Huo Donglai, Wilson David L
Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, Arizona, USA.
J Magn Reson Imaging. 2008 Jun;27(6):1412-20. doi: 10.1002/jmri.21352.
To develop and optimize a new modification of GRAPPA (generalized autocalibrating partially parallel acquisitions) MR reconstruction algorithm named "Robust GRAPPA."
In Robust GRAPPA, k-space data points were weighted before the reconstruction. Small or zero weights were assigned to "outliers" in k-space. We implemented a Slow Robust GRAPPA method, which iteratively reweighted the k-space data. It was compared to an ad hoc Fast Robust GRAPPA method, which eliminated (assigned zero weights to) a fixed percentage of k-space "outliers" following an initial estimation procedure. In comprehensive experiments the new algorithms were evaluated using the perceptual difference model (PDM), whereby image quality was quantitatively compared to the reference image. Independent variables included algorithm type, total reduction factor, outlier ratio, center filling options, and noise across multiple image datasets, providing 10,800 test images for evaluation.
The Fast Robust GRAPPA method gave results very similar to Slow Robust GRAPPA, and showed significant improvements as compared to regular GRAPPA. Fast Robust GRAPPA added little computation time compared with regular GRAPPA.
Robust GRAPPA was proposed and proved useful for improving the reconstructed image quality. PDM was helpful in designing and optimizing the MR reconstruction algorithms.
开发并优化一种名为“稳健GRAPPA”的GRAPPA（广义自校准部分并行采集）磁共振成像重建算法的新改进方法。
在稳健GRAPPA中，k空间数据点在重建前进行加权。给k空间中的“离群值”赋予小权重或零权重。我们实现了一种慢速稳健GRAPPA方法，该方法对k空间数据进行迭代重新加权。将其与一种临时的快速稳健GRAPPA方法进行比较，后者在初始估计过程之后消除（赋予零权重）固定百分比的k空间“离群值”。在综合实验中，使用感知差异模型（PDM）对新算法进行评估，通过该模型将图像质量与参考图像进行定量比较。自变量包括算法类型、总缩减因子、离群值比例、中心填充选项以及多个图像数据集的噪声，共提供10800张测试图像用于评估。
快速稳健GRAPPA方法得到的结果与慢速稳健GRAPPA非常相似，并且与常规GRAPPA相比有显著改进。与常规GRAPPA相比，快速稳健GRAPPA增加的计算时间很少。
提出了稳健GRAPPA并证明其对提高重建图像质量有用。PDM有助于设计和优化磁共振成像重建算法。
