Automated Assessment of Structural Changes & Functional Recovery Post Spinal Inju

描述(由申请人提供)：建立结构-功能相关性是理解信息在中枢神经系统(CNS)中如何处理的基础。轴突连接是促进中枢神经系统内信息传递和接收的关键关系。最近，扩散加权磁共振成像(DW-MRI)方法被证明可以提供观察结构连通性所需的基本信息，并能够在体内显示中枢神经系统中的纤维束。在这个项目中，我们提出了从高角分辨率扩散加权图像(HARDI)中提取和分析这些模式的方法，该图像具有比扩散张量成像(DTI)更好的分辨率。为此，选择了一个具有生物学意义和临床重要性的模型来研究完整和损伤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，识别损伤区域和远端脊髓节段的神经可塑性变化，与脊髓损伤(SCI)后不同程度的损伤和运动恢复水平相关。在假设检验之前，我们将对Hardi数据进行去噪，然后构建正常的图谱。在正常的寰椎和损伤的脊髓之间进行可变形配准和张量形态测量，以提供与SCI底物相关的每种类型的行为恢复的明显特征。这一假设的验证将在获得Hardi数据后通过对脐带样本进行系统的组织学分析来进行。脊髓将被切开，并用纤维和细胞染色来验证脊髓挫伤(在人类中也很常见)导致的解剖组织变化。从组织学和Hardi分析获得的解剖学特征之间的比较将为图像分析和假设提供验证。根据正常的损伤装置参数，将产生三种严重的脊髓损伤(轻度、轻度和中度挫伤)。然后将识别这些已标记数据子集的结构签名。然后，将使用大间隔分类器实现对新的和损伤的脊髓Hardi数据集的自动分类。最后，将分析随时间获得的Hardi数据，以便通过研究结构随时间的变化并开发与每个选定类别对应的结构转换的动态模型来了解和预测运动恢复的水平。我们将使用特征空间中的自回归模型来跟踪和预测脊髓损伤的结构变化，并将其与功能恢复相关联。

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.