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

TL;DR

This paper introduces a self-supervised neural network for liver $T_{1 ho}$ mapping that incorporates uncertainty estimation to improve accuracy and confidence, especially with limited data, in non-invasive tissue assessment.

Contribution

It presents a novel uncertainty-aware self-supervised learning approach for $T_{1 ho}$ mapping that leverages relaxation constraints and models both epistemic and aleatoric uncertainties.

Findings

Outperforms existing methods with fewer images for liver $T_{1 ho}$ mapping.

Uncertainty estimation aligns with actual confidence levels in liver imaging.

Effective regularization prevents learning from imperfect data.

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.