PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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 as soon as Postprint is submitted to ZB.
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.
Altmetric
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Conference contribution
Keywords Self-Supervised Learning; Motion Detection; Data-Consistent Reconstruction; T2*Quantification; Gradient Echo MRI
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Medical Image Computing and Computer Assisted Intervention – MICCAI 2024
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 15007, Issue: , Pages: 562-571 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
Grants 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
[➜Report correction]