一种基于径向自校准（RASCAL）的广义自动校准部分并行采集（GRAPPA）方法，使用权重插值。
A radial self-calibrated (RASCAL) generalized autocalibrating partially parallel acquisition (GRAPPA) method using weight interpolation.
机构信息
Department of Physiology, Biophysics and Systems Biology, Weill Medical College of Cornell University, New York, NY, USA.
出版信息
NMR Biomed. 2011 Aug;24(7):844-54. doi: 10.1002/nbm.1630. Epub 2010 Dec 28.
A generalized autocalibrating partially parallel acquisition (GRAPPA) method for radial k-space sampling is presented that calculates GRAPPA weights without synthesized or acquired calibration data. Instead, GRAPPA weights are fitted to the undersampled data as if they were the calibration data. Because the relative k-space shifts associated with these GRAPPA weights vary for a radial trajectory, new GRAPPA weights can be resampled for arbitrary shifts through interpolation, which are then used to generate missing projections between the acquired projections. The method is demonstrated in phantoms and in abdominal and brain imaging. Image quality is similar to radial GRAPPA using fully sampled calibration data, and improved relative to a previously described self-calibrated radial GRAPPA technique.
提出了一种用于径向 k 空间采样的广义自校准部分并行采集（GRAPPA）方法，该方法无需合成或获取校准数据即可计算 GRAPPA 权重。相反，GRAPPA 权重拟合到欠采样数据，就像它们是校准数据一样。由于与这些 GRAPPA 权重相关的相对 k 空间移位随径向轨迹而变化，因此可以通过插值重新采样新的 GRAPPA 权重，然后使用这些权重在获取的投影之间生成缺失的投影。该方法在体模和腹部及脑部成像中进行了演示。图像质量类似于使用完全采样校准数据的径向 GRAPPA，并且优于先前描述的自校准径向 GRAPPA 技术。
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