Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/138383
題名: 利用深度學習演算法進行磁共振頻譜重建
Deep learning based MRS reconstruction
作者: 江宗諭
YU, JIANG ZONG
貢獻者: 蔡尚岳
Shang-Yueh Tsai
江宗諭
JIANG ZONG YU
關鍵詞: 磁共振頻譜
深度學習
重建頻譜
日期: 2021
上傳時間: 3-一月-2022
摘要: 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\n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,\n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。
參考文獻: [1] Gujar, S. K., Maheshwari, S., Björkman-Burtscher, I., & Sundgren, P. C. (2005). Magnetic resonance spectroscopy. Journal of neuro-ophthalmology, 25(3), 217- 226.\n[2 ] Dager, S. R., Oskin, N. M., Richards, T. L., & Posse, S. (2008). Research applications of magnetic resonance spectroscopy (MRS) to investigate psychiatric disorders. Topics in magnetic resonance imaging: TMRI, 19(2), 81.\n[3] Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., ... & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377.\n[4] Hatami, N., Sdika, M., & Ratiney, H. (2018, September). Magnetic resonance spectroscopy quantification using deep learning. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 467-475). Springer, Cham.\n[5] Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., & Inman, D. J. (2021). 1D convolutional neural networks and applications: A survey. Mechanical systems and signal processing, 151, 107398.\n[6] Birch, R., Peet, A. C., Dehghani, H., & Wilson, M. (2017). Influence of macromolecule baseline on 1H MR spectroscopic imaging reproducibility. Magnetic resonance in medicine, 77(1), 34-43.\n[7] Deep learning based MRS quantification : CNN integrated with water scaling and partial volume correction\n[8] Lee, H., Lee, H. H., & Kim, H. (2020). Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy. Magnetic resonance in medicine, 84(2), 559-568.\n[9] Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham. [\n\n[10] Lee, H. H., & Kim, H. (2019). Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain. Magnetic resonance in medicine, 82(1), 33-48.\n\n[11] Simpson, R., Devenyi, G. A., Jezzard, P., Hennessy, T. J., & Near, J. (2017). Advanced processing and simulation of MRS data using the FID appliance (FID‐ A)—an open source, MATLAB‐based toolkit. Magnetic resonance in medicine, 77(1), 23-33.
描述: 碩士
國立政治大學
應用物理研究所
108755007
資料來源: http://thesis.lib.nccu.edu.tw/record/#G0108755007
資料類型: thesis
Appears in Collections:學位論文

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