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Relevant bibliographies by topics / Simultaneous algebraic reconstruction technique algorithm

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Academic literature on the topic 'Simultaneous algebraic reconstruction technique algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 25 June 2025

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Journal articles on the topic "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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Dissertations / Theses on the topic "Simultaneous algebraic reconstruction technique algorithm"

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Chlewicki, Wojciech. "3D simultaneous algebraic reconstruction technique for cone-beam projections." Thesis, 2001. http://nemertes.lis.upatras.gr/jspui/handle/10889/3194.

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Lin, Yuan-Yi, and 林源益. "The Study of Using Simultaneous Algebraic Reconstruction Technique on BGA Inspection." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/95945674372612554320.

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碩士<br>國立清華大學<br>動力機械工程學系<br>96<br>On-line optical inspection is become popular, because it can not only improve the product quality but also identify the failure of the process immediately. And hence, not only the production yield can be improved, but also the waste due to failure can be reduced. However, traditional optical inspection machine is limited to detect defects appeared on the surface of the product. In order to detect defects beneath surface, X-Ray image is a powerful tool to apply. Computed tomography can provide the size and shape at any desired section about the defect inside a product while traditional X-Ray image can only provide vague information about the defect. In this dissertation, Simultaneous Algebraic Reconstruction Technique (SART) for computer tomography was adopted to inspect BGA defects. In our simulation study, the effects of various parameters on reconstructed images are also studied. As a result, it was found that the SART with proper parameters used is able to rebuild the 3-D BGA structure properly and the inside flaw can be detected.

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Melvin, Cameron. "Design, development and implementation of a parallel algorithm for computed tomography using algebraic reconstruction technique." Thesis, 2006. http://hdl.handle.net/1993/292.

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This project implements a parallel algorithm for Computed Tomography based on the Algebraic Reconstruction Technique (ART) algorithm. This technique for reconstructing pictures from projections is useful for applications such as Computed Tomography (CT or CAT). The algorithm requires fewer views, and hence less radiation, to produce an image of comparable or better quality. However, the approach is not widely used because of its computationally intensive nature in comparison with rival technologies. A faster ART algorithm could reduce the amount of radiation needed for CT imaging by producing a better image with fewer projections. A reconstruction from projections version of the ART algorithm for two dimensions was implemented in parallel using the Message Passing Interface (MPI) and OpenMP extensions for C. The message passing implementation did not result in faster reconstructions due to prohibitively long and variant communication latency. The shared memory implementation produced positive results, showing a clear computational advantage for multiple processors and measured efficiency ranging from 60-95%. Consistent with the literature, image quality proved to be significantly better compared to the industry standard Filtered Backprojection algorithm especially when reconstructing from fewer projection angles.<br>October 2006

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Xu, Meilian. "Exploiting parallelism of irregular problems and performance evaluation on heterogeneous multi-core architectures." 2012. http://hdl.handle.net/1993/9236.

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In this thesis, we design, develop and implement parallel algorithms for irregular problems on heterogeneous multi-core architectures. Irregular problems exhibit random and unpredictable memory access patterns, poor spatial locality and input dependent control flow. Heterogeneous multi-core processors vary in: clock frequency, power dissipation, programming model (MIMD vs. SIMD), memory design and computing units, scalar versus vector units. The heterogeneity of the processors makes designing efficient parallel algorithms for irregular problems on heterogeneous multicore processors challenging. Techniques of mapping tasks or data on traditional parallel computers can not be used as is on heterogeneous multi-core processors due to the varying hardware. In an attempt to understand the efficiency of futuristic heterogeneous multi-core architectures on applications we study several computation and bandwidth oriented irregular problems on one heterogeneous multi-core architecture, the IBM Cell Broadband Engine (Cell BE). The Cell BE consists of a general processor and eight specialized processors and addresses vector/data-level parallelism and instruction-level parallelism simultaneously. Through these studies on the Cell BE, we provide some discussions and insight on the performance of the applications on heterogeneous multi-core architectures. Verifying these experimental results require some performance modeling. Due to the diversity of heterogeneous multi-core architectures, theoretical performance models used for homogeneous multi-core architectures do not provide accurate results. Therefore, in this thesis we propose an analytical performance prediction model that considers the multitude architectural features of heterogeneous multi-cores (such as DMA transfers, number of instructions and operations, the processor frequency and DMA bandwidth). We show that the execution time from our prediction model is comparable to the execution time of the experimental results for a complex medical imaging application.

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Book chapters on the topic "Simultaneous algebraic reconstruction technique algorithm"

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Kiencke, Sylvia, Yulia M. Levakhina, and Thorsten M. Buzug. "Greedy Projection Access Order for SART Simultaneous Algebraic Reconstruction Technique." In Bildverarbeitung für die Medizin 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36480-8_18.

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Qu, Zhong. "Algebraic Reconstruction Technique in Image Reconstruction Based on Data Mining." In Data Warehousing and Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch219.

