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Abstract #2874

Optimally Regularized GRAPPA/GROWL with Experimental Verifications

Wei Lin1, Feng Huang1, Hu Cheng2, Yu Li1, Arne Reykowski1

1Advanced Concepts Development, Invivo Corporation, Philips Healthcare, Gainesville, FL, United States; 2Indiana University, Bloomington, IN, United States

The performance of GRAPPA-based parallel imaging methods can suffer when the size of the auto-calibration signal (ACS) region becomes small. Based on an analysis of condition number for GRAPPA calibration equation, an optimal Tikhonov regularization factor is proposed to improve the quality of image reconstruction. Alternatively, an optimal amount of noise can be added to the ACS data to stabilize the system. The technique was applied to both GRAPPA and GRAPPA operator for wider radial bands (GROWL), a self-calibrated radial parallel imaging methods. Results show that minimal reconstruction errors are always obtained with the proposed automatic regularization scheme.