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

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.

Keywords

• mr imaging
• parameter mapping
• deep learning
• self-supervised network
• liver iron overload