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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Main Authors: Wei-jhong Siao, 蕭為中
Other Authors: Teng-yi Huang
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/e448bc
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spelling 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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language zh-TW
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description 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.
author2 Teng-yi Huang
author_facet Teng-yi Huang
Wei-jhong Siao
蕭為中
author Wei-jhong Siao
蕭為中
spellingShingle Wei-jhong Siao
蕭為中
GPU-Accelerated High Performance Computing System: Application to Real-Time Functional Magnetic Resonance Imaging
author_sort 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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