结合非线性GRAPPA与SENSE的并行磁共振成像

针对SENSE并行磁共振成像中采用补零缺失数据方法估计敏感度分布不准确性的问题,提出采用非线性GRAPPA方法估算缺失的K空间欠采样数据.计算并行线圈的敏感度分布,将这些敏感度分布应用于SENSE并行磁共振成像.采用不同加速因子的脑磁共振K空间欠采样数据以验证提出算法的重建性能.实验结果表明,与单一的非线性GRAPPA和SENSE重建算法相比,该混合NLGRAPPA-SENSE算法在加速因子较大时可以重建出更加准确的磁共振图像,具有更低的噪声功率(AP)和更高的信噪比(SNR)性能.

Parallel MRI based on combination of nonlinear GRAPPA and SENSE

The nonlinear GRAPPA method was applied to estimate the undersampled K-space data in order to overcome the inaccuracy of sensitivity map of each coil calculated by zero-padding method. Then the accurate sensitivity map of each coil was calculated. The sensitivity encoding (SENSE) method was adopted to reconstruct the MR image from the under-sampled K-space data based on the accurate sensitivity map. The proposed hybrid technique was tested on MR brain image reconstructions at various acceleration rates. Experimental results show that the proposed method can achieve significant improvements in reconstruction accuracy when compared with the nonlinear GRAPPA and SENSE methods, with lower artificial power (AP) and higher signal to noise ratio (SNR).