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）：每年近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。尽管神经影像学在病理定位和手术计划中发挥着重要作用，但目前TBI临床护理并没有充分利用神经影像学计算技术。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、告知患者预后和优化患者护理工作流程。该项目解决了目前临床对信息性脑损伤指标的需求，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，我们将执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。我们将根据多模态图像数据集对病灶、出血、脑室、灰质（GM）、白质（WM）和脑中线进行分割，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，用于急性和慢性TBI评估和纵向变化测量。可变形图像配准对图像中相应的解剖结构进行对齐，并返回包含它们之间变形的位移或流场。我们将继续开发“几何变态”，可以注册图像与显著的外观变化引起的结构生长或收缩，如创伤性脑损伤病理。我们将获得新的体素量化和可视化病理浸润和脑变形引起的损伤或纵向脑变化，两者都在