Chinese Journal of Magnetic Resonance >
Magnetic Resonance R2* Parameter Mapping of Liver Based on Self-supervised Deep Neural Network
Received date: 2023-01-09
Online published: 2023-03-02
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.
Qiqi LU , Zifeng LIAN , Jialong LI , Wenbin SI , Zhaohua MAI , Yanqiu FENG . Magnetic Resonance R2* Parameter Mapping of Liver Based on Self-supervised Deep Neural Network[J]. Chinese Journal of Magnetic Resonance, 2023 , 40(3) : 258 -269 . DOI: 10.11938/cjmr20233050
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