Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA)
Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA)
复制标题
DOI：
10.1002/mrm.10171
复制
发表时间：
2002-06-01
影响因子：
3.3
通讯作者：
Haase, A
中科院分区：
文献类型：
--
作者：
Griswold, MA;Jakob, PM;Haase, A
在这项研究中，提出了一种新型的部分并行采集方法（PPA）方法，该方法可用于使用RF线圈阵列进行空间编码来加速图像采集。这种技术，广义自动校准的部分并行采集（Grappa）是PILS和VD-AUTO-SMASH重建技术的扩展。与以前的方法一样，在Grappa重建之前，不需要详细的，高度准确的RF现场图。此信息是从多个K空间线中获得的，除了正常的图像采集外，还获得了这些信息。与PILS一样，Grappa重建算法在图像组合之前从每个组件线圈中提供了未相信的图像。由于图像重建和图像组合的步骤是在单独的步骤中执行的，因此这会导致更高的SNR和更好的图像质量。引入了Grappa技术后，主要重点是与Grappa实际实施相关的问题，包括重建算法以及所得图像中SNR的分析。最后，显示了体内grappa图像，这些图像证明了该技术的实用性。
In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique.