Methods: Patients whose nasal skin was reconstructed after malignant skin tumor excision were included in the study. These patients were evaluated by their age, gender, comorbities, tumor location, tumor size, reconstruction type, histopathological diagnosis, and tumor recurrence. Results: A total of 130 patients (70 female, 60 male were evaluated. The average age of the patients was 67.8 years. Tumors were located mostly at the dorsum, alar region, and tip of the nose. When reconstruction methods were evaluated, primary closure was preferred in 14.6% patients, full thickness skin grafts were used in 25.3% patients, and reconstruction with flaps were the choice in 60% patients. Different flaps were used according to the subunits. Mostly, dorsal nasal flaps, bilobed flaps, nasolabial flaps, and forehead flaps were used. Conclusion: The defect-only reconstruction principle was accepted in this study. Previously described subunits, such as the dorsum, tip, alar region, lateral wall, columella, and soft triangles, of the nose were further divided into subregions by their anatomical relations. An algorithm was planned with these sub regions. In nasal skin reconstruction, this algorithm helps in selection the methods for the best results and minimize the complications.

Performance Comparison of Reconstruction Algorithms in Discrete Blind Multi-Coset Sampling

DEFF Research Database (Denmark)

Grigoryan, Ruben; Arildsen, Thomas; Tandur, Deepaknath

2012-01-01

This paper investigates the performance of different reconstruction algorithms in discrete blind multi-coset sampling. Multi-coset scheme is a promising compressed sensing architecture that can replace traditional Nyquist-rate sampling in the applications with multi-band frequency sparse signals...

Dense Matching Comparison Between Census and a Convolutional Neural Network Algorithm for Plant Reconstruction

Science.gov (United States)

Xia, Y.; Tian, J.; d'Angelo, P.; Reinartz, P.

2018-05-01

3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

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

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Peigang Ning

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

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

International Nuclear Information System (INIS)

Huang, Xiaobiao; Safranek, James

2014-01-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

Energy Technology Data Exchange (ETDEWEB)

Huang, Xiaobiao, E-mail: xiahuang@slac.stanford.edu; Safranek, James

2014-09-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

Strategies of reconstruction algorithms for computerized tomography

International Nuclear Information System (INIS)

Garderet, P.

1984-10-01

Image reconstruction from projections has progressively spread out over all fields of medical imaging. As the mathematical aspects of the problem become more and more comprehensively explored a great variety of numerical solutions have been developed best suited to such-and-such imaging medical application and taking into account the physical phenomena related to data collection (a priori properties for signal and noise). The purpose of that survey is to present the general mathematical frame and the fundamental assumptions of various strategies; Fourier methods approximate explicit deterministic inversion formula for the Radon transform. Algebraic reconstruction techniques set up an a priori discrete model through a series expansion approach of the solution. The numerical system to be solved is huge when a fine grid of pixels is to be reconstructed; iterative solutions may then be found. Recently some least square procedures have been shown to be tractable which avoid the use of iterative methods. Finally maximum like hood approach incorporates accurately the Poisson nature of photon noise and are well adapted to emission computed tomography. The various strategies will be analysed from both aspects of theoretical assumptions needed for suitable use and of computing facilities, actual performance and cost. In the end we take a glimpse of the extension of the algorithms from two dimensional imaging to fully three dimensional volume analysis in preparation of the future medical imaging technologies

Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle.

Science.gov (United States)

Wu, Junfeng; Dai, Fang; Hu, Gang; Mou, Xuanqin

2018-04-18

Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms.

Aitken extrapolation and epsilon algorithm for an accelerated solution of weakly singular nonlinear Volterra integral equations

International Nuclear Information System (INIS)

Mesgarani, H; Parmour, P; Aghazadeh, N

2010-01-01

In this paper, we apply Aitken extrapolation and epsilon algorithm as acceleration technique for the solution of a weakly singular nonlinear Volterra integral equation of the second kind. In this paper, based on Tao and Yong (2006 J. Math. Anal. Appl. 324 225-37.) the integral equation is solved by Navot's quadrature formula. Also, Tao and Yong (2006) for the first time applied Richardson extrapolation to accelerating convergence for the weakly singular nonlinear Volterra integral equations of the second kind. To our knowledge, this paper may be the first attempt to apply Aitken extrapolation and epsilon algorithm for the weakly singular nonlinear Volterra integral equations of the second kind.

Missing texture reconstruction method based on error reduction algorithm using Fourier transform magnitude estimation scheme.

Science.gov (United States)

Ogawa, Takahiro; Haseyama, Miki

2013-03-01

A missing texture reconstruction method based on an error reduction (ER) algorithm, including a novel estimation scheme of Fourier transform magnitudes is presented in this brief. In our method, Fourier transform magnitude is estimated for a target patch including missing areas, and the missing intensities are estimated by retrieving its phase based on the ER algorithm. Specifically, by monitoring errors converged in the ER algorithm, known patches whose Fourier transform magnitudes are similar to that of the target patch are selected from the target image. In the second approach, the Fourier transform magnitude of the target patch is estimated from those of the selected known patches and their corresponding errors. Consequently, by using the ER algorithm, we can estimate both the Fourier transform magnitudes and phases to reconstruct the missing areas.

Nonlinear Filtering with IMM Algorithm for Ultra-Tight GPS/INS Integration

Directory of Open Access Journals (Sweden)

Dah-Jing Jwo

2013-05-01

Full Text Available Abstract This paper conducts a performance evaluation for the ultra-tight integration of a Global positioning system (GPS and an inertial navigation system (INS, using nonlinear filtering approaches with an interacting multiple model (IMM algorithm. An ultra-tight GPS/INS architecture involves the integration of in-phase and quadrature components from the correlator of a GPS receiver with INS data. An unscented Kalman filter (UKF, which employs a set of sigma points by deterministic sampling, avoids the error caused by linearization as in an extended Kalman filter (EKF. Based on the filter structural adaptation for describing various dynamic behaviours, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the ultra-tight GPS/INS integration. The use of IMM enables tuning of an appropriate value for the process of noise covariance so as to maintain good estimation accuracy and tracking capability. Two examples are provided to illustrate the effectiveness of the design and demonstrate the effective improvement in navigation estimation accuracy. A performance comparison among various filtering methods for ultra-tight integration of GPS and INS is also presented. The IMM based nonlinear filtering approach demonstrates the effectiveness of the algorithm for improved positioning performance.

Nonlinear PI Control with Adaptive Interaction Algorithm for Multivariable Wastewater Treatment Process

Directory of Open Access Journals (Sweden)

S. I. Samsudin

2014-01-01

Full Text Available The wastewater treatment plant (WWTP is highly known with the nonlinearity of the control parameters, thus it is difficult to be controlled. In this paper, the enhancement of nonlinear PI controller (ENon-PI to compensate the nonlinearity of the activated sludge WWTP is proposed. The ENon-PI controller is designed by cascading a sector-bounded nonlinear gain to linear PI controller. The rate variation of the nonlinear gain kn is automatically updated based on adaptive interaction algorithm. Initiative to simplify the ENon-PI control structure by adapting kn has been proved by significant improvement under various dynamic influents. More than 30% of integral square error and 14% of integral absolute error are reduced compared to benchmark PI for DO control and nitrate in nitrogen removal control. Better average effluent qualities, less number of effluent violations, and lower aeration energy consumption resulted.

A multicenter evaluation of seven commercial ML-EM algorithms for SPECT image reconstruction using simulation data

International Nuclear Information System (INIS)

Matsumoto, Keiichi; Ohnishi, Hideo; Niida, Hideharu; Nishimura, Yoshihiro; Wada, Yasuhiro; Kida, Tetsuo

2003-01-01

The maximum likelihood expectation maximization (ML-EM) algorithm has become available as an alternative to filtered back projection in SPECT. The actual physical performance may be different depending on the manufacturer and model, because of differences in computational details. The purpose of this study was to investigate the characteristics of seven different types of ML-EM algorithms using simple simulation data. Seven ML-EM algorithm programs were used: Genie (GE), esoft (Siemens), HARP-III (Hitachi), GMS-5500UI (Toshiba), Pegasys (ADAC), ODYSSEY-FX (Marconi), and Windows-PC (original software). Projection data of a 2-pixel-wide line source in the center of the field of view were simulated without attenuation or scatter. Images were reconstructed with ML-EM by changing the number of iterations from 1 to 45 for each algorithm. Image quality was evaluated after a reconstruction using full width at half maximum (FWHM), full width at tenth maximum (FWTM), and the total counts of the reconstructed images. In the maximum number of iterations, the difference in the FWHM value was up to 1.5 pixels, and that of FWTM, no less than 2.0 pixels. The total counts of the reconstructed images in the initial few iterations were larger or smaller than the converged value depending on the initial values. Our results for the simplest simulation data suggest that each ML-EM algorithm itself provides a simulation image. We should keep in mind which algorithm is being used and its computational details, when physical and clinical usefulness are compared. (author)

Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

Science.gov (United States)

Hu, Yi-Chung

2014-01-01

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM☆

Science.gov (United States)

López, J.D.; Litvak, V.; Espinosa, J.J.; Friston, K.; Barnes, G.R.

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy—an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. PMID:24041874

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.

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

Science.gov (United States)

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

1994-01-01

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

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

The treatment of cancer in radiation therapy is achievable today by techniques that enable highly conformal dose distributions and steep dose gradients. In order to avoid mistreatment, these irradiation techniques have necessitated enhanced patient localization techniques. With an integrated x-ray tube at modern linear accelerators kV-projections can be acquired over a sufficiently large angular space and can be reconstructed to a volumetric image data set from the current situation of the patient prior to irradiation. The so-called Cone-Beam-CT (CBCT) allows a precise verification of patient positioning as well as adaptive radiotherapy. The benefits of an improved patient positioning due to a daily performed CBCT's is contrary to an increased and not negligible radiation exposure of the patient. In order to decrease the radiation exposure, substantial research effort is focused on various dose reduction strategies. Prominent strategies are the decrease of the charge per projection, the reduction of the number of projections as well as the reduction of the acquisition space. Unfortunately, these acquisition schemes lead to images with degraded quality with the widely used Feldkamp-Davis-Kress image reconstruction algorithm. More sophisticated image reconstruction techniques can deal with these dose-reduction strategies without degrading the image quality. A frequently investigated method is the image reconstruction by minimizing the total variation (TV), which is also known as Compressed Sensing (CS). A Compressed Sensing-based reconstruction framework that includes prior images into the reconstruction algorithm is the Prior-Image-Constrained- Compressed-Sensing algorithm (PICCS). The images reconstructed by PICCS outperform the reconstruction results of the conventional Feldkamp-Davis-Kress algorithm (FDK) based method if only a small number of projections are available. However, a drawback of PICCS is that major deviations between prior image data sets and the

Ship nonlinear-feedback course keeping algorithm based on MMG model driven by bipolar sigmoid function for berthing

Directory of Open Access Journals (Sweden)

Qiang Zhang

2017-09-01

Full Text Available Course keeping is hard to implement under the condition of the propeller stopping or reversing at slow speed for berthing due to the ship's dynamic motion becoming highly nonlinear. To solve this problem, a practical Maneuvering Modeling Group (MMG ship mathematic model with propeller reversing transverse forces and low speed correction is first discussed to be applied for the right-handed single-screw ship. Secondly, a novel PID-based nonlinear feedback algorithm driven by bipolar sigmoid function is proposed. The PID parameters are determined by a closed-loop gain shaping algorithm directly, while the closed-loop gain shaping theory was employed for effects analysis of this algorithm. Finally, simulation experiments were carried out on an LPG ship. It is shown that the energy consumption and the smoothness performance of the nonlinear feedback control are reduced by 4.2% and 14.6% with satisfactory control effects; the proposed algorithm has the advantages of robustness, energy saving and safety in berthing practice.

