Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal.

Journal: IEEE transactions on bio-medical engineering
PMID: 38224519

Abstract

OBJECTIVE: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation.

Authors

  • Dicheng Chen
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, P.O. Box 979, Xiamen, 361005, P.R. China.
  • Meijin Lin
    Department of Applied Marine Physics & Engineering, Xiamen University, Xiamen, China.
  • Huiting Liu
  • Jiayu Li
    School of Tourism and Geography, School of Biology and Agriculture, Shaoguan University, Shaoguan, China.
  • Yirong Zhou
  • Taishan Kang
    Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China.
  • Liangjie Lin
    MSC Clinical & Technical Solutions, Philips Healthcare, Beijing, China.
  • Zhigang Wu
    State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Jiazheng Wang
    MSC Clinical & Technical Solutions, Philips Healthcare, Beijing, China.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Jianzhong Lin
    Magnetic Resonance Center, Zhongshan Hospital Xiamen University, Xiamen 361004, China.
  • Xi Chen
    Department of Critical care medicine, Shenzhen Hospital, Southern Medical University, Guangdong, Shenzhen, China.
  • Di Guo
  • Xiaobo Qu

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