Temporal Structured Low-Rank Reconstruction for First-Pass Myocardial Perfusion Imaging

Motivation: First-pass myocardial perfusion imaging is a powerful tool for assessing coronary artery disease, but needs high levels of undersampling to achieve sufficient spatial coverage, spatiotemporal resolution, and motion robustness. Goal(s): To develop efficient temporal image reconstruction models which can leverage linear time-invariant models of dynamic contrast enhancement without identifying an arterial input function or assuming tissue transfer function shapes. Approach: We propose a novel temporal structured low-rank modeling technique to implicitly leverage linear time-invariant models of dynamic contrast enhancement. Results: Temporal structured low-rank modeling outperforms conventional low-rank methods, especially as a local constraint. Impact: Temporal structured low-rank modeling has the potential to improve spatial coverage, spatial resolution, and/or motion robustness for first-pass myocardial perfusion MRI.