Journal articles: 'Simultaneous algebraic reconstruction technique algorithm'

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Batenburg, K. J., J. Sijbers, H. F. Poulsen, and E. Knudsen. "DART: a robust algorithm for fast reconstruction of three-dimensional grain maps." Journal of Applied Crystallography 43, no. 6 (2010): 1464–73. http://dx.doi.org/10.1107/s0021889810034114.

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A novel algorithm is introduced for fast and nondestructive reconstruction of grain maps from X-ray diffraction data. The discrete algebraic reconstruction technique (DART) takes advantage of the intrinsic discrete nature of grain maps, while being based on iterative algebraic methods known from classical tomography. To test the properties of the algorithm, three-dimensional X-ray diffraction microscopy data are simulated and reconstructed with DART as well as by a conventional iterative technique, namely SIRT (simultaneous iterative reconstruction technique). For 100 × 100 pixel reconstructions and moderate noise levels, DART is shown to generate essentially perfect two-dimensional grain maps for as few as three projections per grain with running times on a PC in the range of less than a second. This is seen as opening up the possibility for fast reconstructions in connection within situstudies.

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Pan, Jinxiao, Tie Zhou, Yan Han, and Ming Jiang. "Variable Weighted Ordered Subset Image Reconstruction Algorithm." International Journal of Biomedical Imaging 2006 (2006): 1–7. http://dx.doi.org/10.1155/ijbi/2006/10398.

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We propose two variable weighted iterative reconstruction algorithms (VW-ART and VW-OS-SART) to improve the algebraic reconstruction technique (ART) and simultaneous algebraic reconstruction technique (SART) and establish their convergence. In the two algorithms, the weighting varies with the geometrical direction of the ray. Experimental results with both numerical simulation and real CT data demonstrate that the VW-ART has a significant improvement in the quality of reconstructed images over ART and OS-SART. Moreover, both VW-ART and VW-OS-SART are more promising in convergence speed than the ART and SART, respectively.

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Wei, Lao, Cui Hu, Wang Xuanjun, and Qu Zhongkai. "Application and algorithm research of TDLAS." Journal of Physics: Conference Series 2348, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2348/1/012008.

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Tunable diode laser absorption spectroscopy (TDLAS) is extensively utilized in monitoring of trace gases in the environment. With the relative entropy tomographic reconstruction, simultaneous multiplicative algebraic reconstruction technique algorithm and optimization of existing functions and models, TDLAS has been applied to reconstruction of temperature and humidity field, combustion diagnosis, mass flow monitoring and other domains, this paper will analyze the existing TDLAS application and algorithm research.

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Lee, Donghyeon, Sungho Yun, Jeongtae Soh, Sunho Lim, Hyoyi Kim, and Seungryong Cho. "A generalized simultaneous algebraic reconstruction technique (GSART) for dual-energy X-ray computed tomography." Journal of X-Ray Science and Technology 30, no. 3 (2022): 549–66. http://dx.doi.org/10.3233/xst-211054.

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BACKGROUND: Dual-energy computed tomography (DECT) is a widely used and actively researched imaging modality that can estimate the physical properties of an object more accurately than single-energy CT (SECT). Recently, iterative reconstruction methods called one-step methods have received attention among various approaches since they can resolve the intermingled limitations of the conventional methods. However, the one-step methods typically have expensive computational costs, and their material decomposition performance is largely affected by the accuracy in the spectral coefficients estimation. OBJECTIVE: In this study, we aim to develop an efficient one-step algorithm that can effectively decompose into the basis material maps and is less sensitive to the accuracy of the spectral coefficients. METHODS: By use of a new loss function that employs the non-linear forward model and the weighted squared errors, we propose a one-step reconstruction algorithm named generalized simultaneous algebraic reconstruction technique (GSART). The proposed algorithm was compared with the image-domain material decomposition and other existing one-step reconstruction algorithm. RESULTS: In both simulation and experimental studies, we demonstrated that the proposed algorithm effectively reduced the beam-hardening artifacts thereby increasing the accuracy in the material decomposition. CONCLUSIONS: The proposed one-step reconstruction for material decomposition in dual-energy CT outperformed the image-domain approach and the existing one-step algorithm. We believe that the proposed method is a practically very useful addition to the material-selective image reconstruction field.

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Yu, Hengyong, Changguo Ji, and Ge Wang. "SART-Type Image Reconstruction from Overlapped Projections." International Journal of Biomedical Imaging 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/549537.

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To maximize the time-integrated X-ray flux from multiple X-ray sources and shorten the data acquisition process, a promising way is to allow overlapped projections from multiple sources being simultaneously on without involving the source multiplexing technology. The most challenging task in this configuration is to perform image reconstruction effectively and efficiently from overlapped projections. Inspired by the single-source simultaneous algebraic reconstruction technique (SART), we hereby develop a multisource SART-type reconstruction algorithm regularized by a sparsity-oriented constraint in the soft-threshold filtering framework to reconstruct images from overlapped projections. Our numerical simulation results verify the correctness of the proposed algorithm and demonstrate the advantage of image reconstruction from overlapped projections.

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Arfan, Eko Fahrudin, Endarko, Ain Khusnul, and Rubiyanto Agus. "Enhanced image reconstruction of electrical impedance tomography using simultaneous algebraic reconstruction technique and K-means clustering." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 3987–97. https://doi.org/10.11591/ijece.v13i4.pp3987-3997.

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Electrical impedance tomography (EIT), as a non-ionizing tomography method, has been widely used in various fields of application, such as engineering and medical fields. This study applies an iterative process to reconstruct EIT images using the simultaneous algebraic reconstruction technique (SART) algorithm combined with K-means clustering. The reconstruction started with defining the finite element method (FEM) model and filtering the measurement data with a Butterworth low-pass filter. The next step is solving the inverse problem in the EIT case with the SART algorithm. The results of the SART algorithm approach were classified using the K-means clustering and thresholding. The reconstruction results were evaluated with the peak signal noise ratio (PSNR), structural similarity indices (SSIM), and normalized root mean square error (NRMSE). They were compared with the one-step gauss-newton (GN) and total variation regularization based on iteratively reweighted least-squares (TV-IRLS) methods. The evaluation shows that the average PSNR and SSIM of the proposed reconstruction method are the highest of the other methods, each being 24.24 and 0.94; meanwhile, the average NRMSE value is the lowest, which is 0.04. The performance evaluation also shows that the proposed method is faster than the other methods.

