Fan Lam1, Diego Hernando1, Kevin
F. King2, Zhi-Pei Liang1
1Electrical and Computer Engineering,
University of Illinois at Urbana-Champaign, Urbana, IL, United States; 2Global
Applied Science Lab, GE Healthcare, Waukesha, WI, United States
In
this work, we are addressing the problem on improving compressed sensing
reconstruction in the presence of a reference image. A novel algorithm is
developped to generate a motion compensated reference image to further
improve signal sparsity for a difference image between the reference and the
target image to be reconstructed. A compressed sensing reconstruction scheme
is proposed to reconstruct the difference image and then the overal
reconstruction is constructed by adding the difference image with the
reference. The final reconstruction outperforms conventional CS-based
reconstruction. The comparison is shown for an interventional imaging
experiment.
Keywords