Chinese Journal of Magnetic Resonance ›› 2023, Vol. 40 ›› Issue (3): 258-269.doi: 10.11938/cjmr20233050

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Magnetic Resonance R2* Parameter Mapping of Liver Based on Self-supervised Deep Neural Network

LU Qiqi,LIAN Zifeng,LI Jialong,SI Wenbin,MAI Zhaohua,FENG Yanqiu*()   

  1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
  • Received:2023-01-09 Published:2023-09-05 Online:2023-03-02
  • Contact: *Tel: +86 20 61648271, E-mail: foree@smu.edu.cn.

Abstract:

Magnetic resonance (MR) effective transverse relaxation rate ($R_{2}^{*}$) technique has been widely applied for assessing hepatic iron concentration. However,$R_{2}^{*}$ mapping of iron-loaded liver can be severely degraded by noise. With the development of deep learning, deep neural networks have become effective tools for MR parameter mapping. In this study, a model-guided self-supervised deep neural network was designed for MR $R_{2}^{*}$ parameter mapping of iron-loaded liver. A novel loss function that integrated a noise-corrected physical model and an improved total variation model was used to train the network, which did not require reference $R_{2}^{*}$ parameter maps. Meanwhile, compared to the conventional parameter fitting methods, model-guided self-supervised deep learning method enabled accurate and efficient $R_{2}^{*}$ mapping of iron-loaded liver, suppressed the effect of noise, corrected the bias introduced by noise, and preserved the detailed structure of $R_{2}^{*}$ map.

Key words: MR imaging, parameter mapping, deep learning, self-supervised network, liver iron overload

CLC Number: