Spike Noise Removal for Magnetic Resonance Imaging Based on Sparse Reconstruction

doi:10.11938/cjmr20150105

Abstract

Abstract:

It is well-known that, in magnetic resonance imaging (MRI), the presence of spike noise in the K-space will degrade the quality of reconstructed images. In this work, we proposed a method to remove spike noises based on the non-linear conjugate gradient (NLCG) reconstruction algorithm of compressed sensing (CS). The traditional CG algorithm reconstructs images in the wave-domain, making it difficult to remove spike noises. The proposed algorithm is a partial K-space reconstruction algorithm. Using image sparsity as a restrain, the algorithm reconstructs only the data which is covered by spike noises. Compared with the interpolation and NLCG algorithm, the proposed algorithm was shown to yield better images with less artifacts without the need to know the accurate localization of the spike noises.

Key words: MRI, compressed sensing, non-linear conjugate gradient, K-space reconstruction, spike noise

CLC Number:

O482.53

LI Zhi-min1，XIE Hai-bin1，ZHOU Min-xiong1，ZHANG Cheng-xiu2，XI Wei2，JIANG Xiao-ping2，YANG Guang1. Spike Noise Removal for Magnetic Resonance Imaging Based on Sparse Reconstruction[J]. Chinese Journal of Magnetic Resonance, 2015, 32(1): 40-50.

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