Research on CS-MRI Reconstruction with WHT as Sparsifying Transform

Abstract

In this paper an efficient method for the reconstruction of Magnetic Resonance Image (MRI) from the compressively sampled MR k-space. Compressive Sensing (CS) gives an efficient structure for getting back the signal or image from lesser measurements than that are really necessary according to the Nyquist criterion. The Walsh Hadamard transform is used as the sparsifying transform. In the proposed work radial and Cartesian sampling patterns are applied on k-space to collect minimum samples and MR image is recovered by taking Inverse Fourier transform of the k-space data . The Qualitative and quantitative analysis of the reconstructed images depict that the performance of Walsh Hadamard Transform as sparsifying transform gives better result in comparison with DFT. Experiments conducted on the MR Images of brain and knee show that proposed method gene-rates good quality images