PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Huang, W.* ; Spieker, V. ; Xu, S.* ; Cruz, G.* ; Prieto, C.* ; Schnabel, J.A. ; Hammernik, K.* ; Kuestner, T.* ; Rueckert, D.*

Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging.

In: (29th International Conference on Information Processing in Medical Imaging, IPMI 2025, 25-30 May 2025, Kos). Berlin [u.a.]: Springer, 2026. 168-183 (Lect. Notes Comput. Sc. ; 15829 LNCS)
DOI
Conventional cardiac cine MRI methods rely on retrospective gating, which limits temporal resolution and the ability to capture continuous cardiac dynamics, particularly in patients with arrhythmias and beat-to-beat variations. To address these challenges, we propose a reconstruction framework based on subspace implicit neural representations for real-time cardiac cine MRI of continuously sampled radial data. This approach employs two multilayer perceptrons to learn spatial and temporal subspace bases, leveraging the low-rank properties of cardiac cine MRI. Initialized with low-resolution reconstructions, the networks are fine-tuned using spoke-specific loss functions to recover spatial details and temporal fidelity. Our method directly utilizes the continuously sampled radial k-space spokes during training, thereby eliminating the need for binning and non-uniform FFT. This approach achieves superior spatial and temporal image quality compared to conventional binned methods at the acceleration rate of 10 and 20, demonstrating potential for high-resolution imaging of dynamic cardiac events and enhancing diagnostic capability (Code available: https://github.com/wenqihuang/SubspaceINR-CMR).
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 Cardiac Mri ; Deep Learning ; Image Reconstruction ; Implicit Neural Representations ; Low-rank ; Non-cartesian Sampling
Language english
Publication Year 2026
HGF-reported in Year 2026
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title 29th International Conference on Information Processing in Medical Imaging, IPMI 2025
Conference Date 25-30 May 2025
Conference Location Kos
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 15829 LNCS, Issue: , Pages: 168-183 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30505 - New Technologies for Biomedical Discoveries
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
DOI 10.1007/978-3-031-96628-6_12
Scopus ID 105014494351
Erfassungsdatum 2025-10-22
[➜Report correction]