Breathing Freely: Self-supervised Liver T1rho Mapping from A Single T1rho-weighted Image

Chaoxing Huang, Yurui Qian, Jian Hou, Baiyan Jiang, Queenie Chan, Vincent Wong, Winnie Chiu Wing Chu, Weitian Chen

Keywords: magnetic resonance, T1rho mapping, liver-tissue, self-supervised learning

TL;DR: Learning-based Liver T1rho Mapping

Abstract: Quantitative $T1rho$ imaging is a promising technique for assessment of chronic liver disease. The standard approach requires acquisition of multiple $T1rho$-weighted images of the liver to quantify $T1rho$ relaxation time. The quantification accuracy can be affected by respiratory motion if the subjects cannot hold the breath during the scan. To tackle this problem, we propose a self-supervised mapping method by taking only one $T1rho$-weighted image to do the mapping. Our method takes into account of signal scale variations in MR scan when performing $T1rho$ quantification. Preliminary experimental results show that our method can achieve better mapping performance than the traditional fitting method, particularly in free-breathing scenarios.

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Paper Type: validation/application paper

Primary Subject Area: Application: Radiology

Secondary Subject Area: Application: Other

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Code And Data: https://github.com/mricuhk/demo_self-supervised_T1rho_mapping