Accelerating pixel-by-pixel non-linear curve fitting using parallel computation on graphic processing units: Application to pulmonary perfusion mapping
碩士 === 國立臺灣科技大學 === 電機工程系 === 99 === Due to the technical development of the medical image in recent years, MRI is utilized to evaluate pulmonary perfusion. After injection of contrast agent, the washing-in and washing-out of contract agent in tissues is quantified through a dynamic scan. Then, the...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/625xuh |
| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 99 === Due to the technical development of the medical image in recent years, MRI is utilized to evaluate pulmonary perfusion. After injection of contrast agent, the washing-in and washing-out of contract agent in tissues is quantified through a dynamic scan. Then, the blood flow analysis of the patient can be determined and provided for the follow-up diagnosis. The quantification analysis of lung tissues is to obtain perfusion parameters by using gamma curve fitting. Pixel-by-pixel curve fitting of perfusion generally takes minutes or hours by MATLAB system. Recently, the parallel computing using general-purpose computation on graphics processing units (GPGPU) shows able to accelerate the scientific computing if the algorithm can be parallelized. In this study, GPGPU parallel computation is proposed to reduce the whole calculation time of gamma-curve fitting by Levenberg-Marquardt algorithm. Applying GPU program on the 7-slice perfusion data set, the parallel algorithm reduced the computation time to ~3 seconds. We conclude that the GPU computing is a promising method to accelerate curve fitting.
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