Through time GRAPPA

摘要

Example apparatus and methods control a magnetic resonance imaging (MRI) apparatus to acquire, from an object to be imaged, throughout a period of time, a partitioned non-Cartesian fully-sampled calibration data set. Different groups of lines in the calibration data set are acquired at different points in time under different gradient encoding conditions that yield phase encoding in the direction perpendicular to the non-Cartesian encoded plane. The MRI apparatus is controlled to acquire an under-sampled non-Cartesian data set from the object to be imaged and to reconstruct an image from the under-sampled data set based, at least in part, on a through-time GRAPPA calibration. A GRAPPA weight set can be computed from data in different groups of lines in the calibration data set because different groups of lines can be treated as unique calibration time frames due to phase encoding produced by the different gradient encoding conditions.

主权项

1. A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a computer control the computer to perform a method, comprising:
controlling a magnetic resonance imaging (MRI) apparatus to acquire a calibration data set from an object to be imaged while first, second, and third encoding gradients are active, where the first and second encoding gradients define a non-Cartesian encoded plane, and where the third encoding grading is configured as an encoding gradient varying in a direction perpendicular to the non-Cartesian encoded plane in the object to be imaged, controlling the MRI apparatus to apply the first and second gradients to create the non-Cartesian encoded plane and to apply the third gradient while acquiring the calibration data set, controlling the MRI apparatus to vary the third encoding gradient in an amount sufficient to produce three or more differences in the encoding gradient in consecutive acquisitions; and controlling the MRI apparatus to perform a through-time generalized auto-calibrating partially parallel acquisition (GRAPPA) calibration, where performing the through-time GRAPPA calibration includes computing a GRAPPA weight set from data in the calibration data set, where computing the GRAPPA weight set is based, at least in part, on data acquired while the first and second gradients are being applied and where data acquired with different third gradients comprise unique calibration frames.