Nonlinear fitness-space-structure adaptation and principal component analysis in genetic algorithms: an application to x-ray reflectivity analysis

International Nuclear Information System (INIS)

Tiilikainen, J; Tilli, J-M; Bosund, V; Mattila, M; Hakkarainen, T; Airaksinen, V-M; Lipsanen, H

2007-01-01

Two novel genetic algorithms implementing principal component analysis and an adaptive nonlinear fitness-space-structure technique are presented and compared with conventional algorithms in x-ray reflectivity analysis. Principal component analysis based on Hessian or interparameter covariance matrices is used to rotate a coordinate frame. The nonlinear adaptation applies nonlinear estimates to reshape the probability distribution of the trial parameters. The simulated x-ray reflectivity of a realistic model of a periodic nanolaminate structure was used as a test case for the fitting algorithms. The novel methods had significantly faster convergence and less stagnation than conventional non-adaptive genetic algorithms. The covariance approach needs no additional curve calculations compared with conventional methods, and it had better convergence properties than the computationally expensive Hessian approach. These new algorithms can also be applied to other fitting problems where tight interparameter dependence is present

Genetic algorithms applied to nonlinear and complex domains; TOPICAL

International Nuclear Information System (INIS)

Barash, D; Woodin, A E

1999-01-01

The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a ''final'' result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means

DENSE MATCHING COMPARISON BETWEEN CENSUS AND A CONVOLUTIONAL NEURAL NETWORK ALGORITHM FOR PLANT RECONSTRUCTION

Directory of Open Access Journals (Sweden)

Y. Xia

2018-05-01

Full Text Available 3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

Array diagnostics, spatial resolution, and filtering of undesired radiation with the 3D reconstruction algorithm

DEFF Research Database (Denmark)

Cappellin, C.; Pivnenko, Sergey; Jørgensen, E.

2013-01-01

This paper focuses on three important features of the 3D reconstruction algorithm of DIATOOL: the identification of array elements improper functioning and failure, the obtainable spatial resolution of the reconstructed fields and currents, and the filtering of undesired radiation and scattering...

A constrained conjugate gradient algorithm for computed tomography

Energy Technology Data Exchange (ETDEWEB)

Azevedo, S.G.; Goodman, D.M. [Lawrence Livermore National Lab., CA (United States)

1994-11-15

Image reconstruction from projections of x-ray, gamma-ray, protons and other penetrating radiation is a well-known problem in a variety of fields, and is commonly referred to as computed tomography (CT). Various analytical and series expansion methods of reconstruction and been used in the past to provide three-dimensional (3D) views of some interior quantity. The difficulties of these approaches lie in the cases where (a) the number of views attainable is limited, (b) the Poisson (or other) uncertainties are significant, (c) quantifiable knowledge of the object is available, but not implementable, or (d) other limitations of the data exist. We have adapted a novel nonlinear optimization procedure developed at LLNL to address limited-data image reconstruction problems. The technique, known as nonlinear least squares with general constraints or constrained conjugate gradients (CCG), has been successfully applied to a number of signal and image processing problems, and is now of great interest to the image reconstruction community. Previous applications of this algorithm to deconvolution problems and x-ray diffraction images for crystallography have shown the great promise.

A stand-alone track reconstruction algorithm for the scintillating fibre tracker at the LHCb upgrade

CERN Multimedia

Quagliani, Renato

2017-01-01

The LHCb upgrade detector project foresees the presence of a scintillating fiber tracker (SciFi) to be used during the LHC Run III, starting in 2020. The instantaneous luminosity will be increased up to $2\\times10^{33}$, five times larger than in Run II and a full software event reconstruction will be performed at the full bunch crossing rate by the trigger. The new running conditions, and the tighter timing constraints in the software trigger, represent a big challenge for track reconstruction. This poster presents the design and performance of a novel algorithm that has been developed to reconstruct track segments using solely hits from the SciFi. This algorithm is crucial for the reconstruction of tracks originating from long-lived particles such as $K_{S}^{0}$ and $\\Lambda$ and allows to greatly enhance the physics potential and capabilities of the LHCb upgrade when compared to its previous implementation.

Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two-phase flow

Science.gov (United States)

Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang

2018-05-01

Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.

Shape reconstruction from apparent contours theory and algorithms

CERN Document Server

Bellettini, Giovanni; Paolini, Maurizio

2015-01-01

Motivated by a variational model concerning the depth of the objects in a picture and the problem of hidden and illusory contours, this book investigates one of the central problems of computer vision: the topological and algorithmic reconstruction of a smooth three dimensional scene starting from the visible part of an apparent contour. The authors focus their attention on the manipulation of apparent contours using a finite set of elementary moves, which correspond to diffeomorphic deformations of three dimensional scenes. A large part of the book is devoted to the algorithmic part, with implementations, experiments, and computed examples. The book is intended also as a user's guide to the software code appcontour, written for the manipulation of apparent contours and their invariants. This book is addressed to theoretical and applied scientists working in the field of mathematical models of image segmentation.

Track reconstruction algorithms for the CBM experiment at FAIR

International Nuclear Information System (INIS)

Lebedev, Andrey; Hoehne, Claudia; Kisel, Ivan; Ososkov, Gennady

2010-01-01

The Compressed Baryonic Matter (CBM) experiment at the future FAIR accelerator complex at Darmstadt is being designed for a comprehensive measurement of hadron and lepton production in heavy-ion collisions from 8-45 AGeV beam energy, producing events with large track multiplicity and high hit density. The setup consists of several detectors including as tracking detectors the silicon tracking system (STS), the muon detector (MUCH) or alternatively a set of Transition Radiation Detectors (TRD). In this contribution, the status of the track reconstruction software including track finding, fitting and propagation is presented for the MUCH and TRD detectors. The track propagation algorithm takes into account an inhomogeneous magnetic field and includes accurate calculation of multiple scattering and energy losses in the detector material. Track parameters and covariance matrices are estimated using the Kalman filter method and a Kalman filter modification by assigning weights to hits and using simulated annealing. Three different track finder algorithms based on track following have been developed which either allow for track branches, just select nearest hits or use the mentioned weighting method. The track reconstruction efficiency for central Au+Au collisions at 25 AGeV beam energy using events from the UrQMD model is at the level of 93-95% for both detectors.

Implementation and evaluation of an ordered subsets reconstruction algorithm for transmission PET studies using median root prior and inter-update median filtering

International Nuclear Information System (INIS)

Bettinardi, V.; Gilardi, M.C.; Fazio, F.; Alenius, S.; Ruotsalainen, U.; Numminen, P.; Teraes, M.

2003-01-01

An ordered subsets (OS) reconstruction algorithm based on the median root prior (MRP) and inter-update median filtering was implemented for the reconstruction of low count statistics transmission (TR) scans. The OS-MRP-TR algorithm was evaluated using an experimental phantom, simulating positron emission tomography (PET) whole-body (WB) studies, as well as patient data. Various experimental conditions, in terms of TR scan time (from 1 h to 1 min), covering a wide range of TR count statistics were evaluated. The performance of the algorithm was assessed by comparing the mean value of the attenuation coefficient (MVAC) of known tissue types and the coefficient of variation (CV) for low-count TR images, reconstructed with the OS-MRP-TR algorithm, with reference values obtained from high-count TR images reconstructed with a filtered back-projection (FBP) algorithm. The reconstructed OS-MRP-TR images were then used for attenuation correction of the corresponding emission (EM) data. EM images reconstructed with attenuation correction generated by OS-MRP-TR images, of low count statistics, were compared with the EM images corrected for attenuation using reference (high statistics) TR data. In all the experimental situations considered, the OS-MRP-TR algorithm showed: (1) a tendency towards a stable solution in terms of MVAC; (2) a difference in the MVAC of within 5% for a TR scan of 1 min reconstructed with the OS-MRP-TR and a TR scan of 1 h reconstructed with the FBP algorithm; (3) effectiveness in noise reduction, particularly for low count statistics data [using a specific parameter configuration the TR images reconstructed with OS-MRP-TR(1 min) had a lower CV than the corresponding TR images of a 1-h scan reconstructed with the FBP algorithm]; (4) a difference of within 3% between the mean counts in the EM images attenuation corrected using the OS-MRP-TR images of 1 min and the mean counts in the EM images attenuation corrected using the OS-MRP-TR images of 1 h; (5

Conditional probability distribution associated to the E-M image reconstruction algorithm for neutron stimulated emission tomography

International Nuclear Information System (INIS)

Viana, R.S.; Yoriyaz, H.; Santos, A.

2011-01-01

The Expectation-Maximization (E-M) algorithm is an iterative computational method for maximum likelihood (M-L) estimates, useful in a variety of incomplete-data problems. Due to its stochastic nature, one of the most relevant applications of E-M algorithm is the reconstruction of emission tomography images. In this paper, the statistical formulation of the E-M algorithm was applied to the in vivo spectrographic imaging of stable isotopes called Neutron Stimulated Emission Computed Tomography (NSECT). In the process of E-M algorithm iteration, the conditional probability distribution plays a very important role to achieve high quality image. This present work proposes an alternative methodology for the generation of the conditional probability distribution associated to the E-M reconstruction algorithm, using the Monte Carlo code MCNP5 and with the application of the reciprocity theorem. (author)

Conditional probability distribution associated to the E-M image reconstruction algorithm for neutron stimulated emission tomography

Energy Technology Data Exchange (ETDEWEB)

Viana, R.S.; Yoriyaz, H.; Santos, A., E-mail: rodrigossviana@gmail.com, E-mail: hyoriyaz@ipen.br, E-mail: asantos@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2011-07-01

The Expectation-Maximization (E-M) algorithm is an iterative computational method for maximum likelihood (M-L) estimates, useful in a variety of incomplete-data problems. Due to its stochastic nature, one of the most relevant applications of E-M algorithm is the reconstruction of emission tomography images. In this paper, the statistical formulation of the E-M algorithm was applied to the in vivo spectrographic imaging of stable isotopes called Neutron Stimulated Emission Computed Tomography (NSECT). In the process of E-M algorithm iteration, the conditional probability distribution plays a very important role to achieve high quality image. This present work proposes an alternative methodology for the generation of the conditional probability distribution associated to the E-M reconstruction algorithm, using the Monte Carlo code MCNP5 and with the application of the reciprocity theorem. (author)

Nonlinear smooth orthogonal decomposition of kinematic features of sawing reconstructs muscle fatigue evolution as indicated by electromyography.

Science.gov (United States)

Segala, David B; Gates, Deanna H; Dingwell, Jonathan B; Chelidze, David

2011-03-01

Tracking or predicting physiological fatigue is important for developing more robust training protocols and better energy supplements and/or reducing muscle injuries. Current methodologies are usually impractical and/or invasive and may not be realizable outside of laboratory settings. It was recently demonstrated that smooth orthogonal decomposition (SOD) of phase space warping (PSW) features of motion kinematics can identify fatigue in individual muscle groups. We hypothesize that a nonlinear extension of SOD will identify more optimal fatigue coordinates and provide a lower-dimensional reconstruction of local fatigue dynamics than the linear SOD. Both linear and nonlinear SODs were applied to PSW features estimated from measured kinematics to reconstruct muscle fatigue dynamics in subjects performing a sawing motion. Ten healthy young right-handed subjects pushed a weighted handle back and forth until voluntary exhaustion. Three sets of joint kinematic angles were measured from the right upper extremity in addition to surface electromyography (EMG) recordings. The SOD coordinates of kinematic PSW features were compared against independently measured fatigue markers (i.e., mean and median EMG spectrum frequencies of individual muscle groups). This comparison was based on a least-squares linear fit of a fixed number of the dominant SOD coordinates to the appropriate local fatigue markers. Between subject variability showed that at most four to five nonlinear SOD coordinates were needed to reconstruct fatigue in local muscle groups, while on average 15 coordinates were needed for the linear SOD. Thus, the nonlinear coordinates provided a one-order-of-magnitude improvement over the linear ones.

Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem

DEFF Research Database (Denmark)

Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

2015-01-01

Non-trivial real world decision-making processes usually involve multiple parties having potentially conflicting interests over a set of issues. State-of-the-art multi-objective evolutionary algorithms (MOEA) are well known to solve this class of complex real-world problems. In this paper, we...... compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...... of all aforementioned algorithms is assessed and compared using performance indicators to evaluate proximity, diversity and consistency. Our insights to this comparative study enhanced our understanding of MOEAs performance in order to solve a non-linear complex climate control problem. The empirical...

A parallel implementation of a maximum entropy reconstruction algorithm for PET images in a visual language

International Nuclear Information System (INIS)

Bastiens, K.; Lemahieu, I.

1994-01-01

The application of a maximum entropy reconstruction algorithm to PET images requires a lot of computing resources. A parallel implementation could seriously reduce the execution time. However, programming a parallel application is still a non trivial task, needing specialized people. In this paper a programming environment based on a visual programming language is used for a parallel implementation of the reconstruction algorithm. This programming environment allows less experienced programmers to use the performance of multiprocessor systems. (authors)

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

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

International Nuclear Information System (INIS)

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

2011-01-01

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

Application aspects of advanced antenna diagnostics with the 3D reconstruction algorithm

DEFF Research Database (Denmark)

Cappellin, Cecilia; Pivnenko, Sergey

2015-01-01

This paper focuses on two important applications of the 3D reconstruction algorithm of the commercial software DIATOOL for antenna diagnostics. The first one is the accurate and detailed identification of array malfunctioning, thanks to the available enhanced spatial resolution of the reconstruct...... fields and currents. The second one is the filtering of the scattering from support structures and feed network leakage. Representative experimental results are presented and guidelines on the recommended measurement parameters for obtaining the best diagnostics results are provided....

