Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning
Journal:
arXiv
Published Date:
Feb 24, 2025
Abstract
Purpose: T2* quantification from gradient echo magnetic resonance imaging is
particularly affected by subject motion due to the 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.
Methods: We extend our previously introduced learning-based physics-informed
motion correction method, PHIMO, by utilizing 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.
Results: Our extended version of 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.
Conclusion: PHIMO's competitive motion correction performance, combined with
a reduction in acquisition time by over 40% compared to the state-of-the-art
method, make it a promising solution for motion-robust T2* quantification in
research settings and clinical routine.
