# Motion‐robust T2∗ quantification from low‐resolution gradient echo brain MRI with physics‐informed deep learning

## TL;DR

This paper introduces PHIMO+, a physics-informed deep learning method that improves motion-robust T2∗ quantification in brain MRI, reducing acquisition time while maintaining accuracy.

## Contribution

PHIMO+ enhances motion correction for T2∗ mapping using physics-informed deep learning and acquisition knowledge, achieving better performance and faster acquisition.

## Key findings

- PHIMO+ outperforms learning-based baselines in motion detection and image quality.
- PHIMO+ matches conventional methods in accuracy while reducing acquisition time by over 40%.
- The method is robust to varying magnetic field inhomogeneities and challenging motion patterns.

## Abstract

T2∗ quantification from gradient echo magnetic resonance imaging is particularly affected by subject motion due to its high sensitivity to magnetic field inhomogeneities, which are influenced by motion and might cause signal loss. Thus, motion correction is crucial to obtain high‐quality T2∗ maps.

We extend PHIMO, our previously introduced learning‐based physics‐informed motion correction method for low‐resolution T2∗ mapping. Our extended version, PHIMO+, utilizes acquisition knowledge to enhance the reconstruction performance for challenging motion patterns and increase PHIMO's robustness to varying strengths of magnetic field inhomogeneities across the brain. We perform comprehensive evaluations regarding motion detection accuracy and image quality for data with simulated and real motion.

PHIMO+ outperforms the learning‐based baseline methods both qualitatively and quantitatively with respect to line detection and image quality. Moreover, PHIMO+ performs on par with a conventional state‐of‐the‐art motion correction method for T2∗ quantification from gradient echo MRI, which relies on redundant data acquisition.

PHIMO+'s competitive motion correction performance, combined with a reduction in acquisition time by over 40% compared to the state‐of‐the‐art method, makes it a promising solution for motion‐robust T2∗ quantification in research settings and clinical routine.