【摘要】 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.更多还原

【Abstract】 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.更多还原