Abstract
In clinical Magnetic Resonance Imaging (MRI), any reduction in scan time offers a number of potential benefits ranging from high-temporal-rate observation of physiological processes to improvements in patient comfort. In this paper we proposed a reconstruction algorithm by applying contourlet thresholding in inverse scale space flows. We improved the inverse scale space with the noise item in which there is some meaning information and take full advantage of coutourlet transform's characters: perfect reconstruction, noise restraint and good directional selectivity. Several example MRI reconstructions from highly undersampled K-space data are presented for recovery of detailed features from incomplete and inaccurate measurements.
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