PLR-TV: patch-based low rank with spatio-temporal total variation constraints for ungated myocardial perfusion CMR

Background Ungated myocardial perfusion is a promising alternative for simplifying CMR protocols [1]. Spatio-temporal total variation (TV) constrained reconstruction with radial undersampling was used in a pilot study [2]. And TV constraints combined with a low-rank constraint have shown improvement in some cases for gated perfusion imaging [3]. However, the temporal total-variation constraint may not effectively resolve undersampling artifacts in ungated studies with significant cardiac and respiratory inter-frame motion. A patch-based low-rank method can more effectively exploit redundancies in a dynamic dataset in the form of spatio-temporal patches rather than one large rectangular matrix with an entire image per column. The patch-based method (also termed locally low rank or blockwise low rank) was recently shown to improve upon the standard low-rank constraint in cardiac cine and perfusion imaging [4,5]. Here we adapt a patch-based low-rank method for ungated myocardial perfusion imaging and use it in conjunction with TV constraints.