Non-Cartesian data reconstruction using GRAPPA operator gridding (GROG)
Non-Cartesian data reconstruction using GRAPPA operator gridding (GROG)
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DOI：
10.1002/mrm.21435
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发表时间：
2007-12-01
影响因子：
3.3
通讯作者：
Griswold, Mark A.
中科院分区：
文献类型：
--
作者：
Seiberlich, Nicole;Breuer, Felix A.;Griswold, Mark A.
本文描述了一种新的方法，该方法利用并行成像的概念，利用GRAPPA算子网格（GROG）沿非笛卡尔轨迹采样网格数据。GROG将任何获得的数据点移动到其最近的笛卡尔位置，从而将非笛卡尔数据转换为笛卡尔数据。与其他并行成像方法不同，GROG从沿着逻辑k空间方向的单一基权重集合成任何方向移动的净权重。由于基集的大小大大减小，与其他并行成像方法相比，GROG校准和重建所需的操作和校准数据更少。而不是计算和应用密度补偿函数（DCF）， GROG只需要局部平均，因为重建的点落在笛卡尔网格上。仿真结果表明，使用GROG网格划分的图像的均方根误差（RMSE）值与使用金标准卷积网格划分的图像相似。最后，将GROG与卷积网格技术进行比较，使用沿径向、螺旋形、玫瑰花形和BLADE（即周期性旋转重叠平行线与增强重建[螺旋桨]）轨迹采样的数据。
A novel approach that uses the concepts of parallel imaging to grid data sampled along a non-Cartesian trajectory using GRAPPA operator gridding (GROG) is described. GROG shifts any acquired data point to its nearest Cartesian location, thereby converting non-Cartesian to Cartesian data. Unlike other parallel imaging methods, GROG synthesizes the net weight for a shift in any direction from a single basis set of weights along the logical k-space directions. Given the vastly reduced size of the basis set, GROG calibration and reconstruction requires fewer operations and less calibration data than other parallel imaging methods for gridding. Instead of calculating and applying a density compensation function (DCF), GROG requires only local averaging, as the reconstructed points fall upon the Cartesian grid. Simulations are performed to demonstrate that the root mean square error (RMSE) values of images gridded with GROG are similar to those for images gridded using the gold-standard convolution gridding. Finally, GROG is compared to the convolution gridding technique using data sampled along radial, spiral, rosette, and BLADE (a.k.a. periodically rotated overlapping parallel lines with enhanced reconstruction [PROPELLER]) trajectories.