Highly Accelerated Cartesian Real-Time Cine CMR Using Subject-Specific Zero-Shot Deep Learning Reconstruction

Real-time cine CMR is an ECG-free free-breathing alternative for functionally assessing the heart. To achieve sufficient spatio-temporal resolutions, these require rapid imaging, e.g. compressed sensing (CS) with radial trajectories. However, at high accelerations, CS may suffer from residual aliasing and temporal blurring. Recently, deep learning (DL) reconstruction has gained immense interest for fast MRI. Yet, for free-breathing real-time cine, where subjects have different breathing and cardiac motion patterns, database learning of spatiotemporal correlations has been difficult. Here, we propose a physics-guided DL reconstruction trained in a subject-specific manner. Proposed method improves image quality compared to database-trained DL and conventional methods.