# PCMC-T1: Free-breathing myocardial T1 mapping with Physically-Constrained Motion Correction

arXiv: https://arxiv.org/abs/2308.11281 · HTML: https://mlsanity.com/paper/2308.11281/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [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)

## Built on by (most cited first, 6 of 6)

- [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)
- [CardioSpectrum: Comprehensive Myocardium Motion Analysis with 3D Deep Learning and Geometric Insights](https://mlsanity.com/paper/2407.03794/) (2024)
- [Systole-Conditioned Generative Cardiac Motion](https://mlsanity.com/paper/2507.15894/) (2025)
- [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)

T1 mapping is a quantitative magnetic resonance imaging (qMRI) technique that has emerged as a valuable tool in the diagnosis of diffuse myocardial diseases. However, prevailing approaches have relied heavily on breath-hold sequences to eliminate respiratory motion artifacts. This limitation hinders accessibility and effectiveness for patients who cannot tolerate breath-holding. Image registration can be used to enable free-breathing T1 mapping. Yet, inherent intensity differences between the different time points make the registration task challenging. We introduce PCMC-T1, a physically-constrained deep-learning model for motion correction in free-breathing T1 mapping. We incorporate the signal decay model into the network architecture to encourage physically-plausible deformations along the longitudinal relaxation axis. We compared PCMC-T1 to baseline deep-learning-based image registration approaches using a 5-fold experimental setup on a publicly available dataset of 210 patients. PCMC-T1 demonstrated superior model fitting quality (R2: 0.955) and achieved the highest clinical impact (clinical score: 3.93) compared to baseline methods (0.941, 0.946 and 3.34, 3.62 respectively). Anatomical alignment results were comparable (Dice score: 0.9835 vs. 0.984, 0.988). Our code and trained models are available at https://github.com/eyalhana/PCMC-T1.