DTI Noise Reduction Using Anisotropic Filtering

Diffusion tensor magnetic resonance imaging (DTI) is the main non-invasive utility to reveal the information of local diffusivity of white matter and other fibrous human tissues. Because the diffusion weighted image (DWI) which is used to acquire DTI is sensitive to noise, effective noise removal is required to improve the accuracy of the DTI data and its subsequent applications. Instead of denoise the DWI in different direction separately, in this paper an anisotropic filtering method which synthetically considering the structure information and characteristic of the DTI is proposed. The eigenvalue and eigenvector of the smoothing structure tensor is reconstructed to denoise and keep the structure characteristic simultaneously. By the method, denoising simulations using a synthetic DTI dataset and experiments using an in vivo brain DTI dataset have been done. The results of the simulations and experiments demonstrate that using the method proposed the noise can be removed significantly and is more effective compared with the common methods.