- Workshop presentation
- Open Access
PLR-TV: patch-based low rank with spatio-temporal total variation constraints for ungated myocardial perfusion CMR
© Adluru and DiBella; licensee BioMed Central Ltd. 2014
- Published: 16 January 2014
- Circular Patch
- Ungated Image
- Saturation Recovery Sequence
- Total Variation Reconstruction
- Total Variation Constraint
Ungated myocardial perfusion is a promising alternative for simplifying CMR protocols . Spatio-temporal total variation (TV) constrained reconstruction with radial undersampling was used in a pilot study . And TV constraints combined with a low-rank constraint have shown improvement in some cases for gated perfusion imaging . 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.
PLR-TV is a promising approach for reconstructing undersampled radial ungated data. While the PLR constraint exploits the low rank property effectively in the ungated images, TV constraints can help preserve any contrast loss from the PLR, making the hybrid method more effective than either constraint alone in the presence of motion. Here we chose overlapping circular patches over time (so that the Casorati matrix is almost square), although arbitrary shaped patches can be chosen depending on underlying geometrical structures. While adding PLR constraints increases the reconstruction time, we limited the amount of overlapping of the circular patches so as to cover all of the pixels in the image for the PLR constraint. Randomized patch updates and the use of TV reconstructions as initial estimates for the final PLR-TV reconstructions can reduce the computational cost.
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