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as soon as is submitted to ZB.
Physics-informed deep learning for motion-corrected reconstruction of quantitative brain MRI.
In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). Berlin [u.a.]: Springer, 2024. 562-571 (Lect. Notes Comput. Sc. ; 15007)
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.
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Publication type
Article: Conference contribution
Document type
Scientific Article
Keywords
Self-Supervised Learning; Motion Detection; Data-Consistent Reconstruction; T2*Quantification; Gradient Echo MRI
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Quellenangaben
Volume: 15007,
Pages: 562-571
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)
Grants
Helmholtz Association under the joint research school "Munich School for Data Science - MUDS"