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Fahrudin, Arfan Eko, Endarko Endarko, Khusnul Ain, and Agus Rubiyanto. "Enhanced image reconstruction of electrical impedance tomography using simultaneous algebraic reconstruction technique and K-means clustering." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 3987. http://dx.doi.org/10.11591/ijece.v13i4.pp3987-3997.

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<span lang="EN-US">Electrical impedance tomography (EIT), as a non-ionizing tomography method, has been widely used in various fields of application, such as engineering and medical fields. This study applies an iterative process to reconstruct EIT images using the simultaneous algebraic reconstruction technique (SART) algorithm combined with K-means clustering. The reconstruction started with defining the finite element method (FEM) model and filtering the measurement data with a Butterworth low-pass filter. The next step is solving the inverse problem in the EIT case with the SART algorithm. The results of the SART algorithm approach were classified using the K-means clustering and thresholding. The reconstruction results were evaluated with the peak signal noise ratio (PSNR), structural similarity indices (SSIM), and normalized root mean square error (NRMSE). They were compared with the one-step gauss-newton (GN) and total variation regularization based on iteratively reweighted least-squares (TV-IRLS) methods. The evaluation shows that the average PSNR and SSIM of the proposed reconstruction method are the highest of the other methods, each being 24.24 and 0.94; meanwhile, the average NRMSE value is the lowest, which is 0.04. The performance evaluation also shows that the proposed method is faster than the other methods.</span>

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Liu, Baodong, and Li Zeng. "Parallel SART algorithm of linear scan cone-beam CT for fixed pipeline." Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics 17, no. 3 (2009): 221–32. http://dx.doi.org/10.3233/xst-2009-022400224.

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Linear scan cone-beam Computed Tomography (CT) is useful to fixed pipeline inspection. We extend Simultaneous Algebraic Reconstruction Technique (SART) to linear scan cone-beam CT and focus on reducing its reconstruction time through cluster computing. In order to reduce communication overhead, we investigate a trapeziform image space decomposition scheme and a subsets-reduce communication technique. The performance of proposed parallel algorithm is analyzed theoretically and verified through experiment. The results show that the proposed parallel algorithm can generate approving CT images and its performance is mainly influenced by load imbalance and network bandwidth.

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Yan, Bin, Zhao Jin, Hanming Zhang, Lei Li, and Ailong Cai. "NUFFT-Based Iterative Image Reconstruction via Alternating Direction Total Variation Minimization for Sparse-View CT." Computational and Mathematical Methods in Medicine 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/691021.

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

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Abou Al-Ola, Omar M. Abou, Ryosuke Kasai, Yusaku Yamaguchi, Takeshi Kojima, and Tetsuya Yoshinaga. "Image Reconstruction Algorithm Using Weighted Mean of Ordered-Subsets EM and MART for Computed Tomography." Mathematics 10, no. 22 (2022): 4277. http://dx.doi.org/10.3390/math10224277.

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Iterative image reconstruction algorithms have considerable advantages over transform methods for computed tomography, but they each have their own drawbacks. In particular, the maximum-likelihood expectation-maximization (MLEM) algorithm reconstructs high-quality images even with noisy projection data, but it is slow. On the other hand, the simultaneous multiplicative algebraic reconstruction technique (SMART) converges faster at early iterations but is susceptible to noise. Here, we construct a novel algorithm that has the advantages of these different iterative schemes by combining ordered-subsets EM (OS-EM) and MART (OS-MART) with weighted geometric or hybrid means. It is theoretically shown that the objective function decreases with every iteration and the amount of decrease is greater than the mean between the decreases for OS-EM and OS-MART. We conducted image reconstruction experiments on simulated phantoms and deduced that our algorithm outperforms OS-EM and OS-MART alone. Our algorithm would be effective in practice since it incorporates OS-EM, which is currently the most popular technique of iterative image reconstruction from noisy measured projections.

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Kim, H., M. Lee, H. Choi, C. Min, and H. Choi. "Tomographic image reconstruction techniques for accurate spent fuel assembly verification." Journal of Instrumentation 18, no. 01 (2023): C01032. http://dx.doi.org/10.1088/1748-0221/18/01/c01032.

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Abstract Non-proliferation and the security of nuclear materials are essential. The international atomic energy agency (IAEA) considers a tomographic image acquisition technique of spent fuel assemblies a promising technique to accurately verify the rod-by-rod spent fuel conditions stored in a water pool. Researchers at Yonsei University in Korea developed the bismuth germanate (BGO) scintillator-based Yonsei Single-photon Emission Computed Tomography (YSECT). Previous research validated the YSECT system experimentally to quickly evaluate the radioactivity distribution of test fuel rods in the Korea Institute of Nuclear Nonproliferation and Control (KINAC). Quick verification of the fuel assembly requires the development of a high-quality image reconstruction algorithm that enables image acquisition within a short time. This study examined various tomographic image reconstruction techniques to identify patterns of missing fuel rods accurately. Rotational projection image data sets were obtained for 15 patterns of test fuel rods for 900 seconds using the single-photon emission computed tomography (SPECT) system installed at KINAC. The projection images were acquired every 5° while four 64-channel detectors rotated 90°. The acquired images were reconstructed using the following methods: filtered back-projection, simultaneous iterative reconstruction technique, order-subset simultaneous algebraic reconstruction technique, maximum likelihood expectation maximization (MLEM), and Fast-Iterative Shrinkage-Thresholding algorithm (FISTA). Among the reconstruction algorithms used in this study, the image quality of MLEM showed the best performance, and the image contrast of FISTA showed the highest result. Therefore, the signal-to-noise ratio of the tomographic image was improved using the image reconstruction technique optimized for the YSECT system to verify the patterns of fuel rods. Hence, even for the low-quality measured data with the short-time scan of the SPECT system, this advanced technique is expected to show better discriminability of the patterns of fuel rods in the assembly.

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Gomi, Tsutomu, and Yukio Koibuchi. "Use of a Total Variation Minimization Iterative Reconstruction Algorithm to Evaluate Reduced Projections during Digital Breast Tomosynthesis." BioMed Research International 2018 (June 19, 2018): 1–14. http://dx.doi.org/10.1155/2018/5239082.

