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Spieker, V. ; Eichhorn, H. ; Stelter, J.K.* ; Huang, W.* ; Braren, R.F.* ; Rueckert, D.* ; Sahli Costabal, F.* ; Hammernik, K.* ; Prieto, C.* ; Karampinos, D.C.* ; Schnabel, J.A.

Self-supervised k-space regularization for motion-resolved abdominal MRI using neural implicit k-space representations.

In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024). Berlin [u.a.]: Springer, 2024. 614-624 (Lect. Notes Comput. Sc. ; 15007 LNCS)
DOI
Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application of common image regularization methods to improve the final reconstruction. In this work, we introduce the concept of parallel imaging-inspired self-consistency (PISCO), which we incorporate as novel self-supervised k-space regularization enforcing a consistent neighborhood relationship. At no additional data cost, the proposed regularization significantly improves neural implicit k-space reconstructions on simulated data. Abdominal in-vivo reconstructions using PISCO result in enhanced spatio-temporal image quality compared to state-of-the-art methods. Code is available at https://github.com/compai-lab/2024-miccai-spieker.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Dynamic Mri Reconstruction ; Implicit Neural Representations ; K-space Refinement ; Parallel Imaging ; Self-supervised Learning
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 LNCS, Heft: , Seiten: 614-624 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)