Seiberlich Nicole, Breuer Felix, Heidemann Robin, Blaimer Martin, Griswold Mark, Jakob Peter
Department of Experimental Physics 5, University of Würzburg, Am Hubland, Würzburg, Germany.
Magn Reson Med. 2008 May;59(5):1127-37. doi: 10.1002/mrm.21602.
Most k-space-based parallel imaging reconstruction techniques, such as Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA), necessitate the acquisition of regularly sampled Cartesian k-space data to reconstruct a nonaliased image efficiently. However, non-Cartesian sampling schemes offer some inherent advantages to the user due to their better coverage of the center of k-space and faster acquisition times. On the other hand, these sampling schemes have the disadvantage that the points acquired generally do not lie on a grid and have complex k-space sampling patterns. Thus, the extension of Cartesian GRAPPA to non-Cartesian sequences is nontrivial. This study introduces a simple, novel method for performing Cartesian GRAPPA reconstructions on undersampled non-Cartesian k-space data gridded using GROG (GRAPPA Operator Gridding) to arrive at a nonaliased image. Because the undersampled non-Cartesian data cannot be reconstructed using a single GRAPPA kernel, several Cartesian patterns are selected for the reconstruction. This flexibility in terms of both the appearance and number of patterns allows this pseudo-Cartesian GRAPPA to be used with undersampled data sets acquired with any non-Cartesian trajectory. The successful implementation of the reconstruction algorithm using several different trajectories, including radial, rosette, spiral, one-dimensional non-Cartesian, and zig-zag trajectories, is demonstrated.
大多数基于k空间的并行成像重建技术，如广义自校准部分并行采集（GRAPPA），需要采集规则采样的笛卡尔k空间数据，以便有效地重建无混叠图像。然而，非笛卡尔采样方案由于能更好地覆盖k空间中心且采集时间更快，为用户提供了一些固有优势。另一方面，这些采样方案的缺点是采集的点通常不在网格上，且具有复杂的k空间采样模式。因此，将笛卡尔GRAPPA扩展到非笛卡尔序列并非易事。本研究介绍了一种简单、新颖的方法，用于对使用GROG（GRAPPA算子网格化）网格化的欠采样非笛卡尔k空间数据执行笛卡尔GRAPPA重建，以获得无混叠图像。由于欠采样的非笛卡尔数据不能使用单个GRAPPA内核进行重建，因此选择了几种笛卡尔模式进行重建。模式的外观和数量方面的这种灵活性使得这种伪笛卡尔GRAPPA可用于以任何非笛卡尔轨迹采集的欠采样数据集。使用包括径向、玫瑰花结、螺旋、一维非笛卡尔和锯齿形轨迹在内的几种不同轨迹成功实现了重建算法。
