Comparison of denoising methods in diffusion tensor imaging

In this paper, a non linear adaptive Gaussian de-noising method for diffusion tensor imaging (DTI) is proposed. DTI image are of poor SNR and low resolution images. In order to improve DTI, the proposed method is applied to the diffusion weighted images (DWI) from which DTI is computed. The anisotropic flow principle is used in non linear adaptive Gaussian denoising method and smoothing will vary according to the anisotropic flow. The proposed method is compared with the scalar Partial Differential Equation (PDE) denoising method. The non linear adaptive Gaussian denoising method shows better performance compared to scalar PDE. To evaluate the efficiency of both denoising methods, image quality metrics like peak signal-to-noise ratio (PSNR) and mean structural similarity index measure (MSSIM) are used. The experimental results indicate the good performance of proposed method and it has a better denoising effect in DTI compared to scalar PDE.