DTI扩散张量的一种稳健估计方法

A Robust Diffusion Tensor Estimation Method for DTI

为了获得更精确的DTI扩散张量场,提出了一种基于约束M估计子的稳健估计方法.首先对扩散加权图像序列进行双树复数小波降噪预处理,以减少热噪声影响.然后通过试探法找到一个合适的回归起始点,并通过Cholesky分解对扩散张量进行正定约束.最后寻找局部最小获得DTI扩散张量的约束M估计,并在模拟二阶张量场和真实DTI数据集上进行了实验.与最小二乘法和M估计子回归模型相比,该方法可以更有效地排除热噪声和生理性离群点影响,对DTI扩散张量估计很有价值.

In diffusion tensor imaging（DTI）,diffusion tensor maps are typically calculated from a sequence of diffusion weighted images.However,the diffusion weighted imaging is often influenced by both thermal noise and physiological noise such as artifacts caused by physiological motions.A robust estimation method based on the constrained M-estimator with high breakdown point and high asymptotic efficiency is proposed in this paper for acquiring more accurate DTI diffusion tensor field.First,during preprocessing phase,thermal noise in the diffusion weighted images is removed by implementing dual-tree complex wavelet transform.Then an appropriate regression starting point can be found by random sampling and considering simultaneously the positivity constraint of the diffusion tensor via the Cholesky factorization.Finally,local minimum of the objective function is obtained to achieve constrained M-estimation of the DTI diffusion tensor.Experiments are performed on the synthetic second-order tensor field and the real DTI data set,both corrupted by various levels of outliers.The calculated results show that the proposed method can remove thermal noise and physiological outliers more efficiently compared with the least square regression model and the Geman-McClure M-estimator which are more robust than the standard least square method.Therefore,the proposed method may be particularly useful for the DTI diffusion tensor estimation.