Signal reconstruction from MR truncated data using singularity spectrum analysis

The authors propose a theory of signal Singularity Spectrum Analysis (SSA). According to SSA, they present a method reducing the imaging artifacts due to truncation spectrum data in MRI. In their scheme, after detecting signal singularity locations using wavelet analysis in spectrum domain, weight coefficients of SSA are determined according to SSA. Then the rest of spectrum can be obtained using SSA. Experiment and simulation results show that truncation data imaging method using SSA gives much better reconstructed image than existing methods