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Purpose. We evaluated the efficacies of the adaptive steepest descent projection onto convex sets (ASD-POCS), simultaneous algebraic reconstruction technique (SART), filtered back projection (FBP), and maximum likelihood expectation maximization (MLEM) total variation minimization iterative algorithms for reducing exposure doses during digital breast tomosynthesis for reduced projections. Methods. Reconstructions were evaluated using normal (15 projections) and half (i.e., thinned-out normal) projections (seven projections). The algorithms were assessed by determining the full width at half-maximum (FWHM), and the BR3D Phantom was used to evaluate the contrast-to-noise ratio (CNR) for the in-focus plane. A mean similarity measure of structural similarity (MSSIM) was also used to identify the preservation of contrast in clinical cases. Results. Spatial resolution tended to deteriorate in ASD-POCS algorithm reconstructions involving a reduced number of projections. However, the microcalcification size did not affect the rate of FWHM change. The ASD-POCS algorithm yielded a high CNR independently of the simulated mass lesion size and projection number. The ASD-POCS algorithm yielded a high MSSIM in reconstructions from reduced numbers of projections. Conclusions. The ASD-POCS algorithm can preserve contrast despite a reduced number of projections and could therefore be used to reduce radiation doses.

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Hu, Zhanli, Zixiang Chen, Chao Zhou, et al. "Evaluation of reconstruction algorithms for a stationary digital breast tomosynthesis system using a carbon nanotube X-ray source array." Journal of X-Ray Science and Technology 28, no. 6 (2020): 1157–69. http://dx.doi.org/10.3233/xst-200668.

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Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the X-ray source array to stay stationary during the DBT scanning process. In this work, we evaluate four widely used and investigated DBT image reconstruction algorithms, including the commercial Feldkamp-Davis-Kress algorithm (FBP), the simultaneous iterative reconstruction technique (SIRT), the simultaneous algebraic reconstruction technique (SART) and the total variation regularized SART (SART-TV) for an s-DBT imaging system that we set up in our own laboratory for studies using a semi-elliptical digital phantom and a rubber breast phantom to determine the most superior algorithm for s-DBT image reconstruction among the four algorithms. Several quantitative indexes for image quality assessment, including the peak signal-noise ratio (PSNR), the root mean square error (RMSE) and the structural similarity (SSIM), are used to determine the best algorithm for the imaging system that we set up. Image resolutions are measured via the calculation of the contrast-to-noise ratio (CNR) and artefact spread function (ASF). The experimental results show that the SART-TV algorithm gives reconstructed images with the highest PSNR and SSIM values and the lowest RMSE values in terms of image accuracy and similarity, along with the highest CNR values calculated for the selected features and the best ASF curves in terms of image resolution in the horizontal and vertical directions. Thus, the SART-TV algorithm is proven to be the best algorithm for use in s-DBT image reconstruction for the specific imaging task in our study.

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Boudjelal, Abdelwahhab, Zoubeida Messali, Abderrahim Elmoataz, and Bilal Attallah. "Improved Simultaneous Algebraic Reconstruction Technique Algorithm for Positron-Emission Tomography Image Reconstruction via Minimizing the Fast Total Variation." Journal of Medical Imaging and Radiation Sciences 48, no. 4 (2017): 385–93. http://dx.doi.org/10.1016/j.jmir.2017.09.005.

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Asadchikov, Victor, Alexey Buzmakov, Felix Chukhovskii, et al. "X-ray topo-tomography studies of linear dislocations in silicon single crystals." Journal of Applied Crystallography 51, no. 6 (2018): 1616–22. http://dx.doi.org/10.1107/s160057671801419x.

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This article describes complete characterization of the polygonal dislocation half-loops (PDHLs) introduced by scratching and subsequent bending of an Si(111) crystal. The study is based on the X-ray topo-tomography technique using both a conventional laboratory setup and the high-resolution X-ray image-detecting systems at the synchrotron facilities at KIT (Germany) and ESRF (France). Numerical analysis of PDHL images is performed using the Takagi–Taupin equations and the simultaneous algebraic reconstruction technique (SART) tomographic algorithm.

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Bharkhada, Deepak, Hengyong Yu, Hong Liu, Robert Plemmons, and Ge Wang. "Line-Source Based X-Ray Tomography." International Journal of Biomedical Imaging 2009 (2009): 1–8. http://dx.doi.org/10.1155/2009/534516.

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Current computed tomography (CT) scanners, including micro-CT scanners, utilize a point x-ray source. As we target higher and higher spatial resolutions, the reduced x-ray focal spot size limits the temporal and contrast resolutions achievable. To overcome this limitation, in this paper we propose to use a line-shaped x-ray source so that many more photons can be generated, given a data acquisition interval. In reference to the simultaneous algebraic reconstruction technique (SART) algorithm for image reconstruction from projection data generated by an x-ray point source, here we develop a generalized SART algorithm for image reconstruction from projection data generated by an x-ray line source. Our numerical simulation results demonstrate the feasibility of our novel line-source based x-ray CT approach and the proposed generalized SART algorithm.

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Yu, Hengyong, and Ge Wang. "SART-Type Image Reconstruction from a Limited Number of Projections with the Sparsity Constraint." International Journal of Biomedical Imaging 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/934847.

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Based on the recent mathematical findings on solving the linear inverse problems with sparsity constraints by Daubechiesx et al., here we adapt a simultaneous algebraic reconstruction technique (SART) for image reconstruction from a limited number of projections subject to a sparsity constraint in terms of an invertible compression transform. The algorithm is implemented with an exemplary Haar wavelet transform and tested with a modified Shepp-Logan phantom. Our preliminary results demonstrate that the sparsity constraint helps effectively improve the quality of reconstructed images and reduce the number of necessary projections.

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Lu, Lian, Guowei Tong, Ge Guo, and Shi Liu. "Split Bregman iteration based reconstruction algorithm for electrical capacitance tomography." Transactions of the Institute of Measurement and Control 41, no. 9 (2018): 2389–99. http://dx.doi.org/10.1177/0142331218799841.

