Meeting Banner
Abstract #1780

An self-supervised deep learning based super-resolution method for quantitative MRI

Muzi Guo1,2, Yuanyuan Liu1, Yuxin Yang1, Dong Liang1, Hairong Zheng1, and Yanjie Zhu1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2University of Chinese Academy of Sciences, Bejing, China

High-resolution (HR) quantitative magnetic resonance images (qMRI) are widely used in clinical diagnosis. However, acquisition of such high signal-to-noise ratio data is time consuming, and could lead to motion artifacts. Super-resolution (SR) approaches provide a better trade-off between acquisition time and spatial resolution. However, State-of-the-art SR methods are mostly supervised, which require external training data consisting of specific LR-HR pairs, and have not considered the quantitative conditions, which leads to the estimated quantitative map inaccurate. An self-supervised super-resolution algorithm under quantitative conditions is presented. Experiments on T1ρ quantitative images show encouraging improvements compared to competing SR methods.

This abstract and the presentation materials are available to members only; a login is required.

Join Here