Revisiting outer volume subtraction with deep-learning tools for highly-accelerated real-time cine CMR

Motivation: Real-time cine CMR provides a free-breathing ECG-free approach for heart function assessment. Nevertheless, commercially available real-time cine CMR methods without temporal regularization have limited acceleration and spatio-temporal resolutions. Goal(s): Use deep learning (DL) to remove extra-cardiac volume that aliases into the heart and improve acceleration rates for real-time cine CMR using only spatial regularization. Approach: We characterize pseudo-periodic ghosting artifacts arising from cardiac motion in time-interleaved sequences, then use DL to detect and remove them. This is followed by self-supervised physics-driven DL reconstruction. Results: Proposed technique effectively estimates and removes background signal, leading to substantial image quality improvement. Impact: We characterize and use deep learning (DL) to estimate pseudo-periodic ghosting artifacts arising from cardiac motion in time-interleaved real-time cine sequences. Background removal followed by physics-driven DL reconstruction substantially improves reconstruction at nominal R=8 for higher spatio-temporal resolution acquisitions.