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The electrical capacitance tomography (ECT) technique uses the measured capacitance data to reconstruct the permittivity distribution in a specific measurement area, in which the performances of reconstruction algorithms play a crucial role in the reliability of measurement results. According to the Tikhonov regularization technique, a new cost function with the total least squares technique and the ℓ1-norm based regularizer is presented, in which measurement noises, model deviations and the influence of the outliers in the measurement data are simultaneously considered. The split Bregman technique and the fast-iterative shrinkage-thresholding method are combined into a new iterative scheme to solve the proposed cost function efficiently. Numerical experiment results show that the proposed algorithm achieves the boost in the precision of reconstruction, and under the noise-free condition the image errors for the imaging targets simulated in this paper, that is, 8.4%, 12.4%, 13.5% and 6.4%, are smaller than the linear backprojection (LBP) algorithm, the Tikhonov regularization (TR) algorithm, the truncated singular value decomposition (TSVD) algorithm, the Landweber algorithm and the algebraic reconstruction technique (ART).

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Xu, Meilian, and Parimala Thulasiraman. "Mapping Iterative Medical Imaging Algorithm on Cell Accelerator." International Journal of Biomedical Imaging 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/843924.

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Algebraic reconstruction techniques require about half the number of projections as that of Fourier backprojection methods, which makes these methods safer in terms of required radiation dose. Algebraic reconstruction technique (ART) and its variant OS-SART (ordered subset simultaneous ART) are techniques that provide faster convergence with comparatively good image quality. However, the prohibitively long processing time of these techniques prevents their adoption in commercial CT machines. Parallel computing is one solution to this problem. With the advent of heterogeneous multicore architectures that exploit data parallel applications, medical imaging algorithms such as OS-SART can be studied to produce increased performance. In this paper, we map OS-SART on cell broadband engine (Cell BE). We effectively use the architectural features of Cell BE to provide an efficient mapping. The Cell BE consists of one powerPC processor element (PPE) and eight SIMD coprocessors known as synergetic processor elements (SPEs). The limited memory storage on each of the SPEs makes the mapping challenging. Therefore, we present optimization techniques to efficiently map the algorithm on the Cell BE for improved performance over CPU version. We compare the performance of our proposed algorithm on Cell BE to that of Sun Fire×4600, a shared memory machine. The Cell BE is five times faster than AMD Opteron dual-core processor. The speedup of the algorithm on Cell BE increases with the increase in the number of SPEs. We also experiment with various parameters, such as number of subsets, number of processing elements, and number of DMA transfers between main memory and local memory, that impact the performance of the algorithm.

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Zhao, Rong, Cheng Du, Jianyong Zhang, Ruixue Cheng, Zhongqiang Yu, and Bin Zhou. "Reconstruction Algorithm Optimization Based on Multi-Iteration Adaptive Regularity for Laser Absorption Spectroscopy Tomography." Applied Sciences 13, no. 21 (2023): 12083. http://dx.doi.org/10.3390/app132112083.

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Laser absorption spectroscopy tomography is an effective combustion diagnostic method for obtaining simultaneous two-dimensional distribution measurements of temperature and gas molar concentrations. For the reconstruction process of complex combustion flames, a new algorithm named ‘multi-iterative adaptive optimization regularization’ (MIARO) is proposed. This algorithm is a further development of another algorithm known as the ‘modified adaptive algebraic reconstruction technique’ (MAART) with the improvement of the initial value and adaptive regularization parameter selections. In MIARO, the problem of the MAART’s initial value sensitivity is compensated for, and in addition, reconstruction parameters are also introduced into the regularization so that both the quality of reconstruction and the convergence of regularization are guaranteed. In butane burner experiments, an average relative error of 1.82% was achieved with MIARO, compared to 2.44% with MAART, which is a significant reduction of 25.1%. The simulation and experimental results clearly demonstrate that the MIARO algorithm can be used to reconstruct dynamic combustion fields and eliminate boundary artifacts with improved measurement accuracy and robustness.

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Zhao, Yuqing, Mengyu Sun, Dongjiang Ji, et al. "An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phase-contrast computed tomography." Journal of Synchrotron Radiation 25, no. 5 (2018): 1450–59. http://dx.doi.org/10.1107/s1600577518009219.

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In-line X-ray phase-contrast computed tomography (IL-PCCT) can reveal fine inner structures for low-Z materials (e.g. biological soft tissues), and shows high potential to become clinically applicable. Typically, IL-PCCT utilizes filtered back-projection (FBP) as the standard reconstruction algorithm. However, the FBP algorithm requires a large amount of projection data, and subsequently a large radiation dose is needed to reconstruct a high-quality image, which hampers its clinical application in IL-PCCT. In this study, an iterative reconstruction algorithm for IL-PCCT was proposed by combining the simultaneous algebraic reconstruction technique (SART) with eight-neighbour forward and backward (FAB8) diffusion filtering, and the reconstruction was performed using the Shepp–Logan phantom simulation and a real synchrotron IL-PCCT experiment. The results showed that the proposed algorithm was able to produce high-quality computed tomography images from few-view projections while improving the convergence rate of the computed tomography reconstruction, indicating that the proposed algorithm is an effective method of dose reduction for IL-PCCT.

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Hobiger, Thomas, Tetsuro Kondo, and Yasuhiro Koyama. "Constrained simultaneous algebraic reconstruction technique (C-SART) —a new and simple algorithm applied to ionospheric tomography." Earth, Planets and Space 60, no. 7 (2008): 727–35. http://dx.doi.org/10.1186/bf03352821.

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Zhang, Shunli, Guohua Geng, Guohua Cao, Yuhe Zhang, Baodong Liu, and Xu Dong. "Fast Projection Algorithm for LIM-Based Simultaneous Algebraic Reconstruction Technique and Its Parallel Implementation on GPU." IEEE Access 6 (2018): 23007–18. http://dx.doi.org/10.1109/access.2018.2829861.

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Murase, Kenya. "New image-restoration method using a simultaneous algebraic reconstruction technique: comparison with the Richardson–Lucy algorithm." Radiological Physics and Technology 13, no. 4 (2020): 365–77. http://dx.doi.org/10.1007/s12194-020-00595-y.

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Li, Xiang, Ming Jiang, and Ge Wang. "A numerical simulator in VC++ on PC for iterative image reconstruction." Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics 11, no. 2 (2003): 61–70. http://dx.doi.org/10.3233/xst-2003-00078.

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With the development of computing technology, there is an increasing interest in iterative CT image reconstruction. To study iterative reconstruction algorithms, we have developed a software simulator IterCT in VC++ on PC and made it publicly available on the Internet. In the latest version of the simulator, we have implemented four representative iterative image reconstruction algorithms, which are expectation maximization (EM) method and its ordered-subset version (OSEM), the simultaneous algebraic reconstruction technique (SART) and its ordered-subset version (OSSART). The filtered backprojection method is included as the benchmark.

