关键词: 磁共振成像, 参数图像重建, 深度学习, 自监督网络, 肝脏铁过载

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