Automated Assessment of Structural Changes & Functional Recovery Post Spinal Inju

结构变化的自动评估

• 批准号: 8432789
• 负责人: Baba C Vemuri
• 金额: $ 48.05万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8432789
• 关键词: Algorithms; Atlases; Behavior; Behavioral; Categories; Characteristics; Classification; Communities; Contusions; Data; Data Collection; Data Set; Development; Devices; Diffusion; Diffusion Magnetic Resonance Imaging; Diffusion weighted imaging; Distant; Fiber; Goals; Human; Image Analysis; Imagery; In Vitro; Injury; Label; Learning; Left; Light; Literature; Locomotion; Locomotor Recovery; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Methods; Metric; Modeling; Motor; Neuraxis; Pattern; Population; Population Control; Population Registers; Probability; Process; Property; Rattus; Recovery; Recovery of Function; Research; Resolution; Sampling; Sensory; Severities; Solutions; Sorting - Cell Movement; Spinal; Spinal Cord; Spinal cord injury; Staging; Staining method; Stains; Structural Models; Structure; Techniques; Testing; Time; Traumatic Brain Injury; Validation; Water; Weight; base; clinically relevant; density; expectation; fiber cell; imaging modality; in vivo; injured; interest; member; morphometry; novel; public health relevance; sensor; statistics; transmission process

DESCRIPTION (provided by applicant): Establishing structure-function correlations is fundamental to understanding how information is processed in the central nervous system (CNS). Axonal connectivity is a key relationship that facilitates information transmission and reception within the CNS. Recently, diffusion weighted magnetic resonance imaging (DW-MRI) methods have been shown to provide fundamental information required for viewing structural connectivity and have allowed visualization of fiber bundles in the CNS in vivo. In this project, we propose to develop methods for extraction and analysis of these patterns from high angular resolution diffusion weighted images (HARDI) that is known to have better resolving power over diffusion tensor imaging (DTI). To this end, a biologically relevant and clinically important model has been chosen to study changes in the organization of fibers in the intact and injured spinal cord. Our hypothesis is that, changes in geometrical properties of the anatomical substrate, identifying the region of injury and neuroplastic changes in distant spinal segments, correlate with different magnitudes of injury and levels of locomotor recovery following spinal cord injury (SCI). Prior to hypothesis testing, we will denoise the HARDI data and then construct a normal atlas cord. Deformable registration and tensor morphometry between a normal atlas and an injured cord would be performed to provide a distinct signature for each type of behavior recovery associated with the SCI substrate. Validation of the hypothesis will be performed through systematic histological analysis of cord samples following acquisition of the HARDI data. Spinal cords will be cut and stained with fiber and cell stains to verify changes in anatomical organization that result from contusive injury (common in humans as well) to the spinal cord. A comparison between anatomical characteristics obtained from histological versus HARDI analysis will provide validation for the image analysis and the hypothesis. Three severities of spinal cord injuries will be produced (light, mild and moderate contusions) based upon normed injury device parameters. The structural signatures of these labeled data subsets will then be identified. Automatic classification of novel & injured cord HARDI data sets will then be achieved using a large margin classifier. Finally, HARDI data acquired over time will be analyzed in order to learn and predict the level of locomotor recovery by studying the structural changes over time and developing a dynamic model of structural transformations corresponding to each chosen class. We will use an auto-regressive model in the feature space to track and predict structural changes in SCI and correlate it to functional recovery.

描述（由申请人提供）：建立结构-功能相关性是理解中枢神经系统（CNS）中信息如何处理的基础。轴突连接是促进CNS内信息传输和接收的关键关系。最近，扩散加权磁共振成像（DW-MRI）方法已被证明提供所需的基本信息，观察结构的连接性，并允许在体内的中枢神经系统中的纤维束的可视化。在这个项目中，我们建议开发的方法提取和分析这些模式的高角分辨率扩散加权图像（HARDI），这是已知的具有更好的分辨率比扩散张量成像（DTI）。为此，选择了一种生物学相关和临床重要的模型来研究完整和受损脊髓中纤维组织的变化。我们的假设是，解剖基质的几何性质的变化，确定损伤区域和神经可塑性变化在远处的脊髓节段，与不同程度的损伤和运动恢复水平脊髓损伤（SCI）后。在假设检验之前，我们将对HARDI数据进行去噪，然后构建正常寰椎脊髓。正常寰椎和受损脊髓之间的变形配准和张量形态测量将被执行，以提供与SCI基质相关的每种类型的行为恢复的不同签名。将通过采集HARDI数据后对脐带样本进行系统组织学分析来验证假设。将切割脊髓并用纤维和细胞染色剂染色，以验证脊髓挫伤（也常见于人类）导致的解剖组织变化。组织学分析与HARDI分析获得的解剖特征之间的比较将为图像分析和假设提供验证。根据正常损伤器械参数，将产生三种严重程度的脊髓损伤（轻度、轻度和中度挫伤）。然后将识别这些标记的数据子集的结构签名。然后将使用大边界分类器实现新的和受伤的脊髓HARDI数据集的自动分类。最后，将分析随时间推移获得的HARDI数据，以便通过研究随时间推移的结构变化并开发对应于每个选定类别的结构转换的动态模型来学习和预测运动恢复的水平。我们将在特征空间中使用自回归模型来跟踪和预测SCI的结构变化，并将其与功能恢复相关联。