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Jeon, Min-Gyu, Deog-Hee Doh, Yoshihiro Deguchi, Takahiro Kamimoto, and Minchao Cui. "Evaluation of 3D measurement using CT-TDLAS." Modern Physics Letters B 33, no. 14n15 (2019): 1940018. http://dx.doi.org/10.1142/s0217984919400189.

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In order to satisfy the requirements of high quality and optimal material manufacturing process, it is important to control the environment of the manufacturing process. Depending on these processes, it is possible to improve the quality of the product by adjusting various gases. With the advent of the tunable laser absorption spectroscopy (TDLAS) technique, the temperature and concentration of the gases can be measured simultaneously. Among them, computed tomography-tunable diode laser absorption spectroscopy (CT-TDLAS) is the most important technique for measuring the distributions of temperature and concentration across the two-dimensional planes. This study suggests a three-dimensional measurement to consider the irregular flow of supplying gases. The simultaneous multiplicative algebraic reconstruction technique (SMART) algorithm was used among the CT algorithms. Phantom datasets have been generated by using Gaussian distribution method. It can show expected temperature and concentration distributions. The (HITRAN) database in which the thermo-dynamical properties and the light spectra of H2O are listed were used for the numerical test. The relative average temperature error ratio in the results obtained by the SMART algorithm was about 3.2% for temperature. The maximum error was 86.8 K.

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Nguyen Thanh, Chau, Thao Huong Giang Le, Ngoc Nhat Anh Nguyen, Van Chuan Nguyen, and Tien Thanh Pham. "Study of image reconstruction method for 2D gamma scan technique by anti-aliasing line "Xiaolin Wu" algorithm combined with simultaneous algebraic reconstruction algorithms and testing on MCNP simulation data." Nuclear Science and Technology 12, no. 4 (2024): 38–45. http://dx.doi.org/10.53747/nst.v12i4.397.

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Gamma scanning technique is known to be an effective method for survey the condition of distillation columns in petrochemical refineries and has been widely applied. The result of the gamma scanning is a 1-dimensional graph showing the transmittance counts according to the height of the column. To illuminate the important phenomena occurring on the tray such as foaming; flooding; weeping due to valve failure on the tray needs experienced people and the interpretation results are still quite qualitative. The method of 2-D gamma scanning and reconstruction (2D Tomography), which has just appeared in the world in recent years, is considered as a potential method to help detect the above phenomena. This report presents the two-dimensional gamma scanning configuration, mathematical calculation and noise processing methods by using the combination of Xiaolin Wu’s line drawing algorithm with simultaneous algebraic reconstruction algorithm (SART) based on data from Monte Carlo simulation.

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Jeon, Min-Gyu, Deog-Hee Doh, and Yoshihiro Deguchi. "Measurement Enhancement on Two-Dimensional Temperature Distribution of Methane-Air Premixed Flame Using SMART Algorithm in CT-TDLAS." Applied Sciences 9, no. 22 (2019): 4955. http://dx.doi.org/10.3390/app9224955.

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In this study, the temperature distribution of the Methane-Air premixed flame was measured. In order to enhance the measurement accuracy of the CT-TDLAS (Computed tomography-tunable diode laser absorption spectroscopy), the SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm has been adopted. Further, the SLOS (summation of line of sight) and the CSLOS (corrective summation of line of sight) methods have been adopted to increase measurement accuracies. It has been verified that the relative error for the temperatures measured by the thermocouples and calculated by the CT-TDLAS was about 10%.

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Wang, Wu, Artur Svidrytski, Di Wang, et al. "Quantifying Morphology and Diffusion Properties of Mesoporous Carbon From High-Fidelity 3D Reconstructions." Microscopy and Microanalysis 25, no. 4 (2019): 891–902. http://dx.doi.org/10.1017/s1431927619014600.

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AbstractA reliable quantitative analysis in electron tomography, which depends on the segmentation of the three-dimensional reconstruction, is challenging because of constraints during tilt-series acquisition (missing wedge) and reconstruction artifacts introduced by reconstruction algorithms such as the Simultaneous Iterative Reconstruction Technique (SIRT) and Discrete Algebraic Reconstruction Technique (DART). We have carefully evaluated the fidelity of segmented reconstructions analyzing a disordered mesoporous carbon used as support in catalysis. Using experimental scanning transmission electron microscopy (STEM) tomography data as well as realistic phantoms, we have quantitatively analyzed the effect on the morphological description as well as on diffusion properties (based on a random-walk particle-tracking simulation) to understand the role of porosity in catalysis. The morphological description of the pore structure can be obtained reliably both using SIRT and DART reconstructions even in the presence of a limited missing wedge. However, the measured pore volume is sensitive to the threshold settings, which are difficult to define globally for SIRT reconstructions. This leads to noticeable variations of the diffusion coefficients in the case of SIRT reconstructions, whereas DART reconstructions resulted in more reliable data. In addition, the anisotropy of the determined diffusion properties was evaluated, which was significant in the presence of a limited missing wedge for SIRT and strongly reduced for DART.

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Wang, Hao Peng, Xiao Jing Li, Tong Pan, and Kai Zhao. "A Sequential Structure from Motion Algorithm for Static Scenes Based on VR-Simulation." Advanced Materials Research 712-715 (June 2013): 2389–92. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2389.

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The paper presented a conventional sequential Structure from Motion method, introduced the algebraic concepts and the core techniques used effectively. It is especially essential to comprehend the static scene based sequential of Structure from Motion is extended to a simultaneous segmentation and reconstruction scheme for dynamic scenes. The first outcome is the multiple view geometry, the second is the feature tracking and geometry initializing, and the last is self-calibration.

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Xiao, Dayu, Xiaotong Zhang, Jianhua Li, Nan Bao, and Yan Kang. "Low-Dose X-ray CT Reconstruction Algorithm Using Shearlet Sparse Regularization." Journal of Medical Imaging and Health Informatics 10, no. 3 (2020): 620–27. http://dx.doi.org/10.1166/jmihi.2020.2908.

