Improving GRAPPA using cross-sampled autocalibration data

In conventional generalized autocalibrating partially parallel acquisitions, the autocalibration signal (ACS) lines are acquired with a frequency-encoding direction in parallel to other undersampled lines. In this study, a cross sampling method is proposed to acquire the ACS lines orthogonal to the undersampled lines. This cross sampling method increases the amount of calibration data along the direction, where k-space is undersampled, and especially improves the calibration accuracy when a small number of ACS lines are acquired. The cross sampling method is implemented with swapped frequency and phase encoding gradients. In addition, an iterative coregistration method is also developed to correct the inconsistency between the ACS and undersampled data, which are acquired separately in two orthogonal directions. The same calibration and reconstruction procedure as conventional generalized autocalibrating partially parallel acquisitions is then applied to the corrected data to recover the unacquired k-space data and obtain the final image. Reconstruction results from simulations, phantom and in vivo human brain experiments have distinctly demonstrated that the proposed method, named cross-sampled generalized autocalibrating partially parallel acquisitions, can effectively reduce the aliasing artifacts of conventional generalized autocalibrating partially parallel acquisitions when very few ACS lines are acquired, especially at high outer k-space reduction factors.