Multimodality image-based assessment system for traumatic brain injury

基于图像的多模态脑外伤评估系统

基本信息

批准号：
8601141
负责人：
STEPHEN R AYLWARD
金额：
$ 14.74万
依托单位：
KITWARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-01-01 至 2014-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8601141
关键词：
Accidents Acute Address Algorithms American Anatomy Appearance Behavioral Biological Assay Biological Metamorphosis Blunt Trauma Brain Brain Injuries Brain imaging Chronic Clinical Cognitive Collaborations Computational algorithm Computer software Computing Methodologies Conflict (Psychology)Contracts Craniocerebral Trauma Data Set Databases Detection Development Edema Encapsulated Evolution Future Grouping Growth Hand Health Health Care Costs Hemorrhage Image Image Analysis Imagery Infiltration Injury Investigation Joints Left Lesion Location Measurement Measures Medical Methodology Methods Metric Military Personnel Monitor Motor Multimodal Imaging Neurologic Neuropsychological Tests Operative Surgical Procedures Outcome Outcome Measure Pathology Pathology Report Patient Care Patients Play Process Public Health Recording of previous events Recovery of Function Research Infrastructure Role Serum Services Shapes Slice Specificity Sports Statistical Methods Structure System Techniques Technology Time Tissues Trauma Traumatic Brain Injury Tumor Volume United States Ventricular Work base brain remodeling brain shape clinical care clinical decision-making clinically relevant combat experience falls functional decline gray matter image registration imaging Segmentation improved in vivo member multidisciplinary multimodality neuroimaging neuropsychological novel open source outcome forecast population based prognostic public health relevance response tool user friendly software white matter

项目摘要

DESCRIPTION (provided by applicant): Nearly 1.7 million Americans suffer traumatic brain injury (TBI) annually, which constitutes an important and significant US medical health concern. Although neuroimaging plays an important role in pathology localization and surgical planning, TBI clinical care does not currently take full advantage of neuroimaging computational technology. We propose to develop and validate computational algorithms, based on image segmentation, registration and analysis, which yield quantitative measures to characterize injury, monitor pathology evolution, inform patient prognosis and optimize patient care workflows. This project addresses the current clinical need for informative TBI metrics and the technical need for easy-to-use image analysis tools capable of handling large, heterogenous pathologies that cause severe brain deformations. In Aim 1, we will perform multimodal brain image segmentation for the assessment of acute and chronic TBI, and for measuring longitudinal changes. We will generate quantitative measures of TBI pathology that are based on segmenting lesions, hemorrhages, ventricles, gray matter (GM), white matter (WM) and the brain midline from multimodal image datasets. Clinically, these metrics will be used to quantitatively describe and assess injury at any time point (acute, chronic) and for longitudinal tracking based on pathology type, location and extent. The second aim of this project is to advance the state-of-the-art in image registration for acute and chronic assessment of TBI and for longitudinal change measurement. Deformable image registration aligns corresponding anatomy in images and returns a displacement or flow field encapsulating the deformations between them. We will continue development of "geometric metamorphosis", can register images with significant appearance changes caused by structures that grow or contract, such as TBI pathologies. We will derive novel voxel-wise quantifications and visualizations of pathology infiltration and of brain deformations induced by injury or longitudinal brain changes, both within lesions and within GM and WM. The third aim is to investigate the ability of our novel TBI metrics, derived from image segmentation and registration, to predict outcome and guide clinical decision making. The focus is on final clinical impact and on evaluating the relationship between brain remodeling (e.g. structural changes) with functional recovery or decline. We will use multivariate statistical methods to evaluate the prognostic abilities of the novel multimodal image-based measures of TBI (volumetric and deformation-based) from Aims 1-2 with respect to the neuropsychological motor, cognitive and behavioral outcome measures available for each TBI patient. Multivariate techniques will also allow investigation into the grouping of patient sub populations based on statistical features that describe their commonalities or optimally differentiate between them. This will aid in the customization of clinical workflows specific to each patient sub- group. Ultimately, the technical advances being proposed here will yield the ability to use imaging to monitor brain responses to trauma in an integrative, longitudinal fashion, with maximal clinical utility and specificity.

描述（由申请人提供）：每年有近 170 万美国人遭受创伤性脑损伤 (TBI)，这构成了美国重要的医疗健康问题。尽管神经影像在病理定位和手术计划中发挥着重要作用，但 TBI 临床护理目前尚未充分利用神经影像计算技术。我们建议开发和验证基于图像分割、配准和分析的计算算法，从而产生定量测量来表征损伤、监测病理演变、告知患者预后并优化患者护理工作流程。该项目满足了当前临床对信息丰富的 TBI 指标的需求，以及对易于使用的图像分析工具的技术需求，这些工具能够处理导致严重脑变形的大型异质病理。在目标 1 中，我们将进行多模态脑图像分割，以评估急性和慢性 TBI，并测量纵向变化。我们将基于从多模态图像数据集中分割病变、出血、脑室、灰质 (GM)、白质 (WM) 和大脑中线来生成 TBI 病理学的定量测量。在临床上，这些指标将用于定量描述和评估任何时间点（急性、慢性）的损伤，并根据病理类型、位置和程度进行纵向跟踪。该项目的第二个目标是推进最先进的图像配准技术，用于 TBI 的急性和慢性评估以及纵向变化测量。可变形图像配准对齐图像中的相应解剖结构，并返回封装它们之间变形的位移或流场。我们将继续开发“几何变形”，可以记录由生长或收缩的结构引起的显着外观变化的图像，例如 TBI 病理。我们将对病理浸润和损伤或纵向脑变化引起的脑变形进行新颖的体素量化和可视化，两者均在 GM 和 WM 内的病变。第三个目标是研究我们的新颖 TBI 指标（源自图像分割和配准）预测结果和指导临床决策的能力。重点是最终的临床影响以及评估大脑重塑（例如结构变化）与功能恢复或衰退之间的关系。我们将使用多变量统计方法来评估目标 1-2 中基于 TBI 的新型多模态图像测量（基于体积和变形）的预后能力，涉及每位 TBI 患者可用的神经心理运动、认知和行为结果测量。多变量技术还可以对患者亚群进行分组调查基于描述其共性或最佳区分它们的统计特征的群体。这将有助于定制针对每个患者亚组的临床工作流程。最终，这里提出的技术进步将产生利用成像以综合、纵向方式监测大脑对创伤反应的能力，并具有最大的临床实用性和特异性。