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.