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Abstract #2248

Improving Rank Constrained Reconstructions Using Prior Information with Reordering

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

1Radiology, University of Utah, Salt Lake City, UT, United States; 2Advanced MRI Technologies, Sebastopol, CA, United States

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.