Deep-Learning based Motion Correction for Myocardial T1 Mapping

Myocardial T
1
mapping is a cardiac MRI technique, used to assess myocardial fibrosis. In this technique, a series of T
1
-weighted MRI images are acquired with different saturation or inversion times. These images are fitted to the T
1
model to estimate the model parameters and construct the desired T
1
maps. In the presence of motion, the different T
1
-weighted images are not aligned. This, in turn, will cause errors and inaccuracies in the final estimation of the T
1
maps. Therefore, motion correction is a necessary process for myocardial T
1
mapping. We present a deep-learning (DL) based system for cardiac T
1
weighted MRI images motion correction. When applying our DL-based motion correction system we achieve a statistically significant improved performance by means of R
²
of the model fitting regression, in compared to the model fitting regression without motion correction (0.52 vs 0.29, p<0.05).