Bonus algorithm for large scale stochastic nonlinear programming problems

CERN Document Server

Diwekar, Urmila

2015-01-01

This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these ...

Analytical algorithm for the generation of polygonal projection data for tomographic reconstruction

International Nuclear Information System (INIS)

Davis, G.R.

1996-01-01

Tomographic reconstruction algorithms and filters can be tested using a mathematical phantom, that is, a computer program which takes numerical data as its input and outputs derived projection data. The input data is usually in the form of pixel ''densities'' over a regular grid, or position and dimensions of simple, geometrical objects. The former technique allows a greater variety of objects to be simulated, but is less suitable in the case when very small (relative to the ray-spacing) features are to be simulated. The second technique is normally used to simulate biological specimens, typically a human skull, modelled as a number of ellipses. This is not suitable for simulating non-biological specimens with features such as straight edges and fine cracks. We have therefore devised an algorithm for simulating objects described as a series of polygons. These polygons, or parts of them, may be smaller than the ray-spacing and there is no limit, except that imposed by computing resources, on the complexity, number or superposition of polygons. A simple test of such a phantom, reconstructed using the filtered back-projection method, revealed reconstruction artefacts not normally seen with ''biological'' phantoms. (orig.)

A parallel algorithm for 3D particle tracking and Lagrangian trajectory reconstruction

International Nuclear Information System (INIS)

Barker, Douglas; Zhang, Yuanhui; Lifflander, Jonathan; Arya, Anshu

2012-01-01

Particle-tracking methods are widely used in fluid mechanics and multi-target tracking research because of their unique ability to reconstruct long trajectories with high spatial and temporal resolution. Researchers have recently demonstrated 3D tracking of several objects in real time, but as the number of objects is increased, real-time tracking becomes impossible due to data transfer and processing bottlenecks. This problem may be solved by using parallel processing. In this paper, a parallel-processing framework has been developed based on frame decomposition and is programmed using the asynchronous object-oriented Charm++ paradigm. This framework can be a key step in achieving a scalable Lagrangian measurement system for particle-tracking velocimetry and may lead to real-time measurement capabilities. The parallel tracking algorithm was evaluated with three data sets including the particle image velocimetry standard 3D images data set #352, a uniform data set for optimal parallel performance and a computational-fluid-dynamics-generated non-uniform data set to test trajectory reconstruction accuracy, consistency with the sequential version and scalability to more than 500 processors. The algorithm showed strong scaling up to 512 processors and no inherent limits of scalability were seen. Ultimately, up to a 200-fold speedup is observed compared to the serial algorithm when 256 processors were used. The parallel algorithm is adaptable and could be easily modified to use any sequential tracking algorithm, which inputs frames of 3D particle location data and outputs particle trajectories

Implementation techniques and acceleration of DBPF reconstruction algorithm based on GPGPU for helical cone beam CT

International Nuclear Information System (INIS)

Shen Le; Xing Yuxiang

2010-01-01

The derivative back-projection filtered algorithm for a helical cone-beam CT is a newly developed exact reconstruction method. Due to its large computational complexity, the reconstruction is rather slow for practical use. General purpose graphic processing unit (GPGPU) is an SIMD paralleled hardware architecture with powerful float-point operation capacity. In this paper,we propose a new method for PI-line choice and sampling grid, and a paralleled PI-line reconstruction algorithm implemented on NVIDIA's Compute Unified Device Architecture (CUDA). Numerical simulation studies are carried out to validate our method. Compared with conventional CPU implementation, the CUDA accelerated method provides images of the same quality with a speedup factor of 318. Optimization strategies for the GPU acceleration are presented. Finally, influence of the parameters of the PI-line samples on the reconstruction speed and image quality is discussed. (authors)

An ensemble based nonlinear orthogonal matching pursuit algorithm for sparse history matching of reservoir models

KAUST Repository

Fsheikh, Ahmed H.

2013-01-01

A nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of reservoir models is presented. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated components of the basis functions with the residual. The discovered basis (aka support) is augmented across the nonlinear iterations. Once the basis functions are selected from the dictionary, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on approximate gradient estimation using an iterative stochastic ensemble method (ISEM). ISEM utilizes an ensemble of directional derivatives to efficiently approximate gradients. In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm.

A parallel implementation of a maximum entropy reconstruction algorithm for PET images in a visual language

Energy Technology Data Exchange (ETDEWEB)

Bastiens, K; Lemahieu, I [University of Ghent - ELIS Department, St. Pietersnieuwstraat 41, B-9000 Ghent (Belgium)

1994-12-31

The application of a maximum entropy reconstruction algorithm to PET images requires a lot of computing resources. A parallel implementation could seriously reduce the execution time. However, programming a parallel application is still a non trivial task, needing specialized people. In this paper a programming environment based on a visual programming language is used for a parallel implementation of the reconstruction algorithm. This programming environment allows less experienced programmers to use the performance of multiprocessor systems. (authors). 8 refs, 3 figs, 1 tab.

Algebraic dynamics solutions and algebraic dynamics algorithm for nonlinear ordinary differential equations

Institute of Scientific and Technical Information of China (English)

WANG; Shunjin; ZHANG; Hua

2006-01-01

The problem of preserving fidelity in numerical computation of nonlinear ordinary differential equations is studied in terms of preserving local differential structure and approximating global integration structure of the dynamical system.The ordinary differential equations are lifted to the corresponding partial differential equations in the framework of algebraic dynamics,and a new algorithm-algebraic dynamics algorithm is proposed based on the exact analytical solutions of the ordinary differential equations by the algebraic dynamics method.In the new algorithm,the time evolution of the ordinary differential system is described locally by the time translation operator and globally by the time evolution operator.The exact analytical piece-like solution of the ordinary differential equations is expressd in terms of Taylor series with a local convergent radius,and its finite order truncation leads to the new numerical algorithm with a controllable precision better than Runge Kutta Algorithm and Symplectic Geometric Algorithm.

Evaluation of noise and blur effects with SIRT-FISTA-TV reconstruction algorithm: Application to fast environmental transmission electron tomography.

Science.gov (United States)

Banjak, Hussein; Grenier, Thomas; Epicier, Thierry; Koneti, Siddardha; Roiban, Lucian; Gay, Anne-Sophie; Magnin, Isabelle; Peyrin, Françoise; Maxim, Voichita

2018-06-01

Fast tomography in Environmental Transmission Electron Microscopy (ETEM) is of a great interest for in situ experiments where it allows to observe 3D real-time evolution of nanomaterials under operating conditions. In this context, we are working on speeding up the acquisition step to a few seconds mainly with applications on nanocatalysts. In order to accomplish such rapid acquisitions of the required tilt series of projections, a modern 4K high-speed camera is used, that can capture up to 100 images per second in a 2K binning mode. However, due to the fast rotation of the sample during the tilt procedure, noise and blur effects may occur in many projections which in turn would lead to poor quality reconstructions. Blurred projections make classical reconstruction algorithms inappropriate and require the use of prior information. In this work, a regularized algebraic reconstruction algorithm named SIRT-FISTA-TV is proposed. The performance of this algorithm using blurred data is studied by means of a numerical blur introduced into simulated images series to mimic possible mechanical instabilities/drifts during fast acquisitions. We also present reconstruction results from noisy data to show the robustness of the algorithm to noise. Finally, we show reconstructions with experimental datasets and we demonstrate the interest of fast tomography with an ultra-fast acquisition performed under environmental conditions, i.e. gas and temperature, in the ETEM. Compared to classically used SIRT and SART approaches, our proposed SIRT-FISTA-TV reconstruction algorithm provides higher quality tomograms allowing easier segmentation of the reconstructed volume for a better final processing and analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Energy reconstruction and calibration algorithms for the ATLAS electromagnetic calorimeter

CERN Document Server

Delmastro, M

2003-01-01

The work of this thesis is devoted to the study, development and optimization of the algorithms of energy reconstruction and calibration for the electromagnetic calorimeter (EMC) of the ATLAS experiment, presently under installation and commissioning at the CERN Large Hadron Collider in Geneva (Switzerland). A deep study of the electrical characteristics of the detector and of the signals formation and propagation is conduced: an electrical model of the detector is developed and analyzed through simulations; a hardware model (mock-up) of a group of the EMC readout cells has been built, allowing the direct collection and properties study of the signals emerging from the EMC cells. We analyze the existing multiple-sampled signal reconstruction strategy, showing the need of an improvement in order to reach the advertised performances of the detector. The optimal filtering reconstruction technique is studied and implemented, taking into account the differences between the ionization and calibration waveforms as e...

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM.

Science.gov (United States)

López, J D; Litvak, V; Espinosa, J J; Friston, K; Barnes, G R

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. © 2013. Published by Elsevier Inc. All rights reserved.

Parallel supercomputing: Advanced methods, algorithms, and software for large-scale linear and nonlinear problems

Energy Technology Data Exchange (ETDEWEB)

Carey, G.F.; Young, D.M.

1993-12-31

The program outlined here is directed to research on methods, algorithms, and software for distributed parallel supercomputers. Of particular interest are finite element methods and finite difference methods together with sparse iterative solution schemes for scientific and engineering computations of very large-scale systems. Both linear and nonlinear problems will be investigated. In the nonlinear case, applications with bifurcation to multiple solutions will be considered using continuation strategies. The parallelizable numerical methods of particular interest are a family of partitioning schemes embracing domain decomposition, element-by-element strategies, and multi-level techniques. The methods will be further developed incorporating parallel iterative solution algorithms with associated preconditioners in parallel computer software. The schemes will be implemented on distributed memory parallel architectures such as the CRAY MPP, Intel Paragon, the NCUBE3, and the Connection Machine. We will also consider other new architectures such as the Kendall-Square (KSQ) and proposed machines such as the TERA. The applications will focus on large-scale three-dimensional nonlinear flow and reservoir problems with strong convective transport contributions. These are legitimate grand challenge class computational fluid dynamics (CFD) problems of significant practical interest to DOE. The methods developed and algorithms will, however, be of wider interest.

Quantitative analysis of emphysema in 3D using MDCT: Influence of different reconstruction algorithms

International Nuclear Information System (INIS)

Ley-Zaporozhan, Julia; Ley, Sebastian; Weinheimer, Oliver; Iliyushenko, Svitlana; Erdugan, Serap; Eberhardt, Ralf; Fuxa, Adelheid; Mews, Juergen; Kauczor, Hans-Ulrich

2008-01-01

Purpose: The aim of the study was to compare the influence of different reconstruction algorithms on quantitative emphysema analysis in patients with severe emphysema. Material and methods: Twenty-five patients suffering from severe emphysema were included in the study. All patients underwent inspiratory MDCT (Aquilion-16, slice thickness 1/0.8 mm). The raw data were reconstructed using six different algorithms: bone kernel with beam hardening correction (BHC), soft tissue kernel with BHC; standard soft tissue kernel, smooth soft tissue kernel (internal reference standard), standard lung kernel, and high-convolution kernel. The only difference between image data sets was the algorithm employed to reconstruct the raw data, no additional radiation was required. CT data were analysed using self-written emphysema detection and quantification software providing lung volume, emphysema volume (EV), emphysema index (EI) and mean lung density (MLD). Results: The use of kernels with BHC led to a significant decrease in MLD (5%) and EI (61-79%) in comparison with kernels without BHC. The absolute difference (from smooth soft tissue kernel) in MLD ranged from -0.6 to -6.1 HU and were significant different for all kernels. The EV showed absolute differences between -0.05 and -0.4 L and was significantly different for all kernels. The EI showed absolute differences between -0.8 and -5.1 and was significantly different for all kernels. Conclusion: The use of kernels with BHC led to a significant decrease in MLD and EI. The absolute differences between different kernels without BHC were small but they were larger than the known interscan variation in patients. Thus, for follow-up examinations the same reconstruction algorithm has to be used and use of BHC has to be avoided

A reconstruction algorithm for electrical impedance tomography based on sparsity regularization

KAUST Repository

Jin, Bangti

2011-08-24

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

Influence of radiation dose and reconstruction algorithm in MDCT assessment of airway wall thickness: A phantom study

International Nuclear Information System (INIS)

Gomez-Cardona, Daniel; Nagle, Scott K.; Li, Ke; Chen, Guang-Hong; Robinson, Terry E.

