Method for estimating a GRAPPA reconstruction kernel

摘要

A method for improving the signal-to-noise ratio (SNR) of TGRAPPA. The SNR of the ACS lines is proportional to the condition number of the GRAPPA kernel encoding equations. Therefore, the GRAPPA kernel estimated from higher SNR ACS lines amplifies the random noise in GRAPPA reconstruction. In TGRAPPA reconstruction of dynamic image series, a widely used method to acquire ACS lines is to average-all-frame (AAF). The present disclosure utilizes a tile-all-frame (TAF) as ACS lines to improve the SNR of the reconstructed images.

主权项

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.