Breathing Freely: Self-Supervised Liver T1rho Mapping from a Single T1rho-Weighted Image
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.
Cite
Text
Huang et al. "Breathing Freely: Self-Supervised Liver T1rho Mapping from a Single T1rho-Weighted Image." Medical Imaging with Deep Learning, 2023.Markdown
[Huang et al. "Breathing Freely: Self-Supervised Liver T1rho Mapping from a Single T1rho-Weighted Image." Medical Imaging with Deep Learning, 2023.](https://mlanthology.org/midl/2023/huang2023midl-breathing/)BibTeX
@inproceedings{huang2023midl-breathing,
title = {{Breathing Freely: Self-Supervised Liver T1rho Mapping from a Single T1rho-Weighted Image}},
author = {Huang, Chaoxing and Qian, Yurui and Hou, Jian and Jiang, Baiyan and Chan, Queenie and Wong, Vincent and Chu, Winne and Chen, Weitian},
booktitle = {Medical Imaging with Deep Learning},
year = {2023},
pages = {565-575},
volume = {172},
url = {https://mlanthology.org/midl/2023/huang2023midl-breathing/}
}