2015-01-01

Purpose: Wall thickness (WT) is an airway feature of great interest for the assessment of morphological changes in the lung parenchyma. Multidetector computed tomography (MDCT) has recently been used to evaluate airway WT, but the potential risk of radiation-induced carcinogenesis—particularly in younger patients—might limit a wider use of this imaging method in clinical practice. The recent commercial implementation of the statistical model-based iterative reconstruction (MBIR) algorithm, instead of the conventional filtered back projection (FBP) algorithm, has enabled considerable radiation dose reduction in many other clinical applications of MDCT. The purpose of this work was to study the impact of radiation dose and MBIR in the MDCT assessment of airway WT. Methods: An airway phantom was scanned using a clinical MDCT system (Discovery CT750 HD, GE Healthcare) at 4 kV levels and 5 mAs levels. Both FBP and a commercial implementation of MBIR (Veo TM , GE Healthcare) were used to reconstruct CT images of the airways. For each kV–mAs combination and each reconstruction algorithm, the contrast-to-noise ratio (CNR) of the airways was measured, and the WT of each airway was measured and compared with the nominal value; the relative bias and the angular standard deviation in the measured WT were calculated. For each airway and reconstruction algorithm, the overall performance of WT quantification across all of the 20 kV–mAs combinations was quantified by the sum of squares (SSQs) of the difference between the measured and nominal WT values. Finally, the particular kV–mAs combination and reconstruction algorithm that minimized radiation dose while still achieving a reference WT quantification accuracy level was chosen as the optimal acquisition and reconstruction settings. Results: The wall thicknesses of seven airways of different sizes were analyzed in the study. Compared with FBP, MBIR improved the CNR of the airways, particularly at low radiation dose

Influence of radiation dose and reconstruction algorithm in MDCT assessment of airway wall thickness: A phantom study

Energy Technology Data Exchange (ETDEWEB)

Gomez-Cardona, Daniel [Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Avenue, Madison, Wisconsin 53705 (United States); Nagle, Scott K. [Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Avenue, Madison, Wisconsin 53705 (United States); Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792 (United States); Department of Pediatrics, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792 (United States); Li, Ke; Chen, Guang-Hong, E-mail: gchen7@wisc.edu [Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Avenue, Madison, Wisconsin 53705 (United States); Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792 (United States); Robinson, Terry E. [Department of Pediatrics, Stanford School of Medicine, 770 Welch Road, Palo Alto, California 94304 (United States)

2015-10-15

Purpose: Wall thickness (WT) is an airway feature of great interest for the assessment of morphological changes in the lung parenchyma. Multidetector computed tomography (MDCT) has recently been used to evaluate airway WT, but the potential risk of radiation-induced carcinogenesis—particularly in younger patients—might limit a wider use of this imaging method in clinical practice. The recent commercial implementation of the statistical model-based iterative reconstruction (MBIR) algorithm, instead of the conventional filtered back projection (FBP) algorithm, has enabled considerable radiation dose reduction in many other clinical applications of MDCT. The purpose of this work was to study the impact of radiation dose and MBIR in the MDCT assessment of airway WT. Methods: An airway phantom was scanned using a clinical MDCT system (Discovery CT750 HD, GE Healthcare) at 4 kV levels and 5 mAs levels. Both FBP and a commercial implementation of MBIR (Veo{sup TM}, GE Healthcare) were used to reconstruct CT images of the airways. For each kV–mAs combination and each reconstruction algorithm, the contrast-to-noise ratio (CNR) of the airways was measured, and the WT of each airway was measured and compared with the nominal value; the relative bias and the angular standard deviation in the measured WT were calculated. For each airway and reconstruction algorithm, the overall performance of WT quantification across all of the 20 kV–mAs combinations was quantified by the sum of squares (SSQs) of the difference between the measured and nominal WT values. Finally, the particular kV–mAs combination and reconstruction algorithm that minimized radiation dose while still achieving a reference WT quantification accuracy level was chosen as the optimal acquisition and reconstruction settings. Results: The wall thicknesses of seven airways of different sizes were analyzed in the study. Compared with FBP, MBIR improved the CNR of the airways, particularly at low radiation dose

Effects of light refraction on the accuracy of camera calibration and reconstruction in underwater motion analysis.

Science.gov (United States)

Kwon, Young-Hoo; Casebolt, Jeffrey B

2006-07-01

One of the most serious obstacles to accurate quantification of the underwater motion of a swimmer's body is image deformation caused by refraction. Refraction occurs at the water-air interface plane (glass) owing to the density difference. Camera calibration-reconstruction algorithms commonly used in aquatic research do not have the capability to correct this refraction-induced nonlinear image deformation and produce large reconstruction errors. The aim of this paper is to provide a thorough review of: the nature of the refraction-induced image deformation and its behaviour in underwater object-space plane reconstruction; the intrinsic shortcomings of the Direct Linear Transformation (DLT) method in underwater motion analysis; experimental conditions that interact with refraction; and alternative algorithms and strategies that can be used to improve the calibration-reconstruction accuracy. Although it is impossible to remove the refraction error completely in conventional camera calibration-reconstruction methods, it is possible to improve the accuracy to some extent by manipulating experimental conditions or calibration frame characteristics. Alternative algorithms, such as the localized DLT and the double-plane method are also available for error reduction. The ultimate solution for the refraction problem is to develop underwater camera calibration and reconstruction algorithms that have the capability to correct refraction.

Design of 4D x-ray tomography experiments for reconstruction using regularized iterative algorithms

Science.gov (United States)

Mohan, K. Aditya

2017-10-01

4D X-ray computed tomography (4D-XCT) is widely used to perform non-destructive characterization of time varying physical processes in various materials. The conventional approach to improving temporal resolution in 4D-XCT involves the development of expensive and complex instrumentation that acquire data faster with reduced noise. It is customary to acquire data with many tomographic views at a high signal to noise ratio. Instead, temporal resolution can be improved using regularized iterative algorithms that are less sensitive to noise and limited views. These algorithms benefit from optimization of other parameters such as the view sampling strategy while improving temporal resolution by reducing the total number of views or the detector exposure time. This paper presents the design principles of 4D-XCT experiments when using regularized iterative algorithms derived using the framework of model-based reconstruction. A strategy for performing 4D-XCT experiments is presented that allows for improving the temporal resolution by progressively reducing the number of views or the detector exposure time. Theoretical analysis of the effect of the data acquisition parameters on the detector signal to noise ratio, spatial reconstruction resolution, and temporal reconstruction resolution is also presented in this paper.

An electron tomography algorithm for reconstructing 3D morphology using surface tangents of projected scattering interfaces

Science.gov (United States)

Petersen, T. C.; Ringer, S. P.

2010-03-01

Upon discerning the mere shape of an imaged object, as portrayed by projected perimeters, the full three-dimensional scattering density may not be of particular interest. In this situation considerable simplifications to the reconstruction problem are possible, allowing calculations based upon geometric principles. Here we describe and provide an algorithm which reconstructs the three-dimensional morphology of specimens from tilt series of images for application to electron tomography. Our algorithm uses a differential approach to infer the intersection of projected tangent lines with surfaces which define boundaries between regions of different scattering densities within and around the perimeters of specimens. Details of the algorithm implementation are given and explained using reconstruction calculations from simulations, which are built into the code. An experimental application of the algorithm to a nano-sized Aluminium tip is also presented to demonstrate practical analysis for a real specimen. Program summaryProgram title: STOMO version 1.0 Catalogue identifier: AEFS_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEFS_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 2988 No. of bytes in distributed program, including test data, etc.: 191 605 Distribution format: tar.gz Programming language: C/C++ Computer: PC Operating system: Windows XP RAM: Depends upon the size of experimental data as input, ranging from 200 Mb to 1.5 Gb Supplementary material: Sample output files, for the test run provided, are available. Classification: 7.4, 14 External routines: Dev-C++ ( http://www.bloodshed.net/devcpp.html) Nature of problem: Electron tomography of specimens for which conventional back projection may fail and/or data for which there is a limited angular

Novel automated inversion algorithm for temperature reconstruction using gas isotopes from ice cores

Directory of Open Access Journals (Sweden)

M. Döring

2018-06-01

Full Text Available Greenland past temperature history can be reconstructed by forcing the output of a firn-densification and heat-diffusion model to fit multiple gas-isotope data (δ15N or δ40Ar or δ15Nexcess extracted from ancient air in Greenland ice cores using published accumulation-rate (Acc datasets. We present here a novel methodology to solve this inverse problem, by designing a fully automated algorithm. To demonstrate the performance of this novel approach, we begin by intentionally constructing synthetic temperature histories and associated δ15N datasets, mimicking real Holocene data that we use as true values (targets to be compared to the output of the algorithm. This allows us to quantify uncertainties originating from the algorithm itself. The presented approach is completely automated and therefore minimizes the subjective impact of manual parameter tuning, leading to reproducible temperature estimates. In contrast to many other ice-core-based temperature reconstruction methods, the presented approach is completely independent from ice-core stable-water isotopes, providing the opportunity to validate water-isotope-based reconstructions or reconstructions where water isotopes are used together with δ15N or δ40Ar. We solve the inverse problem T(δ15N, Acc by using a combination of a Monte Carlo based iterative approach and the analysis of remaining mismatches between modelled and target data, based on cubic-spline filtering of random numbers and the laboratory-determined temperature sensitivity for nitrogen isotopes. Additionally, the presented reconstruction approach was tested by fitting measured δ40Ar and δ15Nexcess data, which led as well to a robust agreement between modelled and measured data. The obtained final mismatches follow a symmetric standard-distribution function. For the study on synthetic data, 95 % of the mismatches compared to the synthetic target data are in an envelope between 3.0 to 6.3 permeg for δ15N and 0.23 to 0

Optimization of the muon reconstruction algorithms for LHCb Run 2

CERN Document Server

Aaij, Roel; Dettori, Francesco; Dungs, Kevin; Lopes, Helder; Martinez Santos, Diego; Prisciandaro, Jessica; Sciascia, Barbara; Syropoulos, Vasileios; Stahl, Sascha; Vazquez Gomez, Ricardo

2017-01-01

The muon identification algorithm in the LHCb HLT software trigger and offline reconstruction has been revisited in view of the LHC Run 2. This software has undergone a significant refactorisation, resulting in a modularized common code base between the HLT and offline event processing. Because of the latter, the muon identification is now identical in HLT and offline. The HLT1 algorithm sequence has been updated given the new rate and timing constraints. Also, information from the TT subdetector is used in order to reduce ghost tracks and optimize for low $p_T$ muons. The current software is presented here together with performance studies showing improved efficiencies and reduced timing.

The reconstruction algorithm used for ["6"8Ga]PSMA-HBED-CC PET/CT reconstruction significantly influences the number of detected lymph node metastases and coeliac ganglia

International Nuclear Information System (INIS)

Krohn, Thomas; Birmes, Anita; Winz, Oliver H.; Drude, Natascha I.; Mottaghy, Felix M.; Behrendt, Florian F.; Verburg, Frederik A.

2017-01-01

To investigate whether the numbers of lymph node metastases and coeliac ganglia delineated on ["6"8Ga]PSMA-HBED-CC PET/CT scans differ among datasets generated using different reconstruction algorithms. Data were constructed using the BLOB-OS-TF, BLOB-OS and 3D-RAMLA algorithms. All reconstructions were assessed by two nuclear medicine physicians for the number of pelvic/paraaortal lymph node metastases as well the number of coeliac ganglia. Standardized uptake values (SUV) were also calculated in different regions. At least one ["6"8Ga]PSMA-HBED-CC PET/CT-positive pelvic or paraaortal lymph node metastasis was found in 49 and 35 patients using the BLOB-OS-TF algorithm, in 42 and 33 patients using the BLOB-OS algorithm, and in 41 and 31 patients using the 3D-RAMLA algorithm, respectively, and a positive ganglion was found in 92, 59 and 24 of 100 patients using the three algorithms, respectively. Quantitatively, the SUVmean and SUVmax were significantly higher with the BLOB-OS algorithm than with either the BLOB-OS-TF or the 3D-RAMLA algorithm in all measured regions (p < 0.001 for all comparisons). The differences between the SUVs with the BLOB-OS-TF- and 3D-RAMLA algorithms were not significant in the aorta (SUVmean, p = 0.93; SUVmax, p = 0.97) but were significant in all other regions (p < 0.001 in all cases). The SUVmean ganglion/gluteus ratio was significantly higher with the BLOB-OS-TF algorithm than with either the BLOB-OS or the 3D-RAMLA algorithm and was significantly higher with the BLOB-OS than with the 3D-RAMLA algorithm (p < 0.001 in all cases). The results of ["6"8Ga]PSMA-HBED-CC PET/CT are affected by the reconstruction algorithm used. The highest number of lesions and physiological structures will be visualized using a modern algorithm employing time-of-flight information. (orig.)

