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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).
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Cardiac Mri ; Deep Learning ; Image Reconstruction ; Implicit Neural Representations ; Low-rank ; Non-cartesian Sampling
Sprache englisch
Veröffentlichungsjahr 2026
HGF-Berichtsjahr 2026
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 29th International Conference on Information Processing in Medical Imaging, IPMI 2025
Konferzenzdatum 25-30 May 2025
Konferenzort Kos
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15829 LNCS, Heft: , Seiten: 168-183 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
DOI 10.1007/978-3-031-96628-6_12
Scopus ID 105014494351
Erfassungsdatum 2025-10-22
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