Abstract: Diffusion tensor imaging is an important technique to study tissue brain microstructure and the distribution of white matter fiber tracts, however, affected by the diffusion-weighted signal attenuation and long echo time, diffusion tensor images suffer from serious low signal-to-noise ratio problem. Therefore, efficient denoising techniques are crucial for enhancing image quality. This paper firstly describes the principle of diffusion tensor imaging and the types of noise, discusses the classical diffusion tensor images denoising algorithms. The content focuses on the current research status and shortcomings of diffusion tensor images denoising from the perspectives of traditional image processing-based and deep learning-based. The denoising evaluation criteria and commonly used public datasets are also introduced. Then, the paper discusses and analyzes the diffusion tensor images denoising methods mentioned. Finally, summarizes and outlines the future research directions in this field. 

Key words: Diffusion tensor imaging, diffusion weighted imaging, image denoising, deep learning, generative model