The reconstruction algorithm used for [{sup 68}Ga]PSMA-HBED-CC PET/CT reconstruction significantly influences the number of detected lymph node metastases and coeliac ganglia

Energy Technology Data Exchange (ETDEWEB)

Krohn, Thomas [RWTH University Hospital Aachen, Department of Nuclear Medicine, Aachen (Germany); Ulm University, Department of Nuclear Medicine, Ulm (Germany); Birmes, Anita; Winz, Oliver H.; Drude, Natascha I. [RWTH University Hospital Aachen, Department of Nuclear Medicine, Aachen (Germany); Mottaghy, Felix M. [RWTH University Hospital Aachen, Department of Nuclear Medicine, Aachen (Germany); Maastricht UMC+, Department of Nuclear Medicine, Maastricht (Netherlands); Behrendt, Florian F. [RWTH University Hospital Aachen, Department of Nuclear Medicine, Aachen (Germany); Radiology Institute ' ' Aachen Land' ' , Wuerselen (Germany); Verburg, Frederik A. [RWTH University Hospital Aachen, Department of Nuclear Medicine, Aachen (Germany); University Hospital Giessen and Marburg, Department of Nuclear Medicine, Marburg (Germany)

2017-04-15

To investigate whether the numbers of lymph node metastases and coeliac ganglia delineated on [{sup 68}Ga]PSMA-HBED-CC PET/CT scans differ among datasets generated using different reconstruction algorithms. Data were constructed using the BLOB-OS-TF, BLOB-OS and 3D-RAMLA algorithms. All reconstructions were assessed by two nuclear medicine physicians for the number of pelvic/paraaortal lymph node metastases as well the number of coeliac ganglia. Standardized uptake values (SUV) were also calculated in different regions. At least one [{sup 68}Ga]PSMA-HBED-CC PET/CT-positive pelvic or paraaortal lymph node metastasis was found in 49 and 35 patients using the BLOB-OS-TF algorithm, in 42 and 33 patients using the BLOB-OS algorithm, and in 41 and 31 patients using the 3D-RAMLA algorithm, respectively, and a positive ganglion was found in 92, 59 and 24 of 100 patients using the three algorithms, respectively. Quantitatively, the SUVmean and SUVmax were significantly higher with the BLOB-OS algorithm than with either the BLOB-OS-TF or the 3D-RAMLA algorithm in all measured regions (p < 0.001 for all comparisons). The differences between the SUVs with the BLOB-OS-TF- and 3D-RAMLA algorithms were not significant in the aorta (SUVmean, p = 0.93; SUVmax, p = 0.97) but were significant in all other regions (p < 0.001 in all cases). The SUVmean ganglion/gluteus ratio was significantly higher with the BLOB-OS-TF algorithm than with either the BLOB-OS or the 3D-RAMLA algorithm and was significantly higher with the BLOB-OS than with the 3D-RAMLA algorithm (p < 0.001 in all cases). The results of [{sup 68}Ga]PSMA-HBED-CC PET/CT are affected by the reconstruction algorithm used. The highest number of lesions and physiological structures will be visualized using a modern algorithm employing time-of-flight information. (orig.)

A Nonlinear GMRES Optimization Algorithm for Canonical Tensor Decomposition

OpenAIRE

De Sterck, Hans

2011-01-01

A new algorithm is presented for computing a canonical rank-R tensor approximation that has minimal distance to a given tensor in the Frobenius norm, where the canonical rank-R tensor consists of the sum of R rank-one components. Each iteration of the method consists of three steps. In the first step, a tentative new iterate is generated by a stand-alone one-step process, for which we use alternating least squares (ALS). In the second step, an accelerated iterate is generated by a nonlinear g...

Continuous analog of multiplicative algebraic reconstruction technique for computed tomography

Science.gov (United States)

Tateishi, Kiyoko; Yamaguchi, Yusaku; Abou Al-Ola, Omar M.; Kojima, Takeshi; Yoshinaga, Tetsuya

2016-03-01

We propose a hybrid dynamical system as a continuous analog to the block-iterative multiplicative algebraic reconstruction technique (BI-MART), which is a well-known iterative image reconstruction algorithm for computed tomography. The hybrid system is described by a switched nonlinear system with a piecewise smooth vector field or differential equation and, for consistent inverse problems, the convergence of non-negatively constrained solutions to a globally stable equilibrium is guaranteed by the Lyapunov theorem. Namely, we can prove theoretically that a weighted Kullback-Leibler divergence measure can be a common Lyapunov function for the switched system. We show that discretizing the differential equation by using the first-order approximation (Euler's method) based on the geometric multiplicative calculus leads to the same iterative formula of the BI-MART with the scaling parameter as a time-step of numerical discretization. The present paper is the first to reveal that a kind of iterative image reconstruction algorithm is constructed by the discretization of a continuous-time dynamical system for solving tomographic inverse problems. Iterative algorithms with not only the Euler method but also the Runge-Kutta methods of lower-orders applied for discretizing the continuous-time system can be used for image reconstruction. A numerical example showing the characteristics of the discretized iterative methods is presented.

An Algorithmic Approach to Total Breast Reconstruction with Free Tissue Transfer

Directory of Open Access Journals (Sweden)

Seong Cheol Yu

2013-05-01

Full Text Available As microvascular techniques continue to improve, perforator flap free tissue transfer is now the gold standard for autologous breast reconstruction. Various options are available for breast reconstruction with autologous tissue. These include the free transverse rectus abdominis myocutaneous (TRAM flap, deep inferior epigastric perforator flap, superficial inferior epigastric artery flap, superior gluteal artery perforator flap, and transverse/vertical upper gracilis flap. In addition, pedicled flaps can be very successful in the right hands and the right patient, such as the pedicled TRAM flap, latissimus dorsi flap, and thoracodorsal artery perforator. Each flap comes with its own advantages and disadvantages related to tissue properties and donor-site morbidity. Currently, the problem is how to determine the most appropriate flap for a particular patient among those potential candidates. Based on a thorough review of the literature and accumulated experiences in the author’s institution, this article provides a logical approach to autologous breast reconstruction. The algorithms presented here can be helpful to customize breast reconstruction to individual patient needs.

A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

Energy Technology Data Exchange (ETDEWEB)

Felix, Simon; Bolzern, Roman; Battaglia, Marina, E-mail: simon.felix@fhnw.ch, E-mail: roman.bolzern@fhnw.ch, E-mail: marina.battaglia@fhnw.ch [University of Applied Sciences and Arts Northwestern Switzerland FHNW, 5210 Windisch (Switzerland)

2017-11-01

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS-CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS-CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

Science.gov (United States)

Felix, Simon; Bolzern, Roman; Battaglia, Marina

2017-11-01

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS_CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS_CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

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)

The effect of 18F-FDG-PET image reconstruction algorithms on the expression of characteristic metabolic brain network in Parkinson's disease.

Science.gov (United States)

Tomše, Petra; Jensterle, Luka; Rep, Sebastijan; Grmek, Marko; Zaletel, Katja; Eidelberg, David; Dhawan, Vijay; Ma, Yilong; Trošt, Maja

2017-09-01

To evaluate the reproducibility of the expression of Parkinson's Disease Related Pattern (PDRP) across multiple sets of 18F-FDG-PET brain images reconstructed with different reconstruction algorithms. 18F-FDG-PET brain imaging was performed in two independent cohorts of Parkinson's disease (PD) patients and normal controls (NC). Slovenian cohort (20 PD patients, 20 NC) was scanned with Siemens Biograph mCT camera and reconstructed using FBP, FBP+TOF, OSEM, OSEM+TOF, OSEM+PSF and OSEM+PSF+TOF. American Cohort (20 PD patients, 7 NC) was scanned with GE Advance camera and reconstructed using 3DRP, FORE-FBP and FORE-Iterative. Expressions of two previously-validated PDRP patterns (PDRP-Slovenia and PDRP-USA) were calculated. We compared the ability of PDRP to discriminate PD patients from NC, differences and correlation between the corresponding subject scores and ROC analysis results across the different reconstruction algorithms. The expression of PDRP-Slovenia and PDRP-USA networks was significantly elevated in PD patients compared to NC (palgorithms. PDRP expression strongly correlated between all studied algorithms and the reference algorithm (r⩾0.993, palgorithms varied within 0.73 and 0.08 of the reference value for PDRP-Slovenia and PDRP-USA, respectively. ROC analysis confirmed high similarity in sensitivity, specificity and AUC among all studied reconstruction algorithms. These results show that the expression of PDRP is reproducible across a variety of reconstruction algorithms of 18F-FDG-PET brain images. PDRP is capable of providing a robust metabolic biomarker of PD for multicenter 18F-FDG-PET images acquired in the context of differential diagnosis or clinical trials. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Effect of different reconstruction algorithms on computer-aided diagnosis (CAD) performance in ultra-low dose CT colonography

International Nuclear Information System (INIS)

Lee, Eun Sun; Kim, Se Hyung; Im, Jong Pil; Kim, Sang Gyun; Shin, Cheong-il; Han, Joon Koo; Choi, Byung Ihn

2015-01-01

Highlights: •We assessed the effect of reconstruction algorithms on CAD in ultra-low dose CTC. •30 patients underwent ultra-low dose CTC using 120 and 100 kVp with 10 mAs. •CT was reconstructed with FBP, ASiR and Veo and then, we applied a CAD system. •Per-polyp sensitivity of CAD in ULD CT can be improved with the IR algorithms. •Despite of an increase in the number of FPs with IR, it was still acceptable. -- Abstract: Purpose: To assess the effect of different reconstruction algorithms on computer-aided diagnosis (CAD) performance in ultra-low-dose CT colonography (ULD CTC). Materials and methods: IRB approval and informed consents were obtained. Thirty prospectively enrolled patients underwent non-contrast CTC at 120 kVp/10 mAs in supine and 100 kVp/10 mAs in prone positions, followed by same-day colonoscopy. Images were reconstructed with filtered back projection (FBP), 80% adaptive statistical iterative reconstruction (ASIR80), and model-based iterative reconstruction (MBIR). A commercial CAD system was applied and per-polyp sensitivities and numbers of false-positives (FPs) were compared among algorithms. Results: Mean effective radiation dose of CTC was 1.02 mSv. Of 101 polyps detected and removed by colonoscopy, 61 polyps were detected on supine and on prone CTC datasets on consensus unblinded review, resulting in 122 visible polyps (32 polyps <6 mm, 52 6–9.9 mm, and 38 ≥ 10 mm). Per-polyp sensitivity of CAD for all polyps was highest with MBIR (56/122, 45.9%), followed by ASIR80 (54/122, 44.3%) and FBP (43/122, 35.2%), with significant differences between FBP and IR algorithms (P < 0.017). Per-polyp sensitivity for polyps ≥ 10 mm was also higher with MBIR (25/38, 65.8%) and ASIR80 (24/38, 63.2%) than with FBP (20/38, 58.8%), albeit without statistical significance (P > 0.017). Mean number of FPs was significantly different among algorithms (FBP, 1.4; ASIR, 2.1; MBIR, 2.4) (P = 0.011). Conclusion: Although the performance of stand-alone CAD

Effect of different reconstruction algorithms on computer-aided diagnosis (CAD) performance in ultra-low dose CT colonography

Energy Technology Data Exchange (ETDEWEB)

Lee, Eun Sun [Department of Radiology, Seoul National University Hospital (Korea, Republic of); Institute of Radiation Medicine, Seoul National University Hospital (Korea, Republic of); Kim, Se Hyung, E-mail: shkim7071@gmail.com [Department of Radiology, Seoul National University Hospital (Korea, Republic of); Institute of Radiation Medicine, Seoul National University Hospital (Korea, Republic of); Im, Jong Pil; Kim, Sang Gyun [Department of Internal Medicine, Seoul National University Hospital (Korea, Republic of); Shin, Cheong-il; Han, Joon Koo; Choi, Byung Ihn [Department of Radiology, Seoul National University Hospital (Korea, Republic of); Institute of Radiation Medicine, Seoul National University Hospital (Korea, Republic of)

