Abstract #2844

Water Scaling Strategy for Metabolites Quantified in MRS by Deep Learning

Yu-Long Huang¹, Yi-Ru Lin¹, Teng-Yi Huang², Cheng-Wen Ko³, and Shang-Yueh Tsai^4,5

¹Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ²Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ³Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, ⁴Graduate Institute of Applied Physics, National ChengChi University, Taipei, Taiwan, ⁵Research Center of Mind, Brain and Learning, National ChengChi University, Taipei, Taiwan

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.

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