Algorithms for Automatic Fiber Tract Mapping in the CNS

CNS 中自动纤维束映射的算法

• 批准号: 6624055
• 负责人: Baba C Vemuri
• 金额: $ 34.44万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-01 至 2006-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6624055
• 关键词: animal data; autonomic nervous system; bioimaging /biomedical imaging; brain imaging /visualization /scanning; brain mapping; central neural pathway /tract; computational neuroscience; diffusion; fluorescence microscopy; image processing; laboratory rat; magnetic resonance imaging; spinal cord mapping

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.

要了解中枢神经系统（CNS）中不断发展的病理并开发有效的治疗方法，需要方法将神经纤维连通性与功能的可视化相关联。 这种结构功能信息在中枢神经系统过程中至关重要，因为解剖连接决定了信息的传递和处理位置。 磁共振扩散张量成像（DTI）的最新方法可以提供查看结构连通性所需的基本信息，并可以看到体内大脑中的纤维束。 但是，需要健壮，准确的获取和处理算法来准确映射神经连接性。中枢神经系统（CNS）中的自动纤维道映射是图像处理的一个挑战性问题，因为数据很嘈杂，因此难以对纤维区域的可靠估计。 DTI数据集很大，并且在高效算法的设计中提出了巨大的挑战。在此提案中，我们将开发新颖的，统计上强大的，有效的算法，以用于中枢神经系统中的自动纤维图形映射。 自动纤维道映射问题将分为两个阶段，即数据平滑阶段和纤维道映射阶段。 在前者中，将通过一种新的非线性各向异性扩散算法来实现平滑，该算法可以使数据平滑，同时努力保留所有相关的细节。 在后者中，通过平滑数据计算出平滑的3D矢量场，指示每个空间位置的主要各向异性方向。 然后，使用有效的数值方法，将确定纤维道作为该矢量场的正则积分曲线。 为了验证自动估计的纤维道，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区域之间的相关性，以及从体内获得的DTI数据中估计的纤维图。 然后，该方法的效用将在受伤的脊髓以及先前获得的整个小鼠，大鼠大脑和孤立心脏的数据集上进行测试。