2015-04-15

Highlights: •We assessed the effect of reconstruction algorithms on CAD in ultra-low dose CTC. •30 patients underwent ultra-low dose CTC using 120 and 100 kVp with 10 mAs. •CT was reconstructed with FBP, ASiR and Veo and then, we applied a CAD system. •Per-polyp sensitivity of CAD in ULD CT can be improved with the IR algorithms. •Despite of an increase in the number of FPs with IR, it was still acceptable. -- Abstract: Purpose: To assess the effect of different reconstruction algorithms on computer-aided diagnosis (CAD) performance in ultra-low-dose CT colonography (ULD CTC). Materials and methods: IRB approval and informed consents were obtained. Thirty prospectively enrolled patients underwent non-contrast CTC at 120 kVp/10 mAs in supine and 100 kVp/10 mAs in prone positions, followed by same-day colonoscopy. Images were reconstructed with filtered back projection (FBP), 80% adaptive statistical iterative reconstruction (ASIR80), and model-based iterative reconstruction (MBIR). A commercial CAD system was applied and per-polyp sensitivities and numbers of false-positives (FPs) were compared among algorithms. Results: Mean effective radiation dose of CTC was 1.02 mSv. Of 101 polyps detected and removed by colonoscopy, 61 polyps were detected on supine and on prone CTC datasets on consensus unblinded review, resulting in 122 visible polyps (32 polyps <6 mm, 52 6–9.9 mm, and 38 ≥ 10 mm). Per-polyp sensitivity of CAD for all polyps was highest with MBIR (56/122, 45.9%), followed by ASIR80 (54/122, 44.3%) and FBP (43/122, 35.2%), with significant differences between FBP and IR algorithms (P < 0.017). Per-polyp sensitivity for polyps ≥ 10 mm was also higher with MBIR (25/38, 65.8%) and ASIR80 (24/38, 63.2%) than with FBP (20/38, 58.8%), albeit without statistical significance (P > 0.017). Mean number of FPs was significantly different among algorithms (FBP, 1.4; ASIR, 2.1; MBIR, 2.4) (P = 0.011). Conclusion: Although the performance of stand-alone CAD

Effect of filters and reconstruction algorithms on I-124 PET in Siemens Inveon PET scanner

Science.gov (United States)

Ram Yu, A.; Kim, Jin Su

2015-10-01

Purpose: To assess the effects of filtering and reconstruction on Siemens I-124 PET data. Methods: A Siemens Inveon PET was used. Spatial resolution of I-124 was measured to a transverse offset of 50 mm from the center FBP, 2D ordered subset expectation maximization (OSEM2D), 3D re-projection algorithm (3DRP), and maximum a posteriori (MAP) methods were tested. Non-uniformity (NU), recovery coefficient (RC), and spillover ratio (SOR) parameterized image quality. Mini deluxe phantom data of I-124 was also assessed. Results: Volumetric resolution was 7.3 mm3 from the transverse FOV center when FBP reconstruction algorithms with ramp filter was used. MAP yielded minimal NU with β =1.5. OSEM2D yielded maximal RC. SOR was below 4% for FBP with ramp, Hamming, Hanning, or Shepp-Logan filters. Based on the mini deluxe phantom results, an FBP with Hanning or Parzen filters, or a 3DRP with Hanning filter yielded feasible I-124 PET data.Conclusions: Reconstruction algorithms and filters were compared. FBP with Hanning or Parzen filters, or 3DRP with Hanning filter yielded feasible data for quantifying I-124 PET.

An efficient algorithm for some highly nonlinear fractional PDEs in mathematical physics.

Directory of Open Access Journals (Sweden)

Jamshad Ahmad

Full Text Available In this paper, a fractional complex transform (FCT is used to convert the given fractional partial differential equations (FPDEs into corresponding partial differential equations (PDEs and subsequently Reduced Differential Transform Method (RDTM is applied on the transformed system of linear and nonlinear time-fractional PDEs. The results so obtained are re-stated by making use of inverse transformation which yields it in terms of original variables. It is observed that the proposed algorithm is highly efficient and appropriate for fractional PDEs and hence can be extended to other complex problems of diversified nonlinear nature.

Algorithmic aspects for the reconstruction of spatio-spectral data cubes in the perspective of the SKA

Science.gov (United States)

Mary, D.; Ferrari, A.; Ferrari, C.; Deguignet, J.; Vannier, M.

2016-12-01

With millions of receivers leading to TerraByte data cubes, the story of the giant SKA telescope is also that of collaborative efforts from radioastronomy, signal processing, optimization and computer sciences. Reconstructing SKA cubes poses two challenges. First, the majority of existing algorithms work in 2D and cannot be directly translated into 3D. Second, the reconstruction implies solving an inverse problem and it is not clear what ultimate limit we can expect on the error of this solution. This study addresses (of course partially) both challenges. We consider an extremely simple data acquisition model, and we focus on strategies making it possible to implement 3D reconstruction algorithms that use state-of-the-art image/spectral regularization. The proposed approach has two main features: (i) reduced memory storage with respect to a previous approach; (ii) efficient parallelization and ventilation of the computational load over the spectral bands. This work will allow to implement and compare various 3D reconstruction approaches in a large scale framework.

Principle and Reconstruction Algorithm for Atomic-Resolution Holography

Science.gov (United States)

Matsushita, Tomohiro; Muro, Takayuki; Matsui, Fumihiko; Happo, Naohisa; Hosokawa, Shinya; Ohoyama, Kenji; Sato-Tomita, Ayana; Sasaki, Yuji C.; Hayashi, Kouichi

2018-06-01

Atomic-resolution holography makes it possible to obtain the three-dimensional (3D) structure around a target atomic site. Translational symmetry of the atomic arrangement of the sample is not necessary, and the 3D atomic image can be measured when the local structure of the target atomic site is oriented. Therefore, 3D local atomic structures such as dopants and adsorbates are observable. Here, the atomic-resolution holography comprising photoelectron holography, X-ray fluorescence holography, neutron holography, and their inverse modes are treated. Although the measurement methods are different, they can be handled with a unified theory. The algorithm for reconstructing 3D atomic images from holograms plays an important role. Although Fourier transform-based methods have been proposed, they require the multiple-energy holograms. In addition, they cannot be directly applied to photoelectron holography because of the phase shift problem. We have developed methods based on the fitting method for reconstructing from single-energy and photoelectron holograms. The developed methods are applicable to all types of atomic-resolution holography.

An Algorithmic Comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a Nonlinear Thermal Problem

Directory of Open Access Journals (Sweden)

Felix Fritzen

2018-02-01

Full Text Available A novel algorithmic discussion of the methodological and numerical differences of competing parametric model reduction techniques for nonlinear problems is presented. First, the Galerkin reduced basis (RB formulation is presented, which fails at providing significant gains with respect to the computational efficiency for nonlinear problems. Renowned methods for the reduction of the computing time of nonlinear reduced order models are the Hyper-Reduction and the (Discrete Empirical Interpolation Method (EIM, DEIM. An algorithmic description and a methodological comparison of both methods are provided. The accuracy of the predictions of the hyper-reduced model and the (DEIM in comparison to the Galerkin RB is investigated. All three approaches are applied to a simple uncertainty quantification of a planar nonlinear thermal conduction problem. The results are compared to computationally intense finite element simulations.

Quantum associative memory with linear and non-linear algorithms for the diagnosis of some tropical diseases.

Science.gov (United States)

Tchapet Njafa, J-P; Nana Engo, S G

2018-01-01

This paper presents the QAMDiagnos, a model of Quantum Associative Memory (QAM) that can be a helpful tool for medical staff without experience or laboratory facilities, for the diagnosis of four tropical diseases (malaria, typhoid fever, yellow fever and dengue) which have several similar signs and symptoms. The memory can distinguish a single infection from a polyinfection. Our model is a combination of the improved versions of the original linear quantum retrieving algorithm proposed by Ventura and the non-linear quantum search algorithm of Abrams and Lloyd. From the given simulation results, it appears that the efficiency of recognition is good when particular signs and symptoms of a disease are inserted given that the linear algorithm is the main algorithm. The non-linear algorithm helps confirm or correct the diagnosis or give some advice to the medical staff for the treatment. So, our QAMDiagnos that has a friendly graphical user interface for desktop and smart-phone is a sensitive and a low-cost diagnostic tool that enables rapid and accurate diagnosis of four tropical diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

A system for the 3D reconstruction of retracted-septa PET data using the EM algorithm

International Nuclear Information System (INIS)

Johnson, C.A.; Yan, Y.; Carson, R.E.; Martino, R.L.; Daube-Witherspoon, M.E.

1995-01-01

The authors have implemented the EM reconstruction algorithm for volume acquisition from current generation retracted-septa PET scanners. Although the software was designed for a GE Advance scanner, it is easily adaptable to other 3D scanners. The reconstruction software was written for an Intel iPSC/860 parallel computer with 128 compute nodes. Running on 32 processors, the algorithm requires approximately 55 minutes per iteration to reconstruct a 128 x 128 x 35 image. No projection data compression schemes or other approximations were used in the implementation. Extensive use of EM system matrix (C ij ) symmetries (including the 8-fold in-plane symmetries, 2-fold axial symmetries, and axial parallel line redundancies) reduces the storage cost by a factor of 188. The parallel algorithm operates on distributed projection data which are decomposed by base-symmetry angles. Symmetry operators copy and index the C ij chord to the form required for the particular symmetry. The use of asynchronous reads, lookup tables, and optimized image indexing improves computational performance

A sparsity-based iterative algorithm for reconstruction of micro-CT images from highly undersampled projection datasets obtained with a synchrotron X-ray source

Science.gov (United States)

Melli, S. Ali; Wahid, Khan A.; Babyn, Paul; Cooper, David M. L.; Gopi, Varun P.

2016-12-01

Synchrotron X-ray Micro Computed Tomography (Micro-CT) is an imaging technique which is increasingly used for non-invasive in vivo preclinical imaging. However, it often requires a large number of projections from many different angles to reconstruct high-quality images leading to significantly high radiation doses and long scan times. To utilize this imaging technique further for in vivo imaging, we need to design reconstruction algorithms that reduce the radiation dose and scan time without reduction of reconstructed image quality. This research is focused on using a combination of gradient-based Douglas-Rachford splitting and discrete wavelet packet shrinkage image denoising methods to design an algorithm for reconstruction of large-scale reduced-view synchrotron Micro-CT images with acceptable quality metrics. These quality metrics are computed by comparing the reconstructed images with a high-dose reference image reconstructed from 1800 equally spaced projections spanning 180°. Visual and quantitative-based performance assessment of a synthetic head phantom and a femoral cortical bone sample imaged in the biomedical imaging and therapy bending magnet beamline at the Canadian Light Source demonstrates that the proposed algorithm is superior to the existing reconstruction algorithms. Using the proposed reconstruction algorithm to reduce the number of projections in synchrotron Micro-CT is an effective way to reduce the overall radiation dose and scan time which improves in vivo imaging protocols.

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

International Nuclear Information System (INIS)

Huang Jin-Wang; Feng Jiu-Chao

2014-01-01

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF. (general)

Development of imaging and reconstructions algorithms on parallel processing architectures for applications in non-destructive testing

International Nuclear Information System (INIS)

Pedron, Antoine

2013-01-01

This thesis work is placed between the scientific domain of ultrasound non-destructive testing and algorithm-architecture adequation. Ultrasound non-destructive testing includes a group of analysis techniques used in science and industry to evaluate the properties of a material, component, or system without causing damage. In order to characterise possible defects, determining their position, size and shape, imaging and reconstruction tools have been developed at CEA-LIST, within the CIVA software platform. Evolution of acquisition sensors implies a continuous growth of datasets and consequently more and more computing power is needed to maintain interactive reconstructions. General purpose processors (GPP) evolving towards parallelism and emerging architectures such as GPU allow large acceleration possibilities than can be applied to these algorithms. The main goal of the thesis is to evaluate the acceleration than can be obtained for two reconstruction algorithms on these architectures. These two algorithms differ in their parallelization scheme. The first one can be properly parallelized on GPP whereas on GPU, an intensive use of atomic instructions is required. Within the second algorithm, parallelism is easier to express, but loop ordering on GPP, as well as thread scheduling and a good use of shared memory on GPU are necessary in order to obtain efficient results. Different API or libraries, such as OpenMP, CUDA and OpenCL are evaluated through chosen benchmarks. An integration of both algorithms in the CIVA software platform is proposed and different issues related to code maintenance and durability are discussed. (author) [fr

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

Science.gov (United States)

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

2015-11-01

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

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.

