Meeting Banner
Abstract #2219

Combining Compressed Sensing and Nonlinear Grappa for Highly Accelerated Parallel MRI

Yuchou Chang1, Kevin F. King2, Dong Liang3, Leslie Ying1

1Electrical Engineering, University of Wisconsin - Milwaukee, Milwaukee, WI, United States; 2Global Applied Science Laboratory, GE Healthcare, Waukesha, WI, United States; 3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

CS-GRAPPA has the benefit of decoupling CS and GRAPPA without the need for coil sensitivities. However, noise and errors from the CS step can propagate and be amplified in GRAPPA. We recently developed a nonlinear GRAPPA (NLGRAPPA) approach that can suppress the GRAPPA noise significantly. In this work, we propose to integrate CS and NLGRAPPA to improve CS-GRAPPA reconstruction. The NLGRAPPA step can reduce the amplification of noise and errors in CS reconstruction. Experimental results using phantom and in vivo data demonstrate that the proposed method can significantly improve the reconstruction quality over CS-GRAPPA at high net reduction factors.

Abstract PDF Poster PDF

Keywords

academy accelerate accelerated acceleration acquisition advanced aliased aliasing already among amplification amplified applied around artifacts auto bandwidth benefit brain calibration cardiac carries central channel channels china cine coefficients coil combination combine combined combines combining compressed consistency constant constraint dataset decoupling degree denotes density developed divided dong electrical employed encoding enforce engineering errors evaluated even experimental fast final full fully global gradient highly improve in vivo institutes integrate king laboratory locations matrix named need noise nonlinear novel outer parallel part parts pattern people phantom pixel print produces propagate propose proposed quality random randomly readout recently reconstruct reconstructed reconstructing reconstruction reconstructions reduce reduced reduction represent republic respectively rest sampled sampling scheme science sciences sensing sensitivities sequentially sets several significantly slice solve space sparseness specifically speed spin step superior suppress suppressing technology term terms theory transformed ultimate uniformly union unknown variable vector white