Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing
BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, w...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
ZHANG Yuan [verfasserIn] XIE Honglan [verfasserIn] DU Guohao [verfasserIn] XU Mingwei [verfasserIn] XUE Yanling [verfasserIn] XIAO Tiqiao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Chinesisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: He jishu - Science Press, 2022, 44(2021), 6, Seite 060101-060101 |
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| Übergeordnetes Werk: |
volume:44 ; year:2021 ; number:6 ; pages:060101-060101 |
| Links: |
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| DOI / URN: |
10.11889/j.0253-3219.2021.hjs.44.060101 |
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| Katalog-ID: |
DOAJ080873189 |
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| 520 | |a BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, which leads to a large backlog of experimental data. Graphics processing unit (GPU) is widely used in the field of high-performance computing because of its multi-core architecture and large-scale parallelization.PurposeThis study aims to speed up CT reconstruction by making use of GPU parallel computation for X-ray dynamic micro-CT on the X-ray imaging beamline at SSRF.MethodsAccording to computer unified device architecture (CUDA) programming standard, the back projection process of the filtered back projection (FBP) CT reconstruction algorithm was parallelized, and fast reconstruction of X-ray dynamic micro-CT was implemented on GPU of NVDIA RTX2080. CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized. | ||
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10.11889/j.0253-3219.2021.hjs.44.060101 doi (DE-627)DOAJ080873189 (DE-599)DOAJ75e9943d5dec4466b672d632a3061744 DE-627 ger DE-627 rakwb chi TK9001-9401 ZHANG Yuan verfasserin aut Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, which leads to a large backlog of experimental data. Graphics processing unit (GPU) is widely used in the field of high-performance computing because of its multi-core architecture and large-scale parallelization.PurposeThis study aims to speed up CT reconstruction by making use of GPU parallel computation for X-ray dynamic micro-CT on the X-ray imaging beamline at SSRF.MethodsAccording to computer unified device architecture (CUDA) programming standard, the back projection process of the filtered back projection (FBP) CT reconstruction algorithm was parallelized, and fast reconstruction of X-ray dynamic micro-CT was implemented on GPU of NVDIA RTX2080. CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized. x-ray dynamic micro-ct gpu parallel computing ct reconstruction fast x-ray imaging Nuclear engineering. Atomic power XIE Honglan verfasserin aut DU Guohao verfasserin aut XU Mingwei verfasserin aut XUE Yanling verfasserin aut XIAO Tiqiao verfasserin aut In He jishu Science Press, 2022 44(2021), 6, Seite 060101-060101 (DE-627)DOAJ078593506 02533219 nnns volume:44 year:2021 number:6 pages:060101-060101 https://doi.org/10.11889/j.0253-3219.2021.hjs.44.060101 kostenfrei https://doaj.org/article/75e9943d5dec4466b672d632a3061744 kostenfrei http://www.hjs.sinap.ac.cn/thesisDetails#10.11889/j.0253-3219.2021.hjs.44.060101&lang=zh kostenfrei https://doaj.org/toc/0253-3219 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 44 2021 6 060101-060101 |
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10.11889/j.0253-3219.2021.hjs.44.060101 doi (DE-627)DOAJ080873189 (DE-599)DOAJ75e9943d5dec4466b672d632a3061744 DE-627 ger DE-627 rakwb chi TK9001-9401 ZHANG Yuan verfasserin aut Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, which leads to a large backlog of experimental data. Graphics processing unit (GPU) is widely used in the field of high-performance computing because of its multi-core architecture and large-scale parallelization.PurposeThis study aims to speed up CT reconstruction by making use of GPU parallel computation for X-ray dynamic micro-CT on the X-ray imaging beamline at SSRF.MethodsAccording to computer unified device architecture (CUDA) programming standard, the back projection process of the filtered back projection (FBP) CT reconstruction algorithm was parallelized, and fast reconstruction of X-ray dynamic micro-CT was implemented on GPU of NVDIA RTX2080. CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized. x-ray dynamic micro-ct gpu parallel computing ct reconstruction fast x-ray imaging Nuclear engineering. Atomic power XIE Honglan verfasserin aut DU Guohao verfasserin aut XU Mingwei verfasserin aut XUE Yanling verfasserin aut XIAO Tiqiao verfasserin aut In He jishu Science Press, 2022 44(2021), 6, Seite 060101-060101 (DE-627)DOAJ078593506 02533219 nnns volume:44 year:2021 number:6 pages:060101-060101 https://doi.org/10.11889/j.0253-3219.2021.hjs.44.060101 kostenfrei https://doaj.org/article/75e9943d5dec4466b672d632a3061744 kostenfrei http://www.hjs.sinap.ac.cn/thesisDetails#10.11889/j.0253-3219.2021.hjs.44.060101&lang=zh kostenfrei https://doaj.org/toc/0253-3219 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 44 2021 6 060101-060101 |
