GPU-Accelerated High Performance Computing System: Application to Real-Time Functional Magnetic Resonance Imaging
碩士 === 國立臺灣科技大學 === 電機工程系 === 99 === This study attempts to build a real-time functional magnetic resonance imaging system (rtfMRI) to monitor blood-oxygen-level-dependent (BOLD) signal during fMRI experiment. To detect the BOLD signal change, a Gaussian filter and a general linear model analysis we...
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ndltd-TW-099NTUS54420642019-05-15T20:42:06Z http://ndltd.ncl.edu.tw/handle/e448bc GPU-Accelerated High Performance Computing System: Application to Real-Time Functional Magnetic Resonance Imaging 圖形處理器之高性能運算系統:應用於即時功能性磁振造影之研究 Wei-jhong Siao 蕭為中 碩士 國立臺灣科技大學 電機工程系 99 This study attempts to build a real-time functional magnetic resonance imaging system (rtfMRI) to monitor blood-oxygen-level-dependent (BOLD) signal during fMRI experiment. To detect the BOLD signal change, a Gaussian filter and a general linear model analysis were performed on MRI images immediately subsequent to image acquisitions. A graphic processing unit (GPU) with massively parallel computation kernels was used to accelerate the image processing [i.e. gaussian filter and general linear model analysis (GLM)]. The GPU program was compatible to MATLAB environment through a communication interface of MATLAB and C language. Using GPU computation, the analysis of rtfMRI could be accomplished in less than 1 second in a conventional personal computer. Teng-yi Huang 黃騰毅 2011 學位論文 ; thesis 43 zh-TW |
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碩士 === 國立臺灣科技大學 === 電機工程系 === 99 === This study attempts to build a real-time functional magnetic resonance imaging system (rtfMRI) to monitor blood-oxygen-level-dependent (BOLD) signal during fMRI experiment. To detect the BOLD signal change, a Gaussian filter and a general linear model analysis were performed on MRI images immediately subsequent to image acquisitions. A graphic processing unit (GPU) with massively parallel computation kernels was used to accelerate the image processing [i.e. gaussian filter and general linear model analysis (GLM)]. The GPU program was compatible to MATLAB environment through a communication interface of MATLAB and C language. Using GPU computation, the analysis of rtfMRI could be accomplished in less than 1 second in a conventional personal computer.
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Teng-yi Huang |
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Teng-yi Huang Wei-jhong Siao 蕭為中 |
author |
Wei-jhong Siao 蕭為中 |
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Wei-jhong Siao 蕭為中 GPU-Accelerated High Performance Computing System: Application to Real-Time Functional Magnetic Resonance Imaging |
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Wei-jhong Siao |
title |
GPU-Accelerated High Performance Computing System: Application to Real-Time Functional Magnetic Resonance Imaging |
title_short |
GPU-Accelerated High Performance Computing System: Application to Real-Time Functional Magnetic Resonance Imaging |
title_full |
GPU-Accelerated High Performance Computing System: Application to Real-Time Functional Magnetic Resonance Imaging |
title_fullStr |
GPU-Accelerated High Performance Computing System: Application to Real-Time Functional Magnetic Resonance Imaging |
title_full_unstemmed |
GPU-Accelerated High Performance Computing System: Application to Real-Time Functional Magnetic Resonance Imaging |
title_sort |
gpu-accelerated high performance computing system: application to real-time functional magnetic resonance imaging |
publishDate |
2011 |
url |
http://ndltd.ncl.edu.tw/handle/e448bc |
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