Uncertainty-Aware Self-supervised Neural Network for Liver $T_{1\rho}$ Mapping with Relaxation Constraint

• URL: http://arxiv.org/abs/2207.03105v1
• Date: Thu, 7 Jul 2022 06:10:34 GMT
• Title: Uncertainty-Aware Self-supervised Neural Network for Liver $T_{1\rho}$ Mapping with Relaxation Constraint
• Authors: Chaoxing Huang, Yurui Qian, Simon Chun Ho Yu, Jian Hou, Baiyan Jiang, Queenie Chan, Vincent Wai-Sun Wong, Winnie Chiu-Wing Chu, Weitian Chen
• Abstract summary: Learning-based approaches can map $T_1rho$ from a reduced number of $T_1rho$ weighted images. Existing methods do not provide the confidence level of the $T_1rho$ estimation. We propose a self-supervised learning neural network that learns a $T_1rho$ mapping using the relaxation constraint in the learning process.
• Score: 2.0131108402156146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: $T_{1\rho}$ mapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can map $T_{1\rho}$ from a reduced number of $T_{1\rho}$ weighted images, but requires significant amounts of high quality training data. Moreover, existing methods do not provide the confidence level of the $T_{1\rho}$ estimation. To address these problems, we proposed a self-supervised learning neural network that learns a $T_{1\rho}$ mapping using the relaxation constraint in the learning process. Epistemic uncertainty and aleatoric uncertainty are modelled for the $T_{1\rho}$ quantification network to provide a Bayesian confidence estimation of the $T_{1\rho}$ mapping. The uncertainty estimation can also regularize the model to prevent it from learning imperfect data. We conducted experiments on $T_{1\rho}$ data collected from 52 patients with non-alcoholic fatty liver disease. The results showed that our method outperformed the existing methods for $T_{1\rho}$ quantification of the liver using as few as two $T_{1\rho}$-weighted images. Our uncertainty estimation provided a feasible way of modelling the confidence of the self-supervised learning based $T_{1\rho}$ estimation, which is consistent with the reality in liver $T_{1\rho}$ imaging.

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