# Deep-Learning based Motion Correction for Myocardial T1 Mapping

arXiv: https://arxiv.org/abs/2109.09146 · HTML: https://mlsanity.com/paper/2109.09146/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2021.

## Built on by (most cited first, 4 of 4)

- [Deep-learning-based groupwise registration for motion correction of cardiac T1 mapping](https://mlsanity.com/paper/2406.12456/) (2024)
- [MBSS-T1: Model-Based Subject-Specific Self-Supervised Motion Correction for Robust Cardiac T1 Mapping](https://mlsanity.com/paper/2408.11992/) (2024)
- [Groupwise Registration with Physics-Informed Test-Time Adaptation on Multi-parametric Cardiac MRI](https://mlsanity.com/paper/2510.26022/) (2025)
- [Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI](https://mlsanity.com/paper/2605.10571/) (2026)

## Abstract (arXiv)

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).