Multi-parametric mapping of MRI relaxations in liver has the potential of revealing pathological information of the liver. A self-supervised learning based multi-parametric mapping method is proposed to map TT1ρ and T2 simultaneously, by utilising the relaxation constraint in the learning process. Data noise of different mapping tasks is utilised to make the model uncertainty-aware, which adaptively weight different mapping tasks during learning. The method was examined on a dataset of 51 patients with non-alcoholic fatter liver disease. Results showed that the proposed method can produce comparable parametric maps to the traditional multi-contrast pixel wise fitting method, with a reduced number of images and less computation time. The uncertainty weighting also improves the model performance. It has the potential of accelerating MRI quantitative imaging.
@article{arxiv.2303.07623,
title = {Uncertainty-weighted Multi-tasking for $T_{1\rho}$ and T$_2$ Mapping in the Liver with Self-supervised Learning},
author = {Chaoxing Huang and Yurui Qian and Jian Hou and Baiyan Jiang and Queenie Chan and Vincent WS Wong and Winnie CW Chu and Weitian Chen},
journal= {arXiv preprint arXiv:2303.07623},
year = {2023}
}