Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated virtual coils
Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated virtual coils
复制标题
使用联合非线性核映射和相位共轭虚拟线圈改进 GRAPPA 重建
DOI：
10.1088/1361-6560/ab274d
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发表时间：
2019-07-01
影响因子：
3.5
通讯作者：
Liang, Dong
中科院分区：
文献类型：
--
作者：
Wang, Haifeng;Jia, Sen;Liang, Dong
通过将相位共轭非线性核映射线圈与原始物理线圈进行聚合，改善GRAPPA重建条件，降低重建噪声放大。非线性GRAPPA（NL-GRAPPA）是一种基于核的非迭代方法，可以减少GRAPPA重建中噪声引起的误差。虚拟共轭线圈（VCC）将k空间的共轭对称性嵌入GRAPPA数据合成中，改善了重建条件。这项工作提出了NL-VCC-GRAPPA，联合利用非线性映射虚拟线圈和相位共轭虚拟线圈来进一步降低并行成像中的噪声放大。在体内静态和动态二维成像加速均匀欠采样计划进行评估所提出的方法在视觉图像质量，均方根误差（RMSE），和几何因子（g因子）。分析讨论了加速因子、标定数据大小和核形状对模型的影响。所提出的方法说明了改进的视觉图像质量证明了减少回顾RMSE和前瞻性g因子相比，传统的GRAPPA和最近提出的迭代SENSE-LORAKS重建。该方法虽然需要较大的标定数据量和较小的核尺寸来稳定四重扩展核的标定，但它是非迭代的，对应用中的参数调整相对不敏感。所提出的对常规GRAPPA的NL-VCC扩展为通过广泛可用的均匀欠采样方案以实际有效的方式加速的成像场景带来了明显的改进，而无需迭代。
To improve the reconstruction condition and alleviate the noise amplification of GRAPPA reconstruction by aggregating the phase conjugated and nonlinear kernel mapped coils with the original physical coil. Nonlinear GRAPPA (NL-GRAPPA) is a kernel-based non-iterative approach which can reduce noise-induced error in GRAPPA reconstruction. And virtual conjugate coil (VCC) embeds the conjugate symmetric property of k-space into GRAPPA data synthesis to improve reconstruction condition. This work proposed NL-VCC-GRAPPA to jointly utilize the nonlinear mapped virtual coil and phase conjugated virtual coil to further reduce noise amplification in parallel imaging. In vivo static and dynamic 2D imaging accelerated by uniform undersampling schemes were performed to evaluate the proposed method in terms of visual image quality, root-mean-square-error (RMSE), and geometry factor (g-factor). The effects of acceleration factors, calibration data size and kernel shape on the proposed model were also separately analyzed and discussed. The proposed method illustrated improved visual image quality evidenced by reduced retrospective RMSE and prospective g-factor comparing with conventional GRAPPA and the recently proposed iterative SENSE-LORAKS reconstructions. Although a larger amount of calibration data and smaller kernel size were required to stabilize the calibration of fourfold extended kernel for the proposed method, it was non-iterative and relatively insensitive to parameter adjustment in the applications. The proposed NL-VCC-extension to conventional GRAPPA brings visible improvements for imaging scenarios accelerated by the widely available uniform undersampling schemes in a practically efficient manner without iteration.