A multi-dynamic low-rank deep image prior for three-dimensional real-time cardiovascular magnetic resonance imaging.

PURPOSE: To develop a reconstruction framework for three-dimensional (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 sub-networks are jointly trained 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) 10 healthy subjects (including 2 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 two-dimensional [2D] real-time cine) and image quality (against 2D real-time cine and binning-based five-dimensional cine [5D-Cine]).

RESULTS: In the phantom study, ML-DIP achieved PSNR >29 dB and SSIM >0.90 for scan times as short as 2min, 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 variations 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 1000 by learning low-rank spatial and motion representations from undersampled data, without relying on external fully sampled training datasets.