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.