# MBSS-T1: Model-Based Subject-Specific Self-Supervised Motion Correction for Robust Cardiac T1 Mapping

arXiv: https://arxiv.org/abs/2408.11992 · HTML: https://mlsanity.com/paper/2408.11992/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [SynthMorph: learning contrast-invariant registration without acquired images](https://mlsanity.com/paper/2004.10282/) (2020)
- [Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences](https://mlsanity.com/paper/1806.04066/) (2018)
- [Memorization in deep learning: A survey](https://mlsanity.com/paper/2406.03880/) (2024)
- [LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging](https://mlsanity.com/paper/2107.09060/) (2021)
- [Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking](https://mlsanity.com/paper/1812.01859/) (2018)
- [PCMC-T1: Free-breathing myocardial T1 mapping with Physically-Constrained Motion Correction](https://mlsanity.com/paper/2308.11281/) (2023)
- [Deep-Learning based Motion Correction for Myocardial T1 Mapping](https://mlsanity.com/paper/2109.09146/) (2021)

## Built on by (most cited first, 2 of 2)

- [On Inverse Problems, Parameter Estimation, and Domain Generalization](https://mlsanity.com/paper/2506.06024/) (2025)
- [Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI](https://mlsanity.com/paper/2605.10571/) (2026)

## Abstract (arXiv)

Cardiac T1 mapping is a valuable quantitative MRI technique for diagnosing diffuse myocardial diseases. Traditional methods, relying on breath-hold sequences and cardiac triggering based on an ECG signal, face challenges with patient compliance, limiting their effectiveness. Image registration can enable motion-robust cardiac T1 mapping, but inherent intensity differences between time points pose a challenge. We present MBSS-T1, a subject-specific self-supervised model for motion correction in cardiac T1 mapping. Physical constraints, implemented through a loss function comparing synthesized and motion-corrected images, enforce signal decay behavior, while anatomical constraints, applied via a Dice loss, ensure realistic deformations. The unique combination of these constraints results in motion-robust cardiac T1 mapping along the longitudinal relaxation axis. In a 5-fold experiment on a public dataset of 210 patients (STONE sequence) and an internal dataset of 19 patients (MOLLI sequence), MBSS-T1 outperformed baseline deep-learning registration methods. It achieved superior model fitting quality (R2: 0.975 vs. 0.941, 0.946 for STONE; 0.987 vs. 0.982, 0.965 for MOLLI free-breathing; 0.994 vs. 0.993, 0.991 for MOLLI breath-hold), anatomical alignment (Dice: 0.89 vs. 0.84, 0.88 for STONE; 0.963 vs. 0.919, 0.851 for MOLLI free-breathing; 0.954 vs. 0.924, 0.871 for MOLLI breath-hold), and visual quality (4.33 vs. 3.38, 3.66 for STONE; 4.1 vs. 3.5, 3.28 for MOLLI free-breathing; 3.79 vs. 3.15, 2.84 for MOLLI breath-hold). MBSS-T1 enables motion-robust T1 mapping for broader patient populations, overcoming challenges such as suboptimal compliance, and facilitates free-breathing cardiac T1 mapping without requiring large annotated datasets. Our code is available at https://github.com/TechnionComputationalMRILab/MBSS-T1.