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

doi:10.11938/cjmr20172578

Abstract

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.

Key words: magnetic resonance imaging (MRI), compressed sensing (CS), parallel imaging (PI), sparse sampling

CLC Number:

O482.53

HUANG Li-jie, SONG Yang, ZHAO Xian-ce, XIE Hai-bin, WU Dong-mei, YANG Guang. A New Combination Scheme of GRAPPA and Compressed Sensing for Accelerated Magnetic Resonance Imaging[J]. Chinese Journal of Magnetic Resonance, 2018, 35(1): 31-39.

References

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