We describe a technique that uses tractography to visualize neural pathways in human brains by extending an existing framework that uses overlapping Gaussian tensors to model the signal. At each point on the fiber, an unscented Kalman filter is used to find the most consistent direction as a mixture of previous estimates and of the local model. In our previous framework, the diffusion ellipsoid had a cylindrical shape, i.e., the diffusion tensor's second and third eigenvalues were identical. In this paper, we extend the tensor representation so that the diffusion tensor is represented by an arbitrary ellipsoid. Experiments on synthetic data show a reduction in the angular error at fiber crossings and branchings. Tests on in vivo data demonstrate the ability to trace fibers in areas containing crossings or branchings, and the tests also confirm the superiority of using a full tensor representation over the simplified model.

中文翻译：

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使用无迹卡尔曼滤波器的完整双张量神经纤维束成像算法。

我们描述了一种技术，该技术通过扩展使用重叠高斯张量对信号进行建模的现有框架，使用牵引成像来可视化人脑中的神经通路。在光纤上的每个点，一个无迹卡尔曼滤波器用于找到最一致的方向，作为先前估计和局部模型的混合。在我们之前的框架中，扩散椭球具有圆柱形状，即扩散张量的第二和第三特征值是相同的。在本文中，我们扩展了张量表示，使得扩散张量由任意椭圆体表示。对合成数据的实验表明，纤维交叉和分支处的角度误差有所减少。对体内数据的测试证明了在包含交叉或分支的区域中追踪纤维的能力，