一种结合并行成像和压缩感知的快速磁共振成像新方法

A New Combination Scheme of GRAPPA and Compressed Sensing for Accelerated Magnetic Resonance Imaging

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.

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.