压缩感知（CS）技术和并行成像技术（主要是SENSE技术、GRAPPA技术等）都能通过减少k空间数据的采集量来加快磁共振成像速度，目前已有一些将两种方法相结合进一步加速磁共振成像速度的方法（例如CS-GRAPPA）.本文针对数据采集和重建这两方面对现有CS-GRAPPA方法进行了改进，采集方式上采用了局部等间隔采集模板以满足GRAPPA重建的要求，并对采集模板进行随机放置以满足CS重建的要求；数据重建时，根据自动校正数据估算GRAPPA算法中欠采行的重建误差，并利用误差的大小确定在CS算法中保真的程度.不同磁共振图像重建实验的结果表明：与现有方法相比，本文方法能够更好地保留原有图像细节并有效减少伪影.
黄丽洁
,
宋阳
,
赵献策
,
谢海滨
,
吴东梅
,
杨光
. 一种结合并行成像和压缩感知的快速磁共振成像新方法[J]. 波谱学杂志, 2018
, 35(1)
: 31
-39
.
DOI: 10.11938/cjmr20172578
Both compressed sensing (CS) and parallel imaging (PI) can be used to accelerate magnetic resonance imaging (MRI) by under-sampling the k space data. Several methods combining CS and PI have been proposed to further improve the scanning speed. In this paper, we proposed a new approach to combine CS and PI. We used GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) algorithm to reconstruct local under-sampled k space data, and CS to reconstruct the whole k space data for each coil. In the CS reconstruction step, we constrained that the reconstructed k space data should be assimilated to both the sampled k space data and the reconstructed k space data by GRAPPA. In addition, we designed a new sampling strategy to improve the quality of image reconstruction. In vivo imaging results demonstrated that the proposed approach could effectively remove artifacts and improve the image quality.
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