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Eichhorn, H. ; Spieker, V. ; Hammernik, K.* ; Saks, E.* ; Weiss, K.* ; Preibisch, C.* ; Schnabel, J.A.

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)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Self-Supervised Learning; Motion Detection; Data-Consistent Reconstruction; T2*Quantification; Gradient Echo MRI
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15007, Heft: , Seiten: 562-571 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
Förderungen Helmholtz Association under the joint research school "Munich School for Data Science - MUDS"
DOI 10.1007/978-3-031-72104-5_54
WOS ID 001342232700054
Scopus ID 85212526361
Erfassungsdatum 2024-12-09
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