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Automated Assessment of Structural Changes & Functional Recovery Post Spinal Inju

结构变化的自动评估

基本信息

批准号：
8628880
负责人：
Baba C Vemuri
金额：
$ 49.45万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-04-01 至 2016-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8628880
关键词：
Algebraic Geometry 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 Geometry 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

项目摘要

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-功能相关性对于理解中枢神经系统（CNS）如何处理信息至关重要。轴突连接是促进中枢神经系统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）方法已被证明可以提供观察结构连接性所需的基本信息，并允许体内中枢神经系统纤维束的可视化。在这个项目中，我们建议开发从高角分辨率扩散加权图像（HARDI）中提取和分析这些图案的方法，众所周知，该图像比扩散张量成像（DTI）具有更好的分辨率。为此，选择了一种具有生物学相关性和临床意义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，解剖基质的几何特性的变化，识别损伤区域和远处脊柱节段的神经塑性变化，与脊髓损伤（SCI）后不同程度的损伤和运动恢复水平相关。在进行假设检验之前，我们将对 HARDI 数据进行去噪，然后构建正常的图谱线。将在正常寰椎和受伤脊髓之间进行变形配准和张量形态测量，为与 SCI 基质相关的每种类型的行为恢复提供独特的特征。获取 HARDI 数据后，将通过对脐带样本进行系统组织学分析来验证该假设。脊髓将被切割并用纤维和细胞染色剂染色，以验证脊髓挫伤（在人类中也常见）导致的解剖组织变化。通过组织学分析与 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。根据规范的损伤装置参数，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数据子集的结构特征。然后，将使用大余量分类器来实现新的和受损的脐带 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与每个所选类别相对应的结构转变动态模型来学习和预测运动恢复水平。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。

项目成果

期刊论文数量（21）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

AN OVER-COMPLETE DICTIONARY BASED REGULARIZED RECONSTRUCTION OF A FIELD OF ENSEMBLE AVERAGE PROPAGATORS.

DOI：
10.1109/isbi.2012.6235711
发表时间：
2012-07-12
期刊：
Proceedings. IEEE International Symposium on Biomedical Imaging
影响因子：
0
作者：
Ye W;Vemuri BC;Entezari A
通讯作者：
Entezari A

Tracking on the Product Manifold of Shape and Orientation for Tractography from Diffusion MRI.

DOI：
10.1109/cvpr.2014.390
发表时间：
2014-06
期刊：
Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops
影响因子：
0
作者：
Wang Y;Salehian H;Cheng G;Vemuri BC
通讯作者：
Vemuri BC

Multi-class DTI Segmentation: A Convex Approach.

多类 DTI 分割：凸方法。

DOI：
发表时间：
2012
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
0
作者：
Xie,Yuchen;Chen,Ting;Ho,Jeffrey;Vemuri,BabaC
通讯作者：
Vemuri,BabaC

Tractography from HARDI using an intrinsic unscented Kalman filter.

DOI：
10.1109/tmi.2014.2355138
发表时间：
2015-01
期刊：
IEEE transactions on medical imaging
影响因子：
10.6
作者：
Cheng G;Salehian H;Forder JR;Vemuri BC
通讯作者：
Vemuri BC

A Riemannian framework for matching point clouds represented by the Schrödinger distance transform.

DOI：
10.1109/cvpr.2014.486
发表时间：
2014-06
期刊：
Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
影响因子：
0
作者：
Deng Y;Rangarajan A;Eisenschenk S;Vemuri BC
通讯作者：
Vemuri BC