  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 T$T_{1\rho}$ and T$_2$
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.
