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.