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Abstract #4892

Coarse-To-Fine Iterative Reweighted L1-Norm Compressed Sensing for Dynamic Imaging

Michael Lustig1,2, Julia Velikina3, Alexey Samsonov3, Chuck Mistretta3,4, John Mark Pauly2, Michael Elad5

1Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, United States; 2Electrical Engineering, Stanford University, Stanford, CA, United States; 3Medical Physics, University of Wisconsin-Madison, Madison, WI, United States; 4Radiology, University of Wisconsin-Madison, Madison, WI, United States; 5Computer Science, Technion IIT, Haifa, Israel

A coarse-to-fine compressed sensing (CS) reconstruction for dynamic imaging is presented. It is inspired by the composite image constraint in HYPR-like processing. At each temporal scale, a composite image is reconstructed using a CS reconstruction. The result is used as an initial image for the next finer scale. In addition it is used to generate weighting of the l1-norm in the CS reconstruction, promoting sparsity at locations that appear in the composite. Reconstruction from highly undersampled DCE-MRA is demonstrated.