MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping.

Journal: NeuroImage
Published Date: Jul 8, 2021

Abstract

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (χ, χ and χ) and the acquired single-orientation phase. The convolutional neural networks are embedded into the physical model to learn a regularization term containing prior information. χ and phase induced by χ and χ terms were used as the labels for network training. Quantitative evaluation metrics were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications.

Authors

  • Ruimin Feng
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Jiayi Zhao
    School of Psychology, Shanghai University of Sport, Shanghai, China.
  • He Wang
    Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, China International Neuroscience Institute, Beijing, China.
  • Baofeng Yang
    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
  • Jie Feng
  • Yuting Shi
    National Clinical Research Center of Oral Diseases, Shanghai 200011, China.
  • Ming Zhang
    Heilongjiang Key Laboratory for Laboratory Animals and Comparative Medicine, College of Veterinary Medicine, Harbin 150030, China.
  • Chunlei Liu
    Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
  • Yuyao Zhang
    School of Information and Science and Technology, ShanghaiTech University, Shanghai, China.
  • Jie Zhuang
    School of Psychology, Shanghai University of Sport, Shanghai, China.
  • Hongjiang Wei
    Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. Electronic address: hongjiang.wei@sjtu.edu.cn.

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