Malcolm James G, Shenton Martha E, Rathi Yogesh
Psychiatry Neuroimaging Laboratory, Harvard Medical School, Boston, MA, USA.
Inf Process Med Imaging. 2009;21:126-38. doi: 10.1007/978-3-642-02498-6_11.
We describe a technique to simultaneously estimate a local neural fiber model and trace out its path. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by the previous. To do this we model the signal as a mixture of Gaussian tensors and perform tractography within a filter framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Synthetic experiments demonstrate that this approach reduces signal reconstruction error and significantly improves the angular resolution at crossings and branchings. In vivo experiments confirm the ability to trace out fibers in areas known to contain such crossing and branching while providing inherent path regularization.
我们描述了一种同时估计局部神经纤维模型并描绘其路径的技术。现有技术独立估计每个体素处的局部纤维方向，因此对估计的纤维模型不存在连续的置信度认知。我们将纤维追踪公式化为递归估计：在追踪纤维的每一步，当前估计都以前一步为指导。为此，我们将信号建模为高斯张量的混合，并在滤波器框架内进行纤维束成像。从一个种子点开始，使用无迹卡尔曼滤波器将每根纤维追踪到其终点，以同时拟合局部模型并沿最一致的方向传播。尽管存在噪声和不确定性，但这为沿纤维的每个点的局部结构提供了因果估计。合成实验表明，这种方法减少了信号重建误差，并显著提高了交叉点和分支处的角分辨率。体内实验证实了在已知包含此类交叉和分支的区域追踪纤维的能力，同时提供了固有的路径正则化。
