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.

为了了解中枢神经系统（CNS）中不断发展的病理学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相关联的方法。 这种结构-功能信息是中枢神经系统过程的基础，因为解剖学连接决定了信息的传递和处理。 磁共振扩散张量成像（DTI）的最新方法可以提供观察结构连接所需的基本信息，并可以可视化体内大脑中的纤维束。 然而，需要强大且准确的采集和处理算法来准确地映射神经连接性。中枢神经系统（CNS）中的自动纤维束映射是图像处理的一个具有挑战性的问题，因为数据是有噪声的，使得难以可靠地估计纤维束。DTI数据集是大的，并提出了一个艰巨的挑战，在设计有效的算法。在这项建议中，我们将开发新的，统计上强大的和有效的算法自动纤维束映射在中枢神经系统。 自动纤维束映射问题将分两个阶段解决，即数据平滑阶段和纤维束映射阶段。 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，从平滑的数据计算指示在每个空间位置处的主导各向异性方向的平滑3D向量场。 纤维束将被确定为这个矢量场的正则化积分曲线，使用有效的数值方法。 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体内获得的DTI数据中估计的纤维束之间的相关性。 然后将在损伤的脊髓和先前获得的整个小鼠，大鼠大脑和离体心脏的数据集上测试该方法的病理学效用。