A multi-dynamic low-rank deep image prior (ML-DIP) for 3D real-time cardiovascular MRI
Chen C, Vornehm M, Bu Z, Chandrasekaran P, Sultan MA, Arshad SM, Liu Y, Han Y, Ahmad R (2025)
Publication Type: Journal article
Publication year: 2025
Journal
Pages Range: 102015
Article Number: 102015
DOI: 10.1016/j.jocmr.2025.102015
Abstract
Purpose
To develop a reconstruction framework for 3D real-time cine cardiovascular magnetic resonance (CMR) from highly undersampled data without requiring fully sampled training datasets.
Methods
We developed a multi-dynamic low-rank deep image prior (ML-DIP) framework that models spatial image content and deformation fields using separate neural networks. These networks are optimized per scan to reconstruct the dynamic image series directly from undersampled k-space data. ML-DIP was evaluated on (i) a 3D cine digital phantom with simulated premature ventricular contractions (PVCs), (ii) ten healthy subjects (including two scanned during both rest and exercise), and (iii) 12 patients with a history of PVCs. Phantom results were assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). In vivo performance was evaluated by comparing left-ventricular function quantification (against 2D real-time cine) and image quality (against 2D real-time cine and binning-based 5D-Cine).
Results
In the phantom study, ML-DIP achieved PSNR > 29 dB and SSIM > 0.90 for scan times as short as two minutes, while recovering cardiac motion, respiratory motion, and PVC events. In healthy subjects, ML-DIP yielded functional measurements comparable to 2D cine and higher image quality than 5D-Cine, including during exercise with high heart rates and bulk motion. In PVC patients, ML-DIP preserved beat-to-beat variability and reconstructed irregular beats, whereas 5D-Cine showed motion artifacts and information loss due to binning.
Conclusion
ML-DIP enables high-quality 3D real-time CMR with acceleration factors exceeding 1, 000 by learning low-rank spatial and motion representations from undersampled data, without relying on external fully sampled training datasets.
Authors with CRIS profile
Involved external institutions
United States (USA) (US)How to cite
APA:
Chen, C., Vornehm, M., Bu, Z., Chandrasekaran, P., Sultan, M.A., Arshad, S.M.,... Ahmad, R. (2025). A multi-dynamic low-rank deep image prior (ML-DIP) for 3D real-time cardiovascular MRI. Journal of Cardiovascular Magnetic Resonance, 102015. https://doi.org/10.1016/j.jocmr.2025.102015
MLA:
Chen, Chong, et al. "A multi-dynamic low-rank deep image prior (ML-DIP) for 3D real-time cardiovascular MRI." Journal of Cardiovascular Magnetic Resonance (2025): 102015.
BibTeX: Download