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政大機構典藏 > 理學院 > 應用物理研究所 > 會議論文 > Item 140.119/140953

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/140953

Title:	Water scaling strategy for metabolites quantified in MRS by deep learning
Authors:	蔡尚岳 Tsai, Shang-Yueh Huang, Yu-Long;Lin, Yi-Ru;Huang, Teng-Yi;Ko, Cheng-Wen
Contributors:	應物所
Date:	2020-08
Issue Date:	2022-07-29 15:46:46 (UTC+8)
Abstract:	Recently, it has been shown that MRS can be analyzed by a convolutional neural network (CNN) with concentrations quantified in a relative way. Here, we propose to scale in vivo MRS data according to water signal in simulated spectra and in vivo data so that CNN spectra can be scaled to institutional units for possible between subject comparison. Our results show that the quantified metabolites are at the same level as those quantified using LCModel with water scaling method but with less repeatability. A further phantom study is necessary to validate the proposed method.
Relation:	2020 Proceedings of International Society for Magnetic Resonance in Medicine, International Society for Magnetic Resonance in Medicine, pp.2844
Data Type:	conference
Appears in Collections:	[應用物理研究所 ] 會議論文

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