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Abstract #4861

Compressed Sensing Reconstruction in the Presence of a Reference Image

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.