Abstract #4392
Optimal auto-calibration kernel estimation using double adaptive weights
Enhao Gong 1 and John M Pauly 1
1
Electrical Engineering, Stanford University,
Stanford, CA, United States
The estimation of GRAPPA and SPIRiT auto-calibration
kernel, which is usually formed as an inverse problem,
is an essential step for Parallel Imaging (PI).
Regularizations for the kernel coefficients have been
discussed before to achieve more accurate kernel
estimation. However, the weighting for each measurement
in the inverse problem has not been fully investigated.
In this work, we propose a novel scheme for
auto-calibration PI, which consider both measurement and
kernel coefficients to achieve an optimal solution under
a statistical model. Experiments compared with previous
proposed calibration methods demonstrated advantages in
kernel value estimation and reconstruction accuracy.
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