扩散张量图像去噪算法研究进展

Research Progress of Denoising Algorithms for Diffusion Tensor Images

扩散张量成像是研究组织大脑微结构与白质纤维束分布的重要手段,然而受扩散加权信号衰减与长回波时间的影响,扩散张量图像存在严重的低信噪比问题.因此,有效的去噪技术在提高图像质量方面发挥着重要的作用.本文首先阐述了扩散张量成像的原理及噪声类型;其次论述了经典的扩散张量图像去噪算法,包括基于传统图像处理方法与基于深度学习方法,并着重探讨了扩散张量图像去噪的研究现状及不足;接着介绍了去噪评估标准及常用的公开数据集;然后讨论分析了文中提及的扩散张量图像去噪方法;最后总结并对该领域未来的研究方向进行了展望.

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.