1. A method of determining a k-space convolution kernel in Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) reconstruction of magnetic resonance imaging, comprising: acquiring plural frames of 2-D images or 3-D images at a same slice location (2-D) or slab location (3-D); acquiring k-space data of the plural frames of 2-D images or 3-D images; providing at least two sets of auto-calibration signal (ACS) lines from the acquired k-space data; utilizing a number of linear equations to estimate the k-space convolution kernel that is greater than a number of linear equations that could be derived from one set of ACS lines; and wherein the linear equations are a linear regression to at least two sets of ACS lines to estimate the k-space convolution kernel, wherein the linear regression is defined by the relationship: 
 AG=b, wherein an m×n matrix A is the input of the GRAPPA reconstruction or other k-space based reconstructions, wherein each row of the matrix A represents a sliding window in k-space, m is the number of reconstructed k-space points/sliding window, wherein G is a vectorized GRAPPA kernel with size n×1, and wherein b is a vectorized output of the GRAPPA reconstruction with size m×1.