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Computed tomography (CT) scans produce ionizing radiation in the body, and high-dose CT scans may increase the risk of cancer. Therefore, reducing the CT radiation dose is particularly important in clinical diagnosis, which is achieved mainly by reducing projection views and tube current. However, the projection data are incomplete in the case of sparse views, which may cause stripe artifacts in the image reconstructed by the filtered back projection (FBP) algorithm, thereby losing the details of the image. Low current intensity also increases the noise of the projection data, degrading the quality of the reconstructed image. This study aimed to use the alternating direction method of multipliers (ADMM) to address the shearlet-based sparse regularization problem, which is subsequently referred to as ADMM-shearlet method. The low-dose projection data were simulated by adding Gaussian noise with zero mean to high-dose projection data. Then FBP, simultaneous algebraic reconstruction technique, total variation, and ADMM-shearlet methods were used to reconstruct images. Normalized mean square error, peak signal-to-noise ratio, and universal quality index were used to evaluate the performance of different reconstruction algorithms. Compared with the traditional reconstruction algorithms, the ADMM-shearlet algorithm performed well in suppressing the noise due to the low dose while maintaining the image details.

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Wei, Zhiqing, Yanping Bai, Rong Cheng, et al. "Improved sparse domain super-resolution reconstruction algorithm based on CMUT." PLOS ONE 18, no. 8 (2023): e0290989. http://dx.doi.org/10.1371/journal.pone.0290989.

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A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. To solve the problem, this paper proposes an improved medical image super-resolution (MeSR) method based on the sparse domain. First, we use the simultaneous algebraic reconstruction technique (SART) with high imaging accuracy to reconstruct the image into a training image in a sparse domain model. Secondly, we denoise and enhance the contrast of the SART images to obtain improved detail images before training the dictionary. Then, we use the original detail image as the guide image to further process the improved detail image. Therefore, a high-precision dictionary was obtained during the testing phase and applied to filtered back projection SR reconstruction. We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. The results showed significant improvements in peak signal-to-noise ratio, entropy, and average gradient compared to previously reported algorithms. The experimental results demonstrated that the proposed MeSR method can use noisy reconstructed images as input for the training phase of the SR algorithm and produce excellent visual effects.

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Li, Jian, Jin Li, Chenli Guo, et al. "Method for Reconstructing Velocity Field Images of the Internal Structures of Bridges Based on Group Sparsity." Electronics 13, no. 22 (2024): 4574. http://dx.doi.org/10.3390/electronics13224574.

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Non-destructive testing (NDT) enables the determination of internal defects and flaws in concrete structures without damaging them, making it a common application in current bridge concrete inspections. However, due to the complexity of the internal structure of this type of concrete, limitations regarding measurement point placement, and the extensive detection area, accurate defect detection cannot be guaranteed. This paper proposes a method that combines the Simultaneous Algebraic Reconstruction Technique with Group Sparsity Regularization (SART-GSR) to achieve tomographic imaging of bridge concrete under sparse measurement conditions. Firstly, a mathematical model is established based on the principles of the tomographic imaging of bridge concrete; secondly, the SART algorithm is used to solve for its velocity values; thirdly, on the basis of the SART results, GSR is applied for optimized solution processing; finally, simulation experiments are conducted to verify the reconstruction effects of the SART-GSR algorithm compared with those of the SART and ART algorithms. The results show that the SART-GSR algorithm reduced the relative error to 1.5% and the root mean square error to 89.76 m/s compared to the SART and ART algorithms. This improvement in accuracy makes it valuable for the tomographic imaging of bridge concrete and provides a reference for defect detection in bridge concrete.

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Gou, Jun-Nian, Pan-Pan Zhai, and Hai-Ying Dong. "Sparse and limited-angle CT image reconstruction using dictionary learning constrained by L1 norm." Insight - Non-Destructive Testing and Condition Monitoring 61, no. 10 (2019): 584–90. http://dx.doi.org/10.1784/insi.2019.61.10.584.

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Reconstructed images from computed tomography (CT) using the algebraic reconstruction technique (ART) and simultaneous ART (SART) algorithms often suffer from obvious artefacts when only sparse and limited-angle projection data are available. Using the ability of dictionary learning (DL) in image feature extraction and sparse signal representation, a new iterative reconstruction algorithm, ART-DL-L1, is proposed to overcome the aforementioned limitations. This new algorithm is based on DL and an L1 norm constraint, combined with ART. An alternate iterative solving strategy based on an approach of 'ART first, then adaptive dictionary learning' is suggested and is explicitly described in a flowchart depicting the ART-DL-L1 algorithm. For both a noisy projection of 360° sparse data and limitedangle data of 120°, simulation reconstruction results from the classic Shepp-Logan image obtained using ART-DL-L1 appear to be better than those obtained using SART and total variation (TV) algorithms and also better than the cutting-edge ART-DL-L2 algorithm. Five evaluation metrics corresponding to the root-mean-square error (RMSE), the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR), the residuals and the structural similarity (SSIM) index are adopted to estimate the reconstruction effect. The results suggest that the five metrics obtained using ART-DL-L1 outperform those obtained using the other three algorithms. The impact of using patches of various sizes played by the DL part in ART-DL-L1 is considered in the simulations and the patch size achieving the best reconstructed image quality is identified in this case as 25 (5 × 5). Overall, the proposed ART-DL-L1 algorithm may reduce artefacts and suppress noise from incomplete noisy projection CT imaging to some degree.

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Jeon, Min-Gyu, Jeong-Woong Hong, Deog-Hee Doh, and Yoshihiro Deguchi. "A study on two-dimensional temperature and concentration distribution of Propane-Air premixed flame using CT-TDLAS." Modern Physics Letters B 34, no. 07n09 (2020): 2040020. http://dx.doi.org/10.1142/s0217984920400205.

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To use supplying gases and energy resources efficiently, accurate measurement of irregular gas is necessary. The TDLAS (Tunable laser absorption spectroscopy) technique can be used to control and monitor the supplying gas conditions and combustion of industrial processes. Recently, CT-TDLAS (Computed tomography-tunable diode laser absorption spectroscopy) has been developed to measure the temperature and concentration field of gases. In this study, the 2-dimensional temperature distribution of the Propane-Air premixed flame in several mixing conditions of fuel has been measured by the constructed CT-TDLAS system. 2-Dimensional temperature distributions are measured by 16 path cells. Further, the third-order polynomial regression analysis was applied to resolve the absorption spectra from the incident and transmitted light for a particular gas. The SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm has been adopted for reconstructing the absorption coefficients on the detecting area. As a result of comparing the temperature for the 2-dimensional detecting area using the thermocouple and CT-TDLAS technique, it has been verified that the relative error for the temperatures measured by the thermocouples and calculated by the CT-TDLAS was up to 8%.

