| dc.contributor | 應物所 | |
| dc.creator (作者) | 蔡尚岳 | |
| dc.creator (作者) | Tsai, Shang-Yueh | |
| dc.creator (作者) | Huang, Yu-Long;Lin, Yi-Ru;Huang, Teng-Yi;Ko, Cheng-Wen | |
| dc.date (日期) | 2020-08 | |
| dc.date.accessioned | 29-Jul-2022 15:46:46 (UTC+8) | - |
| dc.date.available | 29-Jul-2022 15:46:46 (UTC+8) | - |
| dc.date.issued (上傳時間) | 29-Jul-2022 15:46:46 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/140953 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 104 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 2020 Proceedings of International Society for Magnetic Resonance in Medicine, International Society for Magnetic Resonance in Medicine, pp.2844 | |
| dc.title (題名) | Water scaling strategy for metabolites quantified in MRS by deep learning | |
| dc.type (資料類型) | conference | |
