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扩散张量成像去噪和纤维跟踪方法

Study on Denoising and Fiber Tracking Algorithm of Diffusion Tensor Imaging

  • 摘要: 提出一种纤维模型的数学合成方法,并利用合成模型对纤维跟踪方法进行了验证. 对Streamline跟踪技术进行改进,采用能量最小化技术对面形或球形张量的跟踪方向进行校正以提高Streamline算法的准确性.为降低图像噪声对纤维跟踪的影响,分别采用小波去噪方法对扩散加权图像和张量场进行处理,并对小波去噪和传统的高斯平滑方法在扩散加权图像噪声抑制方面的作用进行了比较.

     

    Abstract: This paper presents a method to synthesize fiber models,which are then used to validate fiber tracking algorithm.An improvement of streamline tracking technique was proposed to overcome its main shortcoming.The tracking direction of planar or sphere tensor is regularized by using energy minimization technique in tracking process.In order to eliminate the effect of noise,wavelet-based denoising technique was used both on diffusion weighted images and noisy tensor field. We also compared the wavelet-based denoising method with the Gaussian smoothing,the traditional denoising method in image analysis.

     

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