Abstract #0391

PHIMO: Physics-Informed Motion Correction of GRE MRI for T2* Quantification

Hannah Eichhorn^1,2, Kerstin Hammernik², Veronika Spieker^1,2, Elisa Saks^3,4, Kilian Weiss⁵, Christine Preibisch^3,4,6, and Julia A. Schnabel^1,2,7

¹Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany, ²School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, ³School of Medicine and Health, Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany, ⁴School of Medicine and Health, TUM-Neuroimaging Center, Technical University of Munich, Munich, Germany, ⁵Philips GmbH Market DACH, Hamburg, Germany, ⁶School of Medicine and Health, Clinic for Neurology, Technical University of Munich, Munich, Germany, ⁷Biomedical Engineering Department, School of Biomedical Imaging and Imaging Sciences, King’s College London, London, United Kingdom

Synopsis

Keywords: Motion Correction, Quantitative Imaging, Motion Correction, Deep Learning, Brain

Motivation: T2* quantification from GRE-MRI is particularly impacted by subject motion due to its sensitivity to magnetic field inhomogeneities. The current multi-parametric quantitative BOLD motion correction method depends on additional k-space acquisition, extending overall acquisition times.

Goal(s): To develop a learning-based motion correction method tailored to T2* quantification that avoids redundant data acquisition.

Approach: PHIMO leverages multi-echo T2* decay information to identify motion-corrupted k-space lines and excludes them from a data-consistent deep learning reconstruction.

Results: We are able to correct motion artifacts in subjects with stronger motion, approaching the performance of the current motion correction method, while substantially reducing the acquisition time.

Impact: PHIMO reduces strong motion artifacts in T2* maps by utilizing T2* decay information in an unrolled DL reconstruction. PHIMO avoids redundant data acquisition compared to a current correction method and reduces the acquisition time by over 40%, facilitating clinical applicability.

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