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Uncertainty-weighted Multi-tasking for $T_{1\rho}$ and T$_2$ Mapping in the Liver with Self-supervised Learning
- Publication Year :
- 2023
-
Abstract
- 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.
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2303.07623
- Document Type :
- Working Paper