波谱学杂志2024，Vol.41Issue(3)：341-361,21.DOI:10.11938/cjmr20243087
扩散张量图像去噪算法研究进展
Research Progress of Denoising Algorithms for Diffusion Tensor Images
摘要
Abstract
Diffusion tensor imaging is an essential 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 starts with the principle of diffusion tensor imaging and the types of noise.Then it discusses the classical diffusion tensor image denoising algorithms,including algorithms based on traditional image processing and deep learning.Special emphasis is given to the status and shortcomings of diffusion tensor image denoising research.The denoising evaluation criteria and commonly used public datasets are also introduced,followed by experiments and quantitative analysis on the diffusion tensor image denoising methods mentioned in this paper.Finally,it concludes with a summary and an outlook for the field's future research directions.关键词
扩散张量成像/扩散加权成像/图像去噪/深度学习/生成模型Key words
diffusion tensor imaging/diffusion weighted imaging/image denoising/deep learning/generative model分类
数理科学引用本文复制引用
杨黎明,王远军..扩散张量图像去噪算法研究进展[J].波谱学杂志,2024,41(3):341-361,21.基金项目
上海市自然科学基金资助项目(18ZR1426900). （18ZR1426900）