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

Science.gov (United States)

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

2014-05-01

To evaluate the influence of radiation dose settings and reconstruction algorithms on the measurement accuracy and reproducibility of semi-automated pulmonary nodule volumetry. CT scans were performed on a chest phantom containing various nodules (10 and 12mm; +100, -630 and -800HU) at 120kVp with tube current-time settings of 10, 20, 50, and 100mAs. Each CT was reconstructed using filtered back projection (FBP), iDose(4) and iterative model reconstruction (IMR). Semi-automated volumetry was performed by two radiologists using commercial volumetry software for nodules at each CT dataset. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. The absolute percentage measurement errors and differences were then calculated for volume and mass. The influence of radiation dose and reconstruction algorithm on measurement accuracy, reproducibility and objective image quality metrics was analyzed using generalized estimating equations. Measurement accuracy and reproducibility of nodule volume and mass were not significantly associated with CT radiation dose settings or reconstruction algorithms (p>0.05). Objective image quality metrics of CT images were superior in IMR than in FBP or iDose(4) at all radiation dose settings (pvolumetry can be applied to low- or ultralow-dose chest CT with usage of a novel iterative reconstruction algorithm without losing measurement accuracy and reproducibility. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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.

A spectral image processing algorithm for evaluating the influence of the illuminants on the reconstructed reflectance

Science.gov (United States)

Toadere, Florin

2017-12-01

A spectral image processing algorithm that allows the illumination of the scene with different illuminants together with the reconstruction of the scene's reflectance is presented. Color checker spectral image and CIE A (warm light 2700 K), D65 (cold light 6500 K) and Cree TW Series LED T8 (4000 K) are employed for scene illumination. Illuminants used in the simulations have different spectra and, as a result of their illumination, the colors of the scene change. The influence of the illuminants on the reconstruction of the scene's reflectance is estimated. Demonstrative images and reflectance showing the operation of the algorithm are illustrated.

A neural network image reconstruction technique for electrical impedance tomography

International Nuclear Information System (INIS)

Adler, A.; Guardo, R.

1994-01-01

Reconstruction of Images in Electrical Impedance Tomography requires the solution of a nonlinear inverse problem on noisy data. This problem is typically ill-conditioned and requires either simplifying assumptions or regularization based on a priori knowledge. This paper presents a reconstruction algorithm using neural network techniques which calculates a linear approximation of the inverse problem directly from finite element simulations of the forward problem. This inverse is adapted to the geometry of the medium and the signal-to-noise ratio (SNR) used during network training. Results show good conductivity reconstruction where measurement SNR is similar to the training conditions. The advantages of this method are its conceptual simplicity and ease of implementation, and the ability to control the compromise between the noise performance and resolution of the image reconstruction

Rational approximations for tomographic reconstructions

International Nuclear Information System (INIS)

Reynolds, Matthew; Beylkin, Gregory; Monzón, Lucas

2013-01-01

We use optimal rational approximations of projection data collected in x-ray tomography to improve image resolution. Under the assumption that the object of interest is described by functions with jump discontinuities, for each projection we construct its rational approximation with a small (near optimal) number of terms for a given accuracy threshold. This allows us to augment the measured data, i.e., double the number of available samples in each projection or, equivalently, extend (double) the domain of their Fourier transform. We also develop a new, fast, polar coordinate Fourier domain algorithm which uses our nonlinear approximation of projection data in a natural way. Using augmented projections of the Shepp–Logan phantom, we provide a comparison between the new algorithm and the standard filtered back-projection algorithm. We demonstrate that the reconstructed image has improved resolution without additional artifacts near sharp transitions in the image. (paper)

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

International Nuclear Information System (INIS)

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

1990-01-01

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

Computed Tomography Image Quality Evaluation of a New Iterative Reconstruction Algorithm in the Abdomen (Adaptive Statistical Iterative Reconstruction-V) a Comparison With Model-Based Iterative Reconstruction, Adaptive Statistical Iterative Reconstruction, and Filtered Back Projection Reconstructions.

Science.gov (United States)

Goodenberger, Martin H; Wagner-Bartak, Nicolaus A; Gupta, Shiva; Liu, Xinming; Yap, Ramon Q; Sun, Jia; Tamm, Eric P; Jensen, Corey T

The purpose of this study was to compare abdominopelvic computed tomography images reconstructed with adaptive statistical iterative reconstruction-V (ASIR-V) with model-based iterative reconstruction (Veo 3.0), ASIR, and filtered back projection (FBP). Abdominopelvic computed tomography scans for 36 patients (26 males and 10 females) were reconstructed using FBP, ASIR (80%), Veo 3.0, and ASIR-V (30%, 60%, 90%). Mean ± SD patient age was 32 ± 10 years with mean ± SD body mass index of 26.9 ± 4.4 kg/m. Images were reviewed by 2 independent readers in a blinded, randomized fashion. Hounsfield unit, noise, and contrast-to-noise ratio (CNR) values were calculated for each reconstruction algorithm for further comparison. Phantom evaluation of low-contrast detectability (LCD) and high-contrast resolution was performed. Adaptive statistical iterative reconstruction-V 30%, ASIR-V 60%, and ASIR 80% were generally superior qualitatively compared with ASIR-V 90%, Veo 3.0, and FBP (P ASIR-V 60% with respective CNR values of 5.54 ± 2.39, 8.78 ± 3.15, and 3.49 ± 1.77 (P ASIR 80% had the best and worst spatial resolution, respectively. Adaptive statistical iterative reconstruction-V 30% and ASIR-V 60% provided the best combination of qualitative and quantitative performance. Adaptive statistical iterative reconstruction 80% was equivalent qualitatively, but demonstrated inferior spatial resolution and LCD.

Three-dimensional digital tomosynthesis iterative reconstruction, artifact reduction and alternative acquisition geometry

CERN Document Server

Levakhina, Yulia

2014-01-01

Yulia Levakhina gives an introduction to the major challenges of image reconstruction in Digital Tomosynthesis (DT), particularly to the connection of the reconstruction problem with the incompleteness of the DT dataset. The author discusses the factors which cause the formation of limited angle artifacts and proposes how to account for them in order to improve image quality and axial resolution of modern DT. The addressed methods include a weighted non-linear back projection scheme for algebraic reconstruction and?novel dual-axis acquisition geometry. All discussed algorithms and methods are supplemented by detailed illustrations, hints for practical implementation, pseudo-code, simulation results and real patient case examples.

Research on Modified Root-MUSIC Algorithm of DOA Estimation Based on Covariance Matrix Reconstruction

Directory of Open Access Journals (Sweden)

Changgan SHU

2014-09-01

Full Text Available In the standard root multiple signal classification algorithm, the performance of direction of arrival estimation will reduce and even lose effect in circumstances that a low signal noise ratio and a small signals interval. By reconstructing and weighting the covariance matrix of received signal, the modified algorithm can provide more accurate estimation results. The computer simulation and performance analysis are given next, which show that under the condition of lower signal noise ratio and stronger correlation between signals, the proposed modified algorithm could provide preferable azimuth estimating performance than the standard method.

Comparison of the effects of model-based iterative reconstruction and filtered back projection algorithms on software measurements in pulmonary subsolid nodules.

Science.gov (United States)

Cohen, Julien G; Kim, Hyungjin; Park, Su Bin; van Ginneken, Bram; Ferretti, Gilbert R; Lee, Chang Hyun; Goo, Jin Mo; Park, Chang Min

2017-08-01

To evaluate the differences between filtered back projection (FBP) and model-based iterative reconstruction (MBIR) algorithms on semi-automatic measurements in subsolid nodules (SSNs). Unenhanced CT scans of 73 SSNs obtained using the same protocol and reconstructed with both FBP and MBIR algorithms were evaluated by two radiologists. Diameter, mean attenuation, mass and volume of whole nodules and their solid components were measured. Intra- and interobserver variability and differences between FBP and MBIR were then evaluated using Bland-Altman method and Wilcoxon tests. Longest diameter, volume and mass of nodules and those of their solid components were significantly higher using MBIR (p algorithms with respect to the diameter, volume and mass of nodules and their solid components. There were no significant differences in intra- or interobserver variability between FBP and MBIR (p > 0.05). Semi-automatic measurements of SSNs significantly differed between FBP and MBIR; however, the differences were within the range of measurement variability. • Intra- and interobserver reproducibility of measurements did not differ between FBP and MBIR. • Differences in SSNs' semi-automatic measurement induced by reconstruction algorithms were not clinically significant. • Semi-automatic measurement may be conducted regardless of reconstruction algorithm. • SSNs' semi-automated classification agreement (pure vs. part-solid) did not significantly differ between algorithms.

Comparison between different tomographic reconstruction algorithms in nuclear medicine imaging; Comparacion entre distintos algoritmos de reconstruccion tomografica en imagenes de medicina nuclear

Energy Technology Data Exchange (ETDEWEB)

Llacer Martos, S.; Herraiz Lablanca, M. D.; Puchal Ane, R.

2011-07-01

This paper compares the image quality obtained with each of the algorithms is evaluated and its running time, to optimize the choice of algorithm to use taking into account both the quality of the reconstructed image as the time spent on the reconstruction.

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

International Nuclear Information System (INIS)

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

2014-01-01

Purpose: To evaluate the influence of radiation dose settings and reconstruction algorithms on the measurement accuracy and reproducibility of semi-automated pulmonary nodule volumetry. Materials and methods: CT scans were performed on a chest phantom containing various nodules (10 and 12 mm; +100, −630 and −800 HU) at 120 kVp with tube current–time settings of 10, 20, 50, and 100 mAs. Each CT was reconstructed using filtered back projection (FBP), iDose 4 and iterative model reconstruction (IMR). Semi-automated volumetry was performed by two radiologists using commercial volumetry software for nodules at each CT dataset. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. The absolute percentage measurement errors and differences were then calculated for volume and mass. The influence of radiation dose and reconstruction algorithm on measurement accuracy, reproducibility and objective image quality metrics was analyzed using generalized estimating equations. Results: Measurement accuracy and reproducibility of nodule volume and mass were not significantly associated with CT radiation dose settings or reconstruction algorithms (p > 0.05). Objective image quality metrics of CT images were superior in IMR than in FBP or iDose 4 at all radiation dose settings (p < 0.05). Conclusion: Semi-automated nodule volumetry can be applied to low- or ultralow-dose chest CT with usage of a novel iterative reconstruction algorithm without losing measurement accuracy and reproducibility

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

Energy Technology Data Exchange (ETDEWEB)

Kim, Hyungjin, E-mail: khj.snuh@gmail.com [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Park, Chang Min, E-mail: cmpark@radiol.snu.ac.kr [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Cancer Research Institute, Seoul National University, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Song, Yong Sub, E-mail: terasong@gmail.com [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Lee, Sang Min, E-mail: sangmin.lee.md@gmail.com [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Goo, Jin Mo, E-mail: jmgoo@plaza.snu.ac.kr [Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of); Cancer Research Institute, Seoul National University, 101, Daehangno, Jongno-gu, Seoul 110-744 (Korea, Republic of)

2014-05-15

Purpose: To evaluate the influence of radiation dose settings and reconstruction algorithms on the measurement accuracy and reproducibility of semi-automated pulmonary nodule volumetry. Materials and methods: CT scans were performed on a chest phantom containing various nodules (10 and 12 mm; +100, −630 and −800 HU) at 120 kVp with tube current–time settings of 10, 20, 50, and 100 mAs. Each CT was reconstructed using filtered back projection (FBP), iDose{sup 4} and iterative model reconstruction (IMR). Semi-automated volumetry was performed by two radiologists using commercial volumetry software for nodules at each CT dataset. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. The absolute percentage measurement errors and differences were then calculated for volume and mass. The influence of radiation dose and reconstruction algorithm on measurement accuracy, reproducibility and objective image quality metrics was analyzed using generalized estimating equations. Results: Measurement accuracy and reproducibility of nodule volume and mass were not significantly associated with CT radiation dose settings or reconstruction algorithms (p > 0.05). Objective image quality metrics of CT images were superior in IMR than in FBP or iDose{sup 4} at all radiation dose settings (p < 0.05). Conclusion: Semi-automated nodule volumetry can be applied to low- or ultralow-dose chest CT with usage of a novel iterative reconstruction algorithm without losing measurement accuracy and reproducibility.

