The Experts below are selected from a list of 29508 Experts worldwide ranked by ideXlab platform
Soojin Lee - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of subset construction methods in osem algorithms using simulated projection data of compton camera
Nuclear Medicine and Molecular Imaging, 2007Co-Authors: Soomee Kim, Jae Sung Lee, Mino Lee, Juhahn Lee, J H Kim, Chan Hyeong Kim, C S Lee, Dong Soo Lee, Soojin LeeAbstract:Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the Ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and Ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a Predefined Order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. Conclusion: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available. (Nucl Med Mol Imaging 2007;41(3):234-240)
Roberto Palmieri - One of the best experts on this subject based on the ideXlab platform.
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processing transactions in a Predefined Order
ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2019Co-Authors: Mohamed M Saad, Masoomeh Javidi Kishi, Shihao Jing, Sandeep Hans, Roberto PalmieriAbstract:In this paper we provide a high performance solution to the problem of committing transactions while enforcing a pre-defined Order. We provide the design and implementation of three algorithms, which deploy a specialized cooperative transaction execution model. This model permits the propagation of written values along the chain of Ordered transactions. We show that, even in the presence of data conflicts, the proposed algorithms outperform single threaded execution, and other baseline and specialized state-of-the-art competitors (e.g., STMLite). The maximum speedup achieved in micro benchmarks, STAMP, PARSEC and SPEC200 applications is in the range of 4.3x -- 16.5x.
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Processing Transactions in a Predefined Order
arXiv: Distributed Parallel and Cluster Computing, 2018Co-Authors: Mohamed M Saad, Masoomeh Javidi Kishi, Shihao Jing, Sandeep Hans, Roberto PalmieriAbstract:In this paper we provide a high performance solution to the problem of committing transactions while enforcing a Predefined Order. We provide the design and implementation of three algorithms, which deploy a specialized cooperative transaction execution model. This model permits the propagation of written values along the chain of Ordered transactions. We show that, even in the presence of data conflicts, the proposed algorithms are able to outperform single-threaded execution, and other baseline and specialized state-of-the-art competitors (e.g., STMLite). The maximum speedup achieved in micro benchmarks, STAMP, PARSEC and SPEC200 applications is in the range of 4.3x -- 16.5x.
Soomee Kim - One of the best experts on this subject based on the ideXlab platform.
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A Comparative Study of Subset Construction Methods in OSEM Algorithms using Simulated Projection Data of Compton Camera
2015Co-Authors: Nucl Med, Soomee Kim, Jae Sung Lee, Juhahn Lee, J H Kim, Mol Imaging, Ph. D, Mi No Leee, Chan Hyeong KimAbstract:Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the Ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and Ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a Predefined Order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP i
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a comparative study of subset construction methods in osem algorithms using simulated projection data of compton camera
Nuclear Medicine and Molecular Imaging, 2007Co-Authors: Soomee Kim, Jae Sung Lee, Mino Lee, Juhahn Lee, J H Kim, Chan Hyeong Kim, C S Lee, Dong Soo Lee, Soojin LeeAbstract:Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the Ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and Ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a Predefined Order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. Conclusion: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available. (Nucl Med Mol Imaging 2007;41(3):234-240)
Chan Hyeong Kim - One of the best experts on this subject based on the ideXlab platform.
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A Comparative Study of Subset Construction Methods in OSEM Algorithms using Simulated Projection Data of Compton Camera
2015Co-Authors: Nucl Med, Soomee Kim, Jae Sung Lee, Juhahn Lee, J H Kim, Mol Imaging, Ph. D, Mi No Leee, Chan Hyeong KimAbstract:Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the Ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and Ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a Predefined Order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP i
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a comparative study of subset construction methods in osem algorithms using simulated projection data of compton camera
Nuclear Medicine and Molecular Imaging, 2007Co-Authors: Soomee Kim, Jae Sung Lee, Mino Lee, Juhahn Lee, J H Kim, Chan Hyeong Kim, C S Lee, Dong Soo Lee, Soojin LeeAbstract:Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the Ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and Ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a Predefined Order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. Conclusion: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available. (Nucl Med Mol Imaging 2007;41(3):234-240)
J H Kim - One of the best experts on this subject based on the ideXlab platform.
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A Comparative Study of Subset Construction Methods in OSEM Algorithms using Simulated Projection Data of Compton Camera
2015Co-Authors: Nucl Med, Soomee Kim, Jae Sung Lee, Juhahn Lee, J H Kim, Mol Imaging, Ph. D, Mi No Leee, Chan Hyeong KimAbstract:Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the Ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and Ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a Predefined Order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP i
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a comparative study of subset construction methods in osem algorithms using simulated projection data of compton camera
Nuclear Medicine and Molecular Imaging, 2007Co-Authors: Soomee Kim, Jae Sung Lee, Mino Lee, Juhahn Lee, J H Kim, Chan Hyeong Kim, C S Lee, Dong Soo Lee, Soojin LeeAbstract:Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the Ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and Ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a Predefined Order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. Conclusion: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available. (Nucl Med Mol Imaging 2007;41(3):234-240)
