Keywords: AI/ML Image Reconstruction, Image Reconstruction, Inverse Problems, Deep learning, Low-rank, Cardiac MRI, Radial sampling
Motivation: Physics-guided self-supervised approaches have proven to be useful in MR image reconstruction from limited Cartesian measurements. However, the potential of radially-sampled k-space data remains largely unexplored.
Goal(s): In this context, we introduce a self-supervised learning approach to reconstruct dynamic images from sparsely-sampled radial cardiac data.
Approach: The proposed model integrates a novel low-rank and sparse regularizer in its iterative framework to better exploit the characteristics of dynamic images.
Results: Our method is compared to iterative reconstruction techniques and other deep neural network approaches in supervised and self-supervised tasks, where the proposed model achieves the best performance for a single and four heartbeat reconstruction.
Impact: Self-supervised models for radially sampled cardiac measurements can now be efficiently trained on limited amounts of data to reliably reconstruct high-contrast and low artifact dynamic MR images, even at high acceleration rates for faster acquisition speed.
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