- 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.
- Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
- Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
- Paper Type: validation/application paper
- Primary Subject Area: Application: Radiology
- Secondary Subject Area: Application: Other
- Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
- Code And Data: https://github.com/mricuhk/demo_self-supervised_T1rho_mapping