Uncertainty-aware self-supervised neural network for liver T 1 ρ mapping with relaxation constraint

作者：Chaoxing Huang, Yurui Qian, Simon C.H. Yu, Jian Hou, Baiyan Jiang, Queenie Chan, Vincent Wai‐Sun Wong, Chiu‐Wing Winnie Chu, Weitian Chen · 发表于：Physics in Medicine and Biology · 年份：2022 · DOI：10.1088/1361-6560/ac9e3e · 被引用次数：9 · 研究领域：Advanced MRI Techniques and Applications、Advanced Neuroimaging Techniques and Applications、Sparse and Compressive Sensing Techniques

Abstract Objective . T 1 ρ mapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can map T 1 ρ from a reduced number of T 1 ρ weighted images but requires significant amounts of high-quality training data. Moreover, existing methods do not provide the confidence level of the T 1 ρ estimation. We aim to develop a learning-based liver T 1 ρ mapping approach that can map T 1 ρ with a reduced number of images and provide uncertainty estimation. Approach . We proposed a self-supervised neural network that learns a T 1 ρ mapping using the relaxation constraint in the learning process. Epistemic uncertainty and aleatoric uncertainty are modelled for the T 1 ρ quantification network to provide a Bayesian confidence estimation of the T 1 ρ mapping. The uncertainty estimation can also regularize the model to prevent it from learning imperfect data . Main results . We conducted experiments on T 1 ρ data collected from 52 patients with non-alcoholic fatty liver disease. The results showed that when only collecting two T 1 ρ -weighted images, our method outperformed the existing methods for T 1 ρ quantification of the liver. Our uncertainty estimation can...