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Zhang, Lingli, and An Luo. "l1/2 regularization for wavelet frames based few-view CT reconstruction." E3S Web of Conferences 269 (2021): 01020. http://dx.doi.org/10.1051/e3sconf/202126901020.

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Reducing the radiation exposure in computed tomography (CT) is always a significant research topic in radiology. Image reconstruction from few-view projection is a reasonable and effective way to decrease the number of rays to lower the radiation exposure. But how to maintain high image reconstruction quality while reducing radiation exposure is a major challenge. To solve this problem, several researchers are absorbed in l0 or l1 regularization based optimization models to deal with it. However, the solution of l1 regularization based optimization model is not sparser than that of l1/2 or l0 regularization, and solving the l0 regularization is more difficult than solving the l1/2 regularization. In this paper, we develop l1/2 regularization for wavelet frames based image reconstruction model to research the few-view problem. First, the existence of the solution of the corresponding model is demonstrated. Second, an alternate direction method (ADM) is utilized to separate the original problem into two subproblems, where the former subproblem about the image is solved using the idea of the proximal mapping, the simultaneous algebraic reconstruction technique (SART) and the projection and contraction (PC) algorithm, and the later subproblem about the wavelet coefficients is solved using the half thresholding (HT) algorithm. Furthermore, the convergence analysis of our method is given by the simulated implementions. Simulated and real experiments confirm the effectiveness of our method.

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Ludwig, Wolfgang, Søeren Schmidt, Erik Mejdal Lauridsen, and Henning Friis Poulsen. "X-ray diffraction contrast tomography: a novel technique for three-dimensional grain mapping of polycrystals. I. Direct beam case." Journal of Applied Crystallography 41, no. 2 (2008): 302–9. http://dx.doi.org/10.1107/s0021889808001684.

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The principles of a novel technique for nondestructive and simultaneous mapping of the three-dimensional grain and the absorption microstructure of a material are explained. The technique is termed X-ray diffraction contrast tomography, underlining its similarity to conventional X-ray absorption contrast tomography with which it shares a common experimental setup. The grains are imaged using the occasionally occurring diffraction contribution to the X-ray attenuation coefficient each time a grain fulfils the diffraction condition. The three-dimensional grain shapes are reconstructed from a limited number of projections using an algebraic reconstruction technique. An algorithm based on scanning orientation space and aiming at determining the corresponding crystallographic grain orientations is proposed. The potential and limitations of a first approach, based on the acquisition of the direct beam projection images only, are discussed in this first part of the paper. An extension is presented in the second part of the paper [Johnson, King, Honnicke, Marrow & Ludwig (2008).J. Appl. Cryst.41, 310–318], addressing the case of combined direct and diffracted beam acquisition.

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Chen, Yun, Yao Lu, Xiangyuan Ma, and Yuesheng Xu. "A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm." Inverse Problems 38, no. 3 (2022): 035005. http://dx.doi.org/10.1088/1361-6420/ac490f.

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Abstract The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Specifically, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization defined on the CAUG for the resulting ill-posed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the superiority of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with the quadratic regularization, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid.

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Gomi, Tsutomu, Hidetake Hara, Yusuke Watanabe, and Shinya Mizukami. "Improved digital chest tomosynthesis image quality by use of a projection-based dual-energy virtual monochromatic convolutional neural network with super resolution." PLOS ONE 15, no. 12 (2020): e0244745. http://dx.doi.org/10.1371/journal.pone.0244745.

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We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm (DE–VM–VDSR) that uses projection data to improve the nodule contrast and reduce ripple artifacts during chest digital tomosynthesis (DT). For estimating the residual errors from high-resolution and multiscale VM images from the projection space, the DE–VM–VDSR algorithm employs a training network (mini-batch stochastic gradient-descent algorithm with momentum) and a hybrid super-resolution (SR) image [simultaneous algebraic reconstruction technique (SART) total-variation (TV) first-iterative shrinkage–thresholding algorithm (FISTA); SART–TV–FISTA] that involves subjective reconstruction with bilateral filtering (BF) [DE–VM–VDSR with BF]. DE-DT imaging was accomplished by pulsed X-ray exposures rapidly switched between low (60 kV, 37 projection) and high (120 kV, 37 projection) tube-potential kVp by employing a 40° swing angle. This was followed by comparison of images obtained employing the conventional polychromatic filtered backprojection (FBP), SART, SART–TV–FISTA, and DE–VM–SART–TV–FISTA algorithms. The improvements in contrast, ripple artifacts, and resolution were compared using the signal-difference-to-noise ratio (SDNR), Gumbel distribution of the largest variations, radial modulation transfer function (radial MTF) for a chest phantom with simulated ground-glass opacity (GGO) nodules, and noise power spectrum (NPS) for uniform water phantom. The novel DE–VM–VDSR with BF improved the overall performance in terms of SDNR (DE–VM–VDSR with BF: 0.1603, without BF: 0.1517; FBP: 0.0521; SART: 0.0645; SART–TV–FISTA: 0.0984; and DE–VM–SART–TV–FISTA: 0.1004), obtained a Gumbel distribution that yielded good images showing the type of simulated GGO nodules used in the chest phantom, and reduced the ripple artifacts. The NPS of DE–VM–VDSR with BF showed the lowest noise characteristics in the high-frequency region (~0.8 cycles/mm). The DE–VM–VDSR without BF yielded an improved resolution relative to that of the conventional reconstruction algorithms for radial MTF analysis (0.2–0.3 cycles/mm). Finally, based on the overall image quality, DE–VM–VDSR with BF improved the contrast and reduced the high-frequency ripple artifacts and noise.

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Zhang, Lingli. "Total variation with modified group sparsity for CT reconstruction under low SNR." Journal of X-Ray Science and Technology 29, no. 4 (2021): 645–62. http://dx.doi.org/10.3233/xst-200833.

