Diffusion Tensor Image Smoothing Using Efficient and Effective Anisotropic Filtering

Abstract

To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging, an anisotropic smoothing algorithm is presented for reducing noise in diffusion tensor images efficiently and effectively. The presented algorithm is based on previous anisotropic diffusion filtering, which is implemented with a straightforward but inefficient explicit numerical scheme. The main contribution of this paper is to improve the performance of the previous method considerably by using unconditionally stable and second order time accurate semi-implicit scheme. Our new method needs only few or even one iteration to achieve better smoothed images than what is generated by tens of iterations of the previous method, which makes it more attractive to practical use. Experiments with simulated and in vivo data have demonstrated the advantage of our new algorithm for denoising diffusion tensor images in terms of efficiency and effectiveness.