Motion-robust T 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ quantification from low-resolution gradient echo brain MRI with physics-informed deep learning.

Purpose: $T_2^{\ast }$ 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 $T_2^{\ast }$ maps.

Methods: We extend PHIMO, our previously introduced learning-based physics-informed motion correction method for low-resolution $T_2^{\ast }$ 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.

Results: 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 $T_2^{\ast }$ 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, makes it a promising solution for motion-robust $T_2^{\ast }$ quantification in research settings and clinical routine.