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Image reconstruction is one of the key technologies in industrial computed tomography. In this paper, an efficient iterative image reconstruction algorithm in industrial computed tomography with the narrow fan-beam projection based on data mining was discussed in detail. In image reconstruction, algebraic technique has un-replaceable advantage when data is incomplete or noise is high. However algebraic method has been highly limited in applications for its low reconstruction speed. In order to resolve this problem, the algebraic reconstruction technique (ART) as a new iterative method, is introduced to accelerate the iteration process and increase the reconstruction speed. Experiment results clearly demonstrate that the algorithm reconstruction technique can effectively improve the quality of images reconstruction in dealing with incomplete projection or noisy projection data.

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Conference papers on the topic "Simultaneous algebraic reconstruction technique algorithm"

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Wang, Jiong, and Yibin Zheng. "Frequency domain simultaneous algebraic reconstruction techniques: algorithm and convergence." In Electronic Imaging 2005, edited by Charles A. Bouman and Eric L. Miller. SPIE, 2005. http://dx.doi.org/10.1117/12.597352.

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Donato, Sandro, Luca Brombal, Fulvia Arfelli, et al. "Optimization of a customized Simultaneous Algebraic Reconstruction Technique algorithm for breast CT." In 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2019. http://dx.doi.org/10.1109/nss/mic42101.2019.9060011.

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Basari and Ria Aprilliyani. "The Effect of Data Acquisition Configuration on Simultaneous Algebraic Reconstruction Technique Algorithm for Microwave Imaging System." In 2018 Asia-Pacific Microwave Conference (APMC). IEEE, 2018. http://dx.doi.org/10.23919/apmc.2018.8617224.

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Han, Defu, Fengjing Xu, Yanhua Peng, et al. "A non-iterative reconstruction technique for wave velocity field of damaged structures based on acoustic emission finite response." In 6th International Conference on Structural Health Monitoring and Integrity Management, 8 - 10 November 2024, Zhengzhou. NDT.net, 2025. https://doi.org/10.58286/30987.

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Aiming at the issue of low iterative efficiency and accuracy of reconstruction methods in wave velocity field such as algebraic reconstruction technique (ART) and simultaneous iterative reconstruction technique(SIRT) in current tomography technology, this paper takes the damaged elastic plate as a research object and utilizes the acoustic emission (AE) technology with high frequency and high sensitivity to propose a discrete wave velocity field reconstruction technology based on multi-point lead break excitation and finite point AE response. This technique overcomes the problem of sparse discrete grid and low-quality wave velocity field reconstruction in the test area which is affected by the limited travel line number between excitation and response. First, a relationship model between material wave velocity, Young’s modulus and damage factor is established by applying theories of elasticity and damage mechanics. Second, based on the discretized elastic plate grid domain and the time-difference vectors between the discrete travel paths, a linear model for reconstructing the wave velocity field in the discretized grid domain is established, incorporating the wave slowness in the grid domain, the discrete unit length of the travel route, and the time differences. On this basis, based on the quantity of the discretized grid domain, a method is proposed to determine the number and position of multiple break lead excitation points on the elastic plate boundary based on finite AE sensor response points, which can make the augmented matrix rank of the reconstructed linear model based on the time difference vector not less than the coefficient matrix rank of its wave slowness. Third, a time parameter extraction method from lead break excitation to AE response is proposed by using the long short-time window time difference algorithm. In addition, the reconstruction analysis of wave velocity field by non-iterative algorithm of the plate structure can be realized by using the least squares algorithm. Finally, the effectiveness of wave velocity field reconstruction technology and damage location identification is verified by taking the elastic structure plate with hole defects as an experimental example, thus providing a convenient and accurate non-destructive testing method for overall health assessment of critical structural components in equipment

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Kong, Huihua, and Jinxiao Pan. "An Improved Ordered-Subset Simultaneous Algebraic Reconstruction Technique." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5302899.

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Ming Jiang and Ge Wang. "Convergence of the simultaneous algebraic reconstruction technique (SART)." In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.986951.

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Li, Chunfang. "Modified simultaneous algebraic reconstruction technique and its application to image reconstruction." In 27th International congress on High-Speed Photography and Photonics, edited by Xun Hou, Wei Zhao, and Baoli Yao. SPIE, 2007. http://dx.doi.org/10.1117/12.725324.

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Hu, Feng, BaoSheng Li, and Baokun Han. "Optimized simultaneous algebraic reconstruction technique in image reconstruction based on optical tomography." In Second Target Recognition and Artificial Intelligence Summit Forum, edited by Tianran Wang, Tianyou Chai, Huitao Fan, and Qifeng Yu. SPIE, 2020. http://dx.doi.org/10.1117/12.2552220.

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Zhang, Yiheng, Heang-Ping Chan, Berkman Sahiner, et al. "Tomosynthesis reconstruction using the simultaneous algebraic reconstruction technique (SART) on breast phantom data." In Medical Imaging, edited by Michael J. Flynn and Jiang Hsieh. SPIE, 2006. http://dx.doi.org/10.1117/12.654891.

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Li, Tingting, Mingjian Sun, Yawei Qu, Yang Liu, and Wenxue Zhou. "Simultaneous algebraic reconstruction technique based on total variation for photoacoustic image." In Optics in Health Care and Biomedical Optics X, edited by Qingming Luo, Xingde Li, Ying Gu, and Dan Zhu. SPIE, 2020. http://dx.doi.org/10.1117/12.2575133.

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