Multimodality image-based assessment system for traumatic brain injury

基于图像的多模态脑外伤评估系统

• 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
• 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

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）：近170万美国人每年遭受创伤性脑损伤（TBI），这构成了美国重要而重要的医疗健康问题。尽管神经影像学在病理定位和手术计划中起着重要作用，但TBI临床护理目前尚未充分利用神经成像计算技术。我们建议根据图像分割，注册和分析来开发和验证计算算法，从而产生定量措施以表征损伤，监测病理进化，告知患者预后并优化患者护理工作流程。该项目解决了当前对TBI指标的临床需求，以及能够处理大型异质病理学的易于使用的图像分析工具的技术需求，这些工具会导致严重的大脑变形。在AIM 1中，我们将执行多模式的大脑图像分割，以评估急性和慢性TBI，并测量纵向变化。我们将生成基于分割病变，出血，心室，灰质（GM），白质（WM）和大脑中线的TBI病理的定量度量。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）以及基于病理类型，位置和程度的纵向跟踪。该项目的第二个目的是促进对TBI的急性和慢性评估以及纵向变化测量的最新图像注册。可变形的图像注册将图像中相应的解剖结构对齐，并返回封装它们之间变形的位移或流场。我们将继续发展“几何变态”，可以注册由成长或收缩的结构引起的显着外观变化的图像，例如TBI病理。我们将得出新颖的素数定量和病理浸润的可视化以及受损伤或纵向大脑变化引起的脑变形的可视化，这两者都内部 病变以及通用汽车和WM内。第三个目的是研究我们新颖的TBI指标的能力，这些指标源自图像分割和注册，以预测结果并指导临床决策。重点是最终临床影响和评估大脑重塑（例如结构变化）与功能恢复或下降之间的关系。我们将使用多元统计方法来评估AIMS 1-2的新型多模式基于TBI（基于体积和变形）的预后能力，相对于每个TBI患者可用的神经心理运动，认知和行为结果度量。多元技术还将允许研究患者子的分组 基于统计特征的人群描述其共同点或最佳区分。这将有助于定制每个患者子组特有的临床工作流程。最终，这里提出的技术进步将产生使用成像以综合性，纵向方式，具有最大的临床实用性和特异性来监测大脑对创伤的反应。