Optimal auto-calibration kernel estimation using double adaptive weights

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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