Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/138383


Title: 利用深度學習演算法進行磁共振頻譜重建
Deep learning based MRS reconstruction
Authors: 江宗諭
YU, JIANG ZONG
Contributors: 蔡尚岳
Shang-Yueh Tsai
江宗諭
JIANG ZONG YU
Keywords: 磁共振頻譜
深度學習
重建頻譜
Date: 2021
Issue Date: 2022-01-03 16:11:39 (UTC+8)
Abstract: 最近深度學習技術廣泛的應用在MRS 的研究上,例如使用卷積神經網路CNN
模型來去除雜訊或者移除基線等等,而本研究主要是在探討使用U-NET 模型來進行大腦頻譜的重建,U-Net 是一種卷積神經網絡(CNN)方法,他可以更好的分割生物醫學影像。先將大腦的模擬頻譜傅立葉轉換成FID 之後進行截斷,如果截斷後留下的點數為8 稱為tFID8,若留下16 的點稱為tFID16,以此類推,在進行傅立葉轉換獲得截斷光譜,藉由這些頻譜來訓練模型,一開始訓練了tFID2048、tFID1024、tFID512、tFID256、tFID128、tFID64、tFID32 、tFID16、tFID8,總共8 個模型,通過觀察不同模型的結果和比較,最終挑選tFID128 和tFID32 這兩個模型應用在活體頻譜上,結果因為水頻譜關係得到不好的結果,為了得到最好的結果,將tFID128 和tFID32 模型進行修改嘗試獲取最好的重建活體頻譜,
結果顯示在模擬頻譜的重建算是非常成功,但是應用在活體頻譜的重建上就不盡理想,所以在進一步的分析模擬頻譜與活體頻譜的誤差,並且將進行一些修正,並重新訓練,得知是因為模擬頻譜與活體頻譜不夠相似造成還原結果有些差異。
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Description: 碩士
國立政治大學
應用物理研究所
108755007
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108755007
Data Type: thesis
Appears in Collections:[應用物理研究所 ] 學位論文

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