Reduction of artifacts in images from MR truncated data using singularity spectrum analysis

Abstract

In this paper, the theory of signal singularity spectrum analysis (SSA) is proposed. Using SSA theory, a new method is presented to reduce truncation artifacts in magnetic resonance (MR) image due to truncated spectrum data. In the scheme, after detecting signal singularity locations using wavelet analysis in spectrum domain, SSA mathematic model is constructed, where weight coefficients are determined by known truncated spectrum data. Then, the remainder of the truncated spectrum can be obtained using SSA. Experiment and simulation results show that the SSA method will produce fewer artifacts in MR image from truncated spectrum than existing methods.