Data Reduction Algorithm Using Nonnegative Matrix Factorization with Nonlinear Constraints

Science.gov (United States)

Sembiring, Pasukat

2017-12-01

Processing ofdata with very large dimensions has been a hot topic in recent decades. Various techniques have been proposed in order to execute the desired information or structure. Non- Negative Matrix Factorization (NMF) based on non-negatives data has become one of the popular methods for shrinking dimensions. The main strength of this method is non-negative object, the object model by a combination of some basic non-negative parts, so as to provide a physical interpretation of the object construction. The NMF is a dimension reduction method thathasbeen used widely for numerous applications including computer vision,text mining, pattern recognitions,and bioinformatics. Mathematical formulation for NMF did not appear as a convex optimization problem and various types of algorithms have been proposed to solve the problem. The Framework of Alternative Nonnegative Least Square(ANLS) are the coordinates of the block formulation approaches that have been proven reliable theoretically and empirically efficient. This paper proposes a new algorithm to solve NMF problem based on the framework of ANLS.This algorithm inherits the convergenceproperty of the ANLS framework to nonlinear constraints NMF formulations.

A trial to reduce cardiac motion artifact on HR-CT images of the lung with the use of subsecond scan and special cine reconstruction algorithm

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Sakai, Fumikazu; Tsuuchi, Yasuhiko; Suzuki, Keiko; Ueno, Keiko; Yamada, Takayuki; Okawa, Tomohiko [Tokyo Women`s Medical Coll. (Japan); Yun, Shen; Horiuchi, Tetsuya; Kimura, Fumiko

1998-05-01

We describe our trial to reduce cardiac motion artifacts on HR-CT images caused by cardiac pulsation by combining use of subsecond CT (scan time 0.8 s) and a special cine reconstruction algorithm (cine reconstruction algorithm with 180-degree helical interpolation). Eleven to 51 HR-CT images were reconstructed with the special cine reconstruction algorithm at the pitch of 0.1 (0.08 s) from the data obtained by two to six contigious rotation scans at the same level. Images with the fewest cardiac motion artifacts were selected for evaluation. These images were compared with those reconstructed with a conventional cine reconstruction algorithm and step-by-step scan. In spite of its increased radiation exposure, technical complexity and slight degradation of spatial resolution, our method was useful in reducing cardiac motion artifacts on HR-CT images in regions adjacent to the heart. (author)

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.

A Numerical Algorithm for Solving a Four-Point Nonlinear Fractional Integro-Differential Equations

Directory of Open Access Journals (Sweden)

Er Gao

2012-01-01

Full Text Available We provide a new algorithm for a four-point nonlocal boundary value problem of nonlinear integro-differential equations of fractional order q∈(1,2] based on reproducing kernel space method. According to our work, the analytical solution of the equations is represented in the reproducing kernel space which we construct and so the n-term approximation. At the same time, the n-term approximation is proved to converge to the analytical solution. An illustrative example is also presented, which shows that the new algorithm is efficient and accurate.

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

Science.gov (United States)

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

1998-06-01

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

Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA).

Science.gov (United States)

Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A

2015-02-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.

Continuous Analog of Accelerated OS-EM Algorithm for Computed Tomography

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Kiyoko Tateishi

2017-01-01

Full Text Available The maximum-likelihood expectation-maximization (ML-EM algorithm is used for an iterative image reconstruction (IIR method and performs well with respect to the inverse problem as cross-entropy minimization in computed tomography. For accelerating the convergence rate of the ML-EM, the ordered-subsets expectation-maximization (OS-EM with a power factor is effective. In this paper, we propose a continuous analog to the power-based accelerated OS-EM algorithm. The continuous-time image reconstruction (CIR system is described by nonlinear differential equations with piecewise smooth vector fields by a cyclic switching process. A numerical discretization of the differential equation by using the geometric multiplicative first-order expansion of the nonlinear vector field leads to an exact equivalent iterative formula of the power-based OS-EM. The convergence of nonnegatively constrained solutions to a globally stable equilibrium is guaranteed by the Lyapunov theorem for consistent inverse problems. We illustrate through numerical experiments that the convergence characteristics of the continuous system have the highest quality compared with that of discretization methods. We clarify how important the discretization method approximates the solution of the CIR to design a better IIR method.

Algorithms for adaptive nonlinear pattern recognition

Science.gov (United States)

Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary

2011-09-01

In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral

Applicability of a set of tomographic reconstruction algorithms for quantitative SPECT on irradiated nuclear fuel assemblies

Energy Technology Data Exchange (ETDEWEB)

Jacobsson Svärd, Staffan, E-mail: staffan.jacobsson_svard@physics.uu.se; Holcombe, Scott; Grape, Sophie

2015-05-21

A fuel assembly operated in a nuclear power plant typically contains 100–300 fuel rods, depending on fuel type, which become strongly radioactive during irradiation in the reactor core. For operational and security reasons, it is of interest to experimentally deduce rod-wise information from the fuel, preferably by means of non-destructive measurements. The tomographic SPECT technique offers such possibilities through its two-step application; (1) recording the gamma-ray flux distribution around the fuel assembly, and (2) reconstructing the assembly's internal source distribution, based on the recorded radiation field. In this paper, algorithms for performing the latter step and extracting quantitative relative rod-by-rod data are accounted for. As compared to application of SPECT in nuclear medicine, nuclear fuel assemblies present a much more heterogeneous distribution of internal attenuation to gamma radiation than the human body, typically with rods containing pellets of heavy uranium dioxide surrounded by cladding of a zirconium alloy placed in water or air. This inhomogeneity severely complicates the tomographic quantification of the rod-wise relative source content, and the deduction of conclusive data requires detailed modelling of the attenuation to be introduced in the reconstructions. However, as shown in this paper, simplified models may still produce valuable information about the fuel. Here, a set of reconstruction algorithms for SPECT on nuclear fuel assemblies are described and discussed in terms of their quantitative performance for two applications; verification of fuel assemblies' completeness in nuclear safeguards, and rod-wise fuel characterization. It is argued that a request not to base the former assessment on any a priori information brings constraints to which reconstruction methods that may be used in that case, whereas the use of a priori information on geometry and material content enables highly accurate quantitative

Development of an image reconstruction algorithm for a few number of projection data

International Nuclear Information System (INIS)

Vieira, Wilson S.; Brandao, Luiz E.; Braz, Delson

2007-01-01

An image reconstruction algorithm was developed for specific cases of radiotracer applications in industry (rotating cylindrical mixers), involving a very few number of projection data. The algorithm was planned for imaging radioactive isotope distributions around the center of circular planes. The method consists of adapting the original expectation maximization algorithm (EM) to solve the ill-posed emission tomography inverse problem in order to reconstruct transversal 2D images of an object with only four projections. To achieve this aim, counts of photons emitted by selected radioactive sources in the plane, after they had been simulated using the commercial software MICROSHIELD 5.05, constitutes the projections and a computational code (SPECTEM) was developed to generate activity vectors or images related to those sources. SPECTEM is flexible to support simultaneous changes of the detectors's geometry, the medium under investigation and the properties of the gamma radiation. As a consequence of the code had been followed correctly the proposed method, good results were obtained and they encouraged us to continue the next step of the research: the validation of SPECTEM utilizing experimental data to check its real performance. We aim this code will improve considerably radiotracer methodology, making easier the diagnosis of fails in industrial processes. (author)

Development of an image reconstruction algorithm for a few number of projection data

Energy Technology Data Exchange (ETDEWEB)

Vieira, Wilson S.; Brandao, Luiz E. [Instituto de Engenharia Nuclear (IEN-CNEN/RJ), Rio de Janeiro , RJ (Brazil)]. E-mails: wilson@ien.gov.br; brandao@ien.gov.br; Braz, Delson [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programa de Pos-graduacao de Engenharia (COPPE). Lab. de Instrumentacao Nuclear]. E-mail: delson@mailhost.lin.ufrj.br

2007-07-01

An image reconstruction algorithm was developed for specific cases of radiotracer applications in industry (rotating cylindrical mixers), involving a very few number of projection data. The algorithm was planned for imaging radioactive isotope distributions around the center of circular planes. The method consists of adapting the original expectation maximization algorithm (EM) to solve the ill-posed emission tomography inverse problem in order to reconstruct transversal 2D images of an object with only four projections. To achieve this aim, counts of photons emitted by selected radioactive sources in the plane, after they had been simulated using the commercial software MICROSHIELD 5.05, constitutes the projections and a computational code (SPECTEM) was developed to generate activity vectors or images related to those sources. SPECTEM is flexible to support simultaneous changes of the detectors's geometry, the medium under investigation and the properties of the gamma radiation. As a consequence of the code had been followed correctly the proposed method, good results were obtained and they encouraged us to continue the next step of the research: the validation of SPECTEM utilizing experimental data to check its real performance. We aim this code will improve considerably radiotracer methodology, making easier the diagnosis of fails in industrial processes. (author)

The use of anatomical information for molecular image reconstruction algorithms: Attention/Scatter correction, motion compensation, and noise reduction

Energy Technology Data Exchange (ETDEWEB)

Chun, Se Young [School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan (Korea, Republic of)

2016-03-15

PET and SPECT are important tools for providing valuable molecular information about patients to clinicians. Advances in nuclear medicine hardware technologies and statistical image reconstruction algorithms enabled significantly improved image quality. Sequentially or simultaneously acquired anatomical images such as CT and MRI from hybrid scanners are also important ingredients for improving the image quality of PET or SPECT further. High-quality anatomical information has been used and investigated for attenuation and scatter corrections, motion compensation, and noise reduction via post-reconstruction filtering and regularization in inverse problems. In this article, we will review works using anatomical information for molecular image reconstruction algorithms for better image quality by describing mathematical models, discussing sources of anatomical information for different cases, and showing some examples.

Image-based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET.

Science.gov (United States)

Rapisarda, E; Bettinardi, V; Thielemans, K; Gilardi, M C

2010-07-21

The interest in positron emission tomography (PET) and particularly in hybrid integrated PET/CT systems has significantly increased in the last few years due to the improved quality of the obtained images. Nevertheless, one of the most important limits of the PET imaging technique is still its poor spatial resolution due to several physical factors originating both at the emission (e.g. positron range, photon non-collinearity) and at detection levels (e.g. scatter inside the scintillating crystals, finite dimensions of the crystals and depth of interaction). To improve the spatial resolution of the images, a possible way consists of measuring the point spread function (PSF) of the system and then accounting for it inside the reconstruction algorithm. In this work, the system response of the GE Discovery STE operating in 3D mode has been characterized by acquiring (22)Na point sources in different positions of the scanner field of view. An image-based model of the PSF was then obtained by fitting asymmetric two-dimensional Gaussians on the (22)Na images reconstructed with small pixel sizes. The PSF was then incorporated, at the image level, in a three-dimensional ordered subset maximum likelihood expectation maximization (OS-MLEM) reconstruction algorithm. A qualitative and quantitative validation of the algorithm accounting for the PSF has been performed on phantom and clinical data, showing improved spatial resolution, higher contrast and lower noise compared with the corresponding images obtained using the standard OS-MLEM algorithm.

Experimental study of stochastic noise propagation in SPECT images reconstructed using the conjugate gradient algorithm.

Science.gov (United States)

Mariano-Goulart, D; Fourcade, M; Bernon, J L; Rossi, M; Zanca, M

2003-01-01

Thanks to an experimental study based on simulated and physical phantoms, the propagation of the stochastic noise in slices reconstructed using the conjugate gradient algorithm has been analysed versus iterations. After a first increase corresponding to the reconstruction of the signal, the noise stabilises before increasing linearly with iterations. The level of the plateau as well as the slope of the subsequent linear increase depends on the noise in the projection data.

A joint Richardson—Lucy deconvolution algorithm for the reconstruction of multifocal structured illumination microscopy data

International Nuclear Information System (INIS)

Ströhl, Florian; Kaminski, Clemens F

2015-01-01

We demonstrate the reconstruction of images obtained by multifocal structured illumination microscopy, MSIM, using a joint Richardson–Lucy, jRL-MSIM, deconvolution algorithm, which is based on an underlying widefield image-formation model. The method is efficient in the suppression of out-of-focus light and greatly improves image contrast and resolution. Furthermore, it is particularly well suited for the processing of noise corrupted data. The principle is verified on simulated as well as experimental data and a comparison of the jRL-MSIM approach with the standard reconstruction procedure, which is based on image scanning microscopy, ISM, is made. Our algorithm is efficient and freely available in a user friendly software package. (paper)

Algebraic dynamics solutions and algebraic dynamics algorithm for nonlinear partial differential evolution equations of dynamical systems

Institute of Scientific and Technical Information of China (English)

2008-01-01