Improving Rank Constrained Reconstructions Using Prior Information with Reordering

Ganesh Adluru¹, Liyong Chen¹, ², David Feinberg², Jeffrey Anderson¹, Edward V.R. DiBella¹

Image reconstruction using a rank penalty term is a promising way to remove undersampling artifacts in multi-image MRI. Exciting results have been reported in dynamic imaging situations where temporal signal changes are highly correlated. However, when the underlying true data have a lot of variation, a low rank constraint may not be the best choice. Here we propose a reordering technique to improve rank constrained reconstructions in such cases. Pixel intensities in the matrix of the multi-image estimate are reordered based on the sorting order of a prior. This results in a better match with the low rank model. Promising results are presented on undersampled multi-image diffusion data.