Comparative analysis of the kernel types in generalized autocalibrating partially parallel acquisition algorithms for accelerated three-dimensional magnetic resonance spectroscopic imaging data

Abstract

This study was conducted to comparatively analyze the performance of generalized autocalibrating partially parallel acquisition (GRAPPA) algorithms according to the kernel types, which has not yet been reported. A GRAPPA algorithm using one 3D kernel or other kernels was used to reconstruct the undersampled 3D MRSI data along three or two PE directions. To quantitatively assess the quality of the reconstruction, the normalized root-mean-squared errors (nRMSEs) were calculated in simulated, phantom, and in vivo data. The nRMSE of the GRAPPA algorithm using one 3D kernel in reconstructing undersampled 3D MRSI data along three PE directions was the lowest. The spectra and maps from the reconstructed data using one 3D kernel were the closest to those from the fully sampled data. We recommend acquiring 3D MRSI data accelerated along three PE directions to reduce scan time and indispensably applying the GRAPPA algorithm using one 3Dkernel to improve reconstruction performance.