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10.11889/j.0253-3219.2021.hjs.44.060101 doi (DE-627)DOAJ080873189 (DE-599)DOAJ75e9943d5dec4466b672d632a3061744 DE-627 ger DE-627 rakwb chi TK9001-9401 ZHANG Yuan verfasserin aut Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, which leads to a large backlog of experimental data. Graphics processing unit (GPU) is widely used in the field of high-performance computing because of its multi-core architecture and large-scale parallelization.PurposeThis study aims to speed up CT reconstruction by making use of GPU parallel computation for X-ray dynamic micro-CT on the X-ray imaging beamline at SSRF.MethodsAccording to computer unified device architecture (CUDA) programming standard, the back projection process of the filtered back projection (FBP) CT reconstruction algorithm was parallelized, and fast reconstruction of X-ray dynamic micro-CT was implemented on GPU of NVDIA RTX2080. CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized. x-ray dynamic micro-ct gpu parallel computing ct reconstruction fast x-ray imaging Nuclear engineering. Atomic power XIE Honglan verfasserin aut DU Guohao verfasserin aut XU Mingwei verfasserin aut XUE Yanling verfasserin aut XIAO Tiqiao verfasserin aut In He jishu Science Press, 2022 44(2021), 6, Seite 060101-060101 (DE-627)DOAJ078593506 02533219 nnns volume:44 year:2021 number:6 pages:060101-060101 https://doi.org/10.11889/j.0253-3219.2021.hjs.44.060101 kostenfrei https://doaj.org/article/75e9943d5dec4466b672d632a3061744 kostenfrei http://www.hjs.sinap.ac.cn/thesisDetails#10.11889/j.0253-3219.2021.hjs.44.060101&lang=zh kostenfrei https://doaj.org/toc/0253-3219 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 44 2021 6 060101-060101 |
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10.11889/j.0253-3219.2021.hjs.44.060101 doi (DE-627)DOAJ080873189 (DE-599)DOAJ75e9943d5dec4466b672d632a3061744 DE-627 ger DE-627 rakwb chi TK9001-9401 ZHANG Yuan verfasserin aut Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, which leads to a large backlog of experimental data. Graphics processing unit (GPU) is widely used in the field of high-performance computing because of its multi-core architecture and large-scale parallelization.PurposeThis study aims to speed up CT reconstruction by making use of GPU parallel computation for X-ray dynamic micro-CT on the X-ray imaging beamline at SSRF.MethodsAccording to computer unified device architecture (CUDA) programming standard, the back projection process of the filtered back projection (FBP) CT reconstruction algorithm was parallelized, and fast reconstruction of X-ray dynamic micro-CT was implemented on GPU of NVDIA RTX2080. CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized. x-ray dynamic micro-ct gpu parallel computing ct reconstruction fast x-ray imaging Nuclear engineering. Atomic power XIE Honglan verfasserin aut DU Guohao verfasserin aut XU Mingwei verfasserin aut XUE Yanling verfasserin aut XIAO Tiqiao verfasserin aut In He jishu Science Press, 2022 44(2021), 6, Seite 060101-060101 (DE-627)DOAJ078593506 02533219 nnns volume:44 year:2021 number:6 pages:060101-060101 https://doi.org/10.11889/j.0253-3219.2021.hjs.44.060101 kostenfrei https://doaj.org/article/75e9943d5dec4466b672d632a3061744 kostenfrei http://www.hjs.sinap.ac.cn/thesisDetails#10.11889/j.0253-3219.2021.hjs.44.060101&lang=zh kostenfrei https://doaj.org/toc/0253-3219 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 44 2021 6 060101-060101 |
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10.11889/j.0253-3219.2021.hjs.44.060101 doi (DE-627)DOAJ080873189 (DE-599)DOAJ75e9943d5dec4466b672d632a3061744 DE-627 ger DE-627 rakwb chi TK9001-9401 ZHANG Yuan verfasserin aut Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, which leads to a large backlog of experimental data. Graphics processing unit (GPU) is widely used in the field of high-performance computing because of its multi-core architecture and large-scale parallelization.PurposeThis study aims to speed up CT reconstruction by making use of GPU parallel computation for X-ray dynamic micro-CT on the X-ray imaging beamline at SSRF.MethodsAccording to computer unified device architecture (CUDA) programming standard, the back projection process of the filtered back projection (FBP) CT reconstruction algorithm was parallelized, and fast reconstruction of X-ray dynamic micro-CT was implemented on GPU of NVDIA RTX2080. CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized. x-ray dynamic micro-ct gpu parallel computing ct reconstruction fast x-ray imaging Nuclear engineering. Atomic power XIE Honglan verfasserin aut DU Guohao verfasserin aut XU Mingwei verfasserin aut XUE Yanling verfasserin aut XIAO Tiqiao verfasserin aut In He jishu Science Press, 2022 44(2021), 6, Seite 060101-060101 (DE-627)DOAJ078593506 02533219 nnns volume:44 year:2021 number:6 pages:060101-060101 https://doi.org/10.11889/j.0253-3219.2021.hjs.44.060101 kostenfrei https://doaj.org/article/75e9943d5dec4466b672d632a3061744 kostenfrei http://www.hjs.sinap.ac.cn/thesisDetails#10.11889/j.0253-3219.2021.hjs.44.060101&lang=zh kostenfrei https://doaj.org/toc/0253-3219 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 44 2021 6 060101-060101 |
