  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).
