Algorithms for Automatic Fiber Tract Mapping in the CNS

为了了解中枢神经系统(CNS)的病理演变并开发有效的治疗方法，需要将神经纤维的连通性与功能可视化联系起来。这种结构-功能信息在中枢神经系统过程中是基本的，因为解剖连接决定了信息在哪里传递和处理。最新的磁共振扩散张量成像(DTI)方法可以提供观察结构连通性所需的基本信息，并可以在体内可视化大脑中的纤维束。然而，需要稳健和准确的采集和处理算法来准确地映射神经连接。中枢神经系统(CNS)神经纤维束的自动定位是图像处理中的一个具有挑战性的问题，因为数据中含有噪声，这使得对神经纤维束的可靠估计变得困难。DTI数据集很大，在高效算法的设计中提出了一个巨大的挑战。在这个方案中，我们将开发新的、统计稳健和高效的算法来实现中枢神经系统的自动纤维束标测。自动光纤束映射问题将分两个阶段解决，即数据平滑阶段和光纤束映射阶段。在前者中，平滑将通过一种新的非线性各向异性扩散算法来实现，该算法在平滑数据的同时努力保留所有相关细节。在后者中，从平滑后的数据计算指示每个空间位置处的主要各向异性方向的平滑3D矢量场。然后利用有效的数值方法将纤维束确定为该矢量场的正则化积分曲线。为了验证自动估计的纤维束，我们将建立染色和切除的大鼠脊髓/脑的荧光显微镜图像中的纤维束与从活体获得的DTI数据估计的纤维束之间的关联。然后，将在损伤的脊髓以及之前获得的完整小鼠、大鼠大脑和孤立心脏的数据集上测试该方法在病理学上的实用性。

To understand evolving pathology in the central nervous system (CNS) and develop effective treatments, ways are needed to correlate the nerve fiber connectivity with the visualization of function. Such structure-function information is fundamental in CNS processes since anatomical connections determine where information is passed and processed. Recent methods of magnetic resonance diffusion tensor imaging (DTI) can provide the fundamental information required for viewing structural connectivity and can visualize fiber bundles in the brain in vivo. However, robust and accurate acquisition and processing algorithms are needed to accurately map the nerve connectivity. Automatic fiber tract mapping in the central nervous system (CNS) is a challenging problem for image processing since the data is noisy, making reliable estimation of the fiber tracts difficult. DTI data sets are large and present a formidable challenge in the design of efficient algorithms. In this proposal, we will develop novel, statistically robust and efficient algorithms for automatic fiber tract mapping in the CNS. The automatic fiber tract mapping problem will be solved in two phases, namely a data smoothing phase and a fiber tract mapping phase. In the former, smoothing will be achieved via a new nonlinear anisotropic diffusion algorithm which smooths the data while striving to retain all relevant detail. In the latter, a smooth 3D vector field indicating the dominant anisotropic direction at each spatial location is computed from the smoothed data. Fiber tracts will then be determined as the regularized integral curves of this vector field using efficient numerical methods. To validate the automatically estimated fiber tracts, we will establish the correlation between fiber tracts in fluorescence microscopy images of stained and excised rat spinal cord/brain and the estimated fiber tracts from the DTI data obtained in vivo. The utility of the method for pathology will then be tested on injured spinal cords and on previously acquired data sets of whole mouse, rat brains and isolated hearts.