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BACKGROUND AND OBJECTIVE: Since the stair artifacts may affect non-destructive testing (NDT) and diagnosis in the later stage, an applicable model is desperately needed, which can deal with the stair artifacts and preserve the edges. However, the classical total variation (TV) algorithm only considers the sparsity of the gradient transformed image. The objective of this study is to introduce and test a new method based on group sparsity to address the low signal-to-noise ratio (SNR) problem. METHODS: This study proposes a weighted total variation with overlapping group sparsity model. This model combines the Gaussian kernel and overlapping group sparsity into TV model denoted as GOGS-TV, which considers the structure sparsity of the image to be reconstructed to deal with the stair artifacts. On one hand, TV is the accepted commercial algorithm, and it can work well in many situations. On the other hand, the Gaussian kernel can associate the points around each pixel. Quantitative assessments are implemented to verify this merit. RESULTS: Numerical simulations are performed to validate the presented method, compared with the classical simultaneous algebraic reconstruction technique (SART) and the state-of-the-art TV algorithm. It confirms the significantly improved SNR of the reconstruction images both in suppressing the noise and preserving the edges using new GOGS-TV model. CONCLUSIONS: The proposed GOGS-TV model demonstrates its advantages to reduce stair artifacts especially in low SNR reconstruction because this new model considers both the sparsity of the gradient image and the structured sparsity. Meanwhile, the Gaussian kernel is utilized as a weighted factor that can be adapted to the global distribution.

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Komolafe, Temitope E., Qiang Du, Yin Zhang, et al. "Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method." Journal of X-Ray Science and Technology 28, no. 6 (2020): 1037–54. http://dx.doi.org/10.3233/xst-190639.

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BACKGROUND: Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. OBJECTIVE: To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. METHODS: The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method. RESULTS: The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem. CONCLUSIONS: This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.

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Wang, Tianyi, Chengxiang Wang, Kequan Zhao, Wei Yu та Min Huang. "Guided image filtering based ℓ0 gradient minimization for limited-angle CT image reconstruction". Journal of Inverse and Ill-posed Problems 29, № 4 (2021): 587–98. http://dx.doi.org/10.1515/jiip-2020-0096.

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Abstract Limited-angle computed tomography (CT) reconstruction problem arises in some practical applications due to restrictions in the scanning environment or CT imaging device. Some artifacts will be presented in image reconstructed by conventional analytical algorithms. Although some regularization strategies have been proposed to suppress the artifacts, such as total variation (TV) minimization, there is still distortion in some edge portions of image. Guided image filtering (GIF) has the advantage of smoothing the image as well as preserving the edge. To further improve the image quality and protect the edge of image, we propose a coupling method, that combines ℓ 0 {\ell_{0}} gradient minimization and GIF. An intermediate result obtained by ℓ 0 {\ell_{0}} gradient minimization is regarded as a guidance image of GIF, then GIF is used to filter the result reconstructed by simultaneous algebraic reconstruction technique (SART) with nonnegative constraint. It should be stressed that the guidance image is dynamically updated as the iteration process, which can transfer the edge to the filtered image. Some simulation and real data experiments are used to evaluate the proposed method. Experimental results show that our method owns some advantages in suppressing the artifacts of limited angle CT and in preserving the edge of image.

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Ming Jiang and Ge Wang. "Convergence of the simultaneous algebraic reconstruction technique (SART)." IEEE Transactions on Image Processing 12, no. 8 (2003): 957–61. http://dx.doi.org/10.1109/tip.2003.815295.

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SHI, Huai-lin, Feng-rong SUN, Wei JIANG, Wei LIU, Tong QIN, and Xin-cai LI. "CUDA based parallel implementation of simultaneous algebraic reconstruction technique." Journal of Computer Applications 31, no. 5 (2011): 1245–48. http://dx.doi.org/10.3724/sp.j.1087.2011.01245.

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Ji, Dongjiang, Gangrong Qu, and Baodong Liu. "Simultaneous algebraic reconstruction technique based on guided image filtering." Optics Express 24, no. 14 (2016): 15897. http://dx.doi.org/10.1364/oe.24.015897.

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Lei Dechuan, 雷德川, 陈浩 Chen Hao, 王远 Wang Yuan, 张成鑫 Zhang Chengxin, 陈云斌 Chen Yunbin, and 胡栋材 Hu Dongcai. "Accelerating simultaneous algebraic reconstruction technique by multi CUDAenabled GPU." High Power Laser and Particle Beams 25, no. 9 (2013): 2418–22. http://dx.doi.org/10.3788/hplpb20132509.2418.

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Zhang, L., W. Lu, F. Yin, and Y. Zhang. "SU-FF-I-37: An Adaptive Simultaneous Algebraic Reconstruction Technique." Medical Physics 34, no. 6Part3 (2007): 2346. http://dx.doi.org/10.1118/1.2760414.

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Wan, Xiaohua, Fa Zhang, Qi Chu, et al. "Three-dimensional reconstruction using an adaptive simultaneous algebraic reconstruction technique in electron tomography." Journal of Structural Biology 175, no. 3 (2011): 277–87. http://dx.doi.org/10.1016/j.jsb.2011.06.002.

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Zhang, Shufang, Fuyao Wang, Cong Zhang, Hui Xie, and Minggang Wan. "Flame slice algebraic reconstruction technique reconstruction algorithm based on radial total variation." Journal of Electronic Imaging 25, no. 5 (2016): 053037. http://dx.doi.org/10.1117/1.jei.25.5.053037.

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Häber, Thomas, Rainer Suntz, and Henning Bockhorn. "Two-Dimensional Tomographic Simultaneous Multispecies Visualization—Part II: Reconstruction Accuracy." Energies 13, no. 9 (2020): 2368. http://dx.doi.org/10.3390/en13092368.

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Recently we demonstrated the simultaneous detection of the chemiluminescence of the radicals OH* (310 nm) and CH* (430 nm), as well as the thermal radiation of soot in laminar and turbulent methane/air diffusion flames. As expected, a strong spatial and temporal coupling of OH* and CH* in laminar and moderate turbulent flames was observed. Taking advantage of this coupling, multispecies tomography enables us to quantify the reconstruction quality completely independent of any phantom studies by simply utilizing the reconstructed distribution of both species. This is especially important in turbulent flames, where it is difficult to separate measurement noise from turbulent fluctuations. It is shown that reconstruction methods based on Tikhonov regularization should be preferred over the widely used algebraic reconstruction technique (ART) and multiplicative algebraic reconstruction techniques (MART), especially for high-speed imaging or generally in the limit of low signal-to-noise ratio.

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Journal articles on the topic 'Simultaneous algebraic reconstruction technique algorithm'

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