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CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">x-ray dynamic micro-ct</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">gpu</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">parallel computing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ct reconstruction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">fast x-ray imaging</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Nuclear engineering. 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Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing |
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Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing |
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ZHANG Yuan |
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ZHANG Yuan XIE Honglan DU Guohao XU Mingwei XUE Yanling XIAO Tiqiao |
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Elektronische Aufsätze |
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ZHANG Yuan |
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10.11889/j.0253-3219.2021.hjs.44.060101 |
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fast reconstruction of x-ray dynamic micro-ct based on gpu parallel computing |
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TK9001-9401 |
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Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing |
| abstract |
BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, which leads to a large backlog of experimental data. Graphics processing unit (GPU) is widely used in the field of high-performance computing because of its multi-core architecture and large-scale parallelization.PurposeThis study aims to speed up CT reconstruction by making use of GPU parallel computation for X-ray dynamic micro-CT on the X-ray imaging beamline at SSRF.MethodsAccording to computer unified device architecture (CUDA) programming standard, the back projection process of the filtered back projection (FBP) CT reconstruction algorithm was parallelized, and fast reconstruction of X-ray dynamic micro-CT was implemented on GPU of NVDIA RTX2080. CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized. |
| abstractGer |
BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, which leads to a large backlog of experimental data. Graphics processing unit (GPU) is widely used in the field of high-performance computing because of its multi-core architecture and large-scale parallelization.PurposeThis study aims to speed up CT reconstruction by making use of GPU parallel computation for X-ray dynamic micro-CT on the X-ray imaging beamline at SSRF.MethodsAccording to computer unified device architecture (CUDA) programming standard, the back projection process of the filtered back projection (FBP) CT reconstruction algorithm was parallelized, and fast reconstruction of X-ray dynamic micro-CT was implemented on GPU of NVDIA RTX2080. CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized. |
| abstract_unstemmed |
BackgroundMassive projection data are produced by user experiments with the X-ray dynamic micro-computed tomography (CT) method on the X-ray imaging beamline at Shanghai synchrotron radiation facility (SSRF). CT reconstruction method based on central processing unit (CPU) serial computing is slow, which leads to a large backlog of experimental data. Graphics processing unit (GPU) is widely used in the field of high-performance computing because of its multi-core architecture and large-scale parallelization.PurposeThis study aims to speed up CT reconstruction by making use of GPU parallel computation for X-ray dynamic micro-CT on the X-ray imaging beamline at SSRF.MethodsAccording to computer unified device architecture (CUDA) programming standard, the back projection process of the filtered back projection (FBP) CT reconstruction algorithm was parallelized, and fast reconstruction of X-ray dynamic micro-CT was implemented on GPU of NVDIA RTX2080. CT reconstruction time was compared with that of CPU-based reconstruction method.ResultsThe comparison of CT reconstruction time shows that the CT reconstruction based on GPU parallel computing achieves about 200 times speedup compared with the CT reconstruction based on CPU serial computing, and the reconstruction time of a set of CT projection data is reduced from minutes to seconds.ConclusionsWith the powerful computing power of GPU, the fast reconstruction of X-ray dynamic micro-CT can be realized. |
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Fast reconstruction of X-ray dynamic micro-CT based on GPU parallel computing |
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https://doi.org/10.11889/j.0253-3219.2021.hjs.44.060101 https://doaj.org/article/75e9943d5dec4466b672d632a3061744 http://www.hjs.sinap.ac.cn/thesisDetails#10.11889/j.0253-3219.2021.hjs.44.060101&lang=zh https://doaj.org/toc/0253-3219 |
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2024-07-03T17:00:26.634Z |
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