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Abstract #4883

l1-Denoised Autocalibrating Parallel Imaging

Tao Zhang1, Michael Lustig1,2, Shreyas Vasanawala3, John Mark Pauly1

1Electrical Engineering, Stanford University, Stanford, CA, United States; 2Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA, United States; 3Radiology, Stanford University, Stanford, CA, United States

In this study, sequential parallel imaging and compressed sensing (CS) are applied to suppress noise and improve image quality. A noise covariance matrix constructed from the GRAPPA interpolation kernels are used to "intelligently inform" the CS optimization about the confidence level of each GRAPPA reconstructed entry. The experiment results show that the proposed method can efficiently suppress noise.