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，需要将神经纤维连接与功能可视化联系起来。这种结构-功能信息是中枢神经系统过程的基础，因为解剖连接决定了信息传递和处理的位置。磁共振弥散张量成像（DTI）的最新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，为了准确地绘制神经连通性，需要稳健、准确的采集和处理算法。中枢神经系统（CNS）的自动纤维束映射是图像处理中一个具有挑战性的问题，因为数据存在噪声，难以对纤维束进行可靠估计。DTI数据集非常庞大，对高效算法的设计提出了巨大的挑战。在这个建议中，我们将开发新颖的、统计上稳健的、高效的算法来自动映射中枢神经系统中的纤维束。光纤束自动映射问题将分为两个阶段来解决，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，从光滑的数据中计算出一个光滑的三维矢量场，表示每个空间位置的主要各向异性方向。然后用有效的数值方法将纤维束确定为该矢量场的正则积分曲线。为了验证自动估计的纤维束，我们将在染色和切除的大鼠脊髓/脑的荧光显微镜图像中建立纤维束与体内获得的DTI数据估计的纤维束之间的相关性。然后将在受伤的脊髓和先前获得的整个小鼠、大鼠大脑和分离心脏的数据集上测试病理学方法的效用。

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.