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Eichhorn, H. ; Hammernik, K.* ; Spieker, V. ; Epp, S.M.* ; Rueckert, D.* ; Preibisch, C.* ; Schnabel, J.A.

Physics-Aware Motion Simulation For T2*-Weighted Brain MRI.

In: (Simulation and Synthesis in Medical Imaging). Berlin [u.a.]: Springer, 2023. 42-52 (Lect. Notes Comput. Sc. ; 14288)
DOI
In this work, we propose a realistic, physics-aware motion simulation procedure for T-2*-weighted magnetic resonance imaging (MRI) to improve learning-based motion correction. As T-2*-weighted MRI is highly sensitive to motion-related changes in magnetic field inhomogeneities, it is of utmost importance to include physics information in the simulation. Additionally, current motion simulations often only assume simplified motion patterns. Our simulations, on the other hand, include real recorded subject motion and realistic effects of motioninduced magnetic field inhomogeneity changes. We demonstrate the use of such simulated data by training a convolutional neural network to detect the presence of motion in affected k-space lines. The network accurately detects motion-affected k-space lines for simulated displacements down to >= 0.5mm (accuracy on test set: 92.5%). Finally, our results demonstrate exciting opportunities of simulation-based k-space line detection combined with more powerful reconstruction methods. Our code is publicly available at: https://github.com/HannahEichhorn/ T2starLineDet.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Brain MRI; Motion Artefacts; Motion Detection; Motion Correction; Deep Learning
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Simulation and Synthesis in Medical Imaging
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 14288, Heft: , Seiten: 42-52 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 10.1007/978-3-031-44689-4_5
WOS ID 001108272700005
Erfassungsdatum 2024-01-16
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