Deep-Learning based Motion Correction for Myocardial T1 Mapping

TL;DR

This paper introduces a deep-learning system for correcting motion artifacts in myocardial T1 mapping MRI images, significantly improving the accuracy of T1 map estimation.

Contribution

The paper presents a novel deep-learning based motion correction method specifically designed for myocardial T1 mapping in cardiac MRI.

Findings

Significant improvement in model fitting regression R2 (0.52 vs 0.29) with motion correction.

Deep learning-based correction outperforms traditional methods.

Enhances the accuracy of myocardial fibrosis assessment.

Abstract

Myocardial T1 mapping is a cardiac MRI technique, used to assess myocardial fibrosis. In this technique, a series of T1-weighted MRI images are acquired with different saturation or inversion times. These images are fitted to the T1 model to estimate the model parameters and construct the desired T1 maps. In the presence of motion, the different T1-weighted images are not aligned. This, in turn, will cause errors and inaccuracies in the final estimation of the T1 maps. Therefore, motion correction is a necessary process for myocardial T1 mapping. We present a deep-learning (DL) based system for cardiac T1-weighted MRI images motion correction. When applying our DL-based motion correction system we achieve a statistically significant improved performance by means of R2 of the model fitting regression, in compared to the model fitting regression without motion correction (0.52 vs 0.29, p<0.05).