Treffer: 非線性曲線擬合使用圖形運算單元之平行運算：應用於肺部微灌流影像 ; Accelerating pixel-by-pixel non-linear curve fitting using parallel computation on graphic processing units: Application to pulmonary perfusion mapping

國立臺灣科技大學電機工程系 ; 學位：碩士 ; 指導教授：黃騰毅 ; 近年來由於磁振造影的技術進步，磁振影像亦可用來量測肺部微灌流。肺部的區域血流供應與氣體供應是否良好匹配決定了氧氣的交換率，因此對比劑微灌流影像是一種有效檢查肺部疾病的方法，病患注射對比劑後，經過MRI等醫療設備觀察對比劑流動的狀況，可以讓醫師知道肺部的血??況，是否發生血塊堵?(肺栓?)的現象。為了分析肺部組織血流的情況，需要複雜的曲線擬合參數計算，一般都需花上幾十分鐘甚至幾個小時，醫師就不能立即診斷病人的病況。近年來，通用圖形平行運算(GPGPU)逐漸能加速科學運算的技術，並用在演算法能夠被平行處理，能有效加快需要大量平行計算的資料。我們的研究中，提出以通用圖形平行運算單元運用在曲線擬合的運算上，減少整個運算過程的時間。當應用在Levenberg-Marquardt 演算法中，平行運算的計算時間改善到3秒的計算時間。總結，我們提出的通用圖形平行運算有希望去降低曲線擬合的計算時間。 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.