# Physics-Aware Motion Simulation for T2*-Weighted Brain MRI

arXiv: https://arxiv.org/abs/2303.10987 · HTML: https://mlsanity.com/paper/2303.10987/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2023.

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

- [Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review](https://mlsanity.com/paper/2305.06739/) (2023)
- [IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided Implicit Neural Representations](https://mlsanity.com/paper/2407.02974/) (2024)
- [Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction](https://mlsanity.com/paper/2509.05071/) (2025)
- [Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning](https://mlsanity.com/paper/2502.17209/) (2025)

## Abstract (arXiv)

In this work, we propose a realistic, physics-aware motion simulation procedure for T2*-weighted magnetic resonance imaging (MRI) to improve learning-based motion correction. As T2*-weighted MRI is highly sensitive to motion-related changes in magnetic field inhomogeneities, it is of utmost importance to include physics information in the simulation. Additionally, current motion simulations often only assume simplified motion patterns. Our simulations, on the other hand, include real recorded subject motion and realistic effects of motion-induced magnetic field inhomogeneity changes. We demonstrate the use of such simulated data by training a convolutional neural network to detect the presence of motion in affected k-space lines. The network accurately detects motion-affected k-space lines for simulated displacements down to ≥ 0.5mm (accuracy on test set: 92.5%). Finally, our results demonstrate exciting opportunities of simulation-based k-space line detection combined with more powerful reconstruction methods.