Uncertainty-aware self-supervised neural network for liver T1ρ mapping with relaxation constraint. (21st November 2022)

• Record Type: Journal Article
• Authors: Huang, Chaoxing; Qian, Yurui; Yu, Simon Chun-Ho; Hou, Jian; Jiang, Baiyan; Chan, Queenie; Wong, Vincent Wai-Sun; Chu, Winnie Chiu-Wing; Chen, Weitian
• Is Part Of: Physics in medicine & biology. Volume 67:Number 22(2022)
• Publication Date: 2022-11-21
• Subjects: quantitative MRI -- self-supervised learning -- uncertainty estimation -- T1ρ; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac9e3e
• Languages: English
• ISSNs: 0031-9155

Abstract

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 further regularize the model to improve the performance of the model and it is consistent with the confidence level of liver T 1 ρ values. Significance . Our method demonstrates the potential for accelerating the T 1 ρ mapping of the liver by using a reduced number of images. It simultaneously provides uncertainty of T 1 ρ quantification which is desirable in clinical applications.