PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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.
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 Dynamic Mri Reconstruction ; Implicit Neural Representations ; K-space Refinement ; Parallel Imaging ; Self-supervised Learning
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 LNCS, Issue: , Pages: 614-624 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
DOI 10.1007/978-3-031-72104-5_59
Scopus ID 85212521047
Erfassungsdatum 2025-01-10
[➜Report correction]