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高阶张量能够以其简单的多项式形式表示多叶函数,被广泛应用于纤维方向分布估计中.但随着高阶张量阶数的增加,现有方法存在难以稳定重构纤维方向和角度分辨率低等缺陷.引入非负约束条件是目前提高稳定性的常用方法,该方法也仅能保证低于6阶时纤维方向的稳定估计.针对以上问题,文章在高阶张量拟合模型基础上引入球面反卷积模型,并提出了一种自适应非负约束迭代算法进行纤维方向分布估计.该算法以高阶张量为基函数拟合纤维扩散分布,沿纤维方向调整非负约束,并自适应训练调整矩阵参数.为了验证本算法的有效性,通过仿真数据与实际临床数据在同等条件下与现有CT-FoD,CSD算法进行角度分辨率,角度误差以及纤维重建对比实验.结果表明,文章所提出的方法在角度分辨率和稳定性方面优于现有的两种方法.
Higher-order tensors can express multi-leaf functions in their simple polynomial form and are widely used in fiber orientation distribution estimation. However, with the increase of higher-order tensor orders, the existing methods have difficulty in stably reconstructing the fiber orientation and angular resolution Low defect.The introduction of non-negative constraints is a common method to improve the stability at present, and the method can only guarantee the stable estimation of fiber orientation below the sixth order.According to the above problems, based on the high-order tensor fitting model The spherical deconvolution model is introduced and an adaptive nonnegative constrained iterative algorithm is proposed to estimate the fiber orientation distribution. The algorithm uses the higher order tensor as the basis function to fit the fiber diffusion distribution and adjusts the nonnegative constraint along the fiber direction In order to validate the validity of this algorithm, we compared the angular resolution, angle error and fiber reconstruction with the existing CT-FoD and CSD algorithms under the same conditions by the simulation data and the actual clinical data.The results show that The proposed method outperforms the existing two methods in terms of angular resolution and stability.