  We propose PHIMO, a physics-informed learning-based motion correction method
tailored to quantitative MRI. PHIMO leverages information from the signal
evolution to exclude motion-corrupted k-space lines from a data-consistent
reconstruction. We demonstrate the potential of PHIMO for the application of
T2* quantification from gradient echo MRI, which is particularly sensitive to
motion due to its sensitivity to magnetic field inhomogeneities. A
state-of-the-art technique for motion correction requires redundant acquisition
of the k-space center, prolonging the acquisition. We show that PHIMO can
detect and exclude intra-scan motion events and, thus, correct for severe
motion artifacts. PHIMO approaches the performance of the state-of-the-art
motion correction method, while substantially reducing the acquisition time by
over 40%, facilitating clinical applicability. Our code is available at
https://github.com/HannahEichhorn/PHIMO.
