bibtext: @InProceedings{ Eic_PhysicsInformed_MICCAI2024,
                   author = { Eichhorn, Hannah and Spieker, Veronika and Hammernik, Kerstin and Saks, Elisa and Weiss, Kilian and Preibisch, Christine and Schnabel, Julia A. },
                   title = { { Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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/compai-lab/2024-miccai-eichhorn.
title: Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI
authors: Eichhorn, Hannah Spieker, Veronika Hammernik, Kerstin Saks, Elisa Weiss, Kilian Preibisch, Christine Schnabel, Julia A.