College of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, 310018, Zhejiang, China.
Sci Rep. 2021 Apr 26;11(1):9005. doi: 10.1038/s41598-021-88567-z.
Generalized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.
广义自动校准部分并行采集（GRAPPA）和其他并行磁共振成像（pMRI）方法通过在线性计算缺失点周围的欠采样数据来恢复 k 空间中的未采集数据。为了获得线性计算的权重，需要在 k 空间的中心采集少量的自动校准信号（ACS）线。因此，这种方法使用的采样模式是在中间区域全采样数据，并在标称降采样因子下在外 k 空间欠采样。在本文中，我们提出了一种新的重建方法，该方法采用了一种与传统采样模式不同的多变量密度采样（MVDS）。我们的方法可以使用多个降采样因子和较少的 ACS 线显著提高图像质量。具体来说，传统的采样模式仅使用单个降采样因子在 ACS 外部区域均匀地对数据进行欠采样，但我们使用多个降采样因子。在采集 k 空间数据时，我们保持 ACS 线不变，对于 ACS 线附近的数据使用较小的降采样因子进行欠采样，对于 k 空间的最外层使用较大的降采样因子。使用较小降采样因子的欠采样数据对该区域进行重建后，误差更低。实验结果表明，在相同数据量的情况下，使用 NL-GRAPPA 重建我们方法采集的 k 空间数据可以比传统方法产生更低的噪声和更少的伪影。特别是，当 ACS 线数量较少时，我们的方法非常有效。
