Continued Development of Infant Brain Analysis Tools

婴儿大脑分析工具的持续开发

• 批准号: 9755508
• 负责人: Gang Li
• 金额: $ 46.53万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-03 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9755508
• 关键词: 2 year old; Address; Adult; Age; Age-Months; Appearance; Atlases; Automobile Driving; Base of the Brain; Brain; Complex; Computer software; Data; Data Set; Development; Documentation; Education and Outreach; Environment; Exhibits; Goals; Growth; Human; Image; Infant; Infant Development; Joints; Label; Learning; Letters; Life; MRI Scans; Magnetic Resonance Imaging; Maps; Measurement; Methods; Multimodal Imaging; National Institute of Mental Health; Neurodevelopmental Disorder; Online Systems; Paper; Play; Probability; Process; Publications; Reporting; Research; Research Personnel; Role; Series; Shapes; Software Tools; Source; Speed; Strategic Planning; Structure; Surface; T2 weighted imaging; Techniques; Time; Tissues; Training; Variant; adaptive learning; base; computerized tools; connectome; cranium; critical period; data acquisition; diffusion weighted; experience; file format; gray matter; imaging modality; imaging study; improved; innovation; interoperability; novel; postnatal; random forest; reconstruction; tool; white matter

Continued Development of Infant Brain Analysis Tools Abstract: The increasing availability of infant brain MR images, such as those that will be collected through the Baby Connectome Project (BCP, on which Dr. Shen is a Co-PI, focusing on data acquisition), affords unprecedented opportunities for precise charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, to fully benefit from these datasets, a major barrier that needs to be overcome is the critical lacking of computational tools for accurate processing and analysis of infant MRI data, which typically exhibit poor tissue contrast, large within tissue intensity variation, and regionally-heterogeneous and dynamic changes. To fill this critical gap, in 2012 we pioneered in creating an infant-centric MRI processing software package, called infant Brain Extraction and Analysis Tool (iBEAT), and a set of infant-specific atlases, called UNC 0-1-2 Infant Atlases, and further made them freely and publicly available via NITRC. Over the last 4 years, iBEAT and UNC 0-1-2 Infant Atlases have been downloaded 2900+ and 5600+ times, respectively, and contributed to 160+ independent research papers. As indicated by 30+ support letters, iBEAT is now driving the research for MRI studies of early brain development in many labs throughout the world. Results produced by iBEAT are also highlighted in the National Institute of Mental Health (NIMH)'s 2015-2020 Strategic Plan. This project is dedicated to the continuous development, hardening, and dissemination of iBEAT, by developing innovative software modules with comprehensive user support. To achieve this goal, we propose four aims. In Aim 1, we will create an innovative learning-based multi-source information integration framework for joint skull stripping and tissue segmentation for accurate structural measurements. Our method employs random forest to adaptively learn the optimal image appearance features from multimodality images and also informative context features from tissue probability maps. In Aim 2, we will construct longitudinal infant brain atlases at multiple time points (i.e., 1, 3, 6, 9, and 12 months of age) for both T1-/T2-weighted and diffusion-weighted MR images. We propose a longitudinally-consistent sparse representation technique to construct representative atlases with significantly improved structural details by explicitly dealing with possible misalignments between images even after registration. In Aim 3, we will develop a novel learning-based approach for cortical topology correction and integrate it, along with our infant-centric analysis tools and atlases for cortical surfaces, into iBEAT for precise mapping of dynamic and complex cortical changes in infants. Unlike existing tools that perform poorly for infant brains, we will incorporate infant-dedicated tools for topology correction, surface reconstruction, registration, parcellation, and measurements. We will further integrate longitudinal infant cortical surface atlases equipped with parcellations based on growth trajectories. In Aim 4, we will significantly enhance iBEAT in terms of its software functionalities as well as user support via systematic outreach and training. Finally, we will employ iBEAT to process all imaging data from BCP and will release both the iBEAT software package and the processed BCP data to the public via NITRC.

婴儿大脑分析工具的持续开发 抽象的： 婴儿大脑 MR 图像的可用性不断增加，例如通过婴儿收集的图像 Connectome 项目（BCP，沉博士是该项目的联合 PI，专注于数据采集），提供 精确绘制动态早期大脑发育轨迹的前所未有的机会 了解正常和异常的生长。然而，要充分受益于这些数据集，一个主要障碍 需要克服的是严重缺乏精确处理和分析的计算工具 婴儿 MRI 数据，通常表现出较差的组织对比度、组织内强度变化较大，以及 区域异质性和动态变化。为了填补这一关键空白，我们于 2012 年率先创建了 以婴儿为中心的 MRI 处理软件包，称为婴儿脑提取和分析工具 (iBEAT)， 以及一套婴儿专用地图集，称为 UNC 0-1-2 婴儿地图集，并进一步将其自由公开 可通过 NITRC 获取。在过去 4 年里，iBEAT 和 UNC 0-1-2 婴儿图集已被下载 2900 多次 和 5600+ 次，贡献了 160 多篇独立研究论文。如30+所示 支持信，iBEAT 目前正在许多实验室推动早期大脑发育的 MRI 研究 全世界。 iBEAT 得出的结果也在国家心理研究所中得到强调 健康 (NIMH) 2015-2020 年战略计划。 该项目致力于 iBEAT 的持续开发、强化和传播，由 开发具有全面用户支持的创新软件模块。为了实现这一目标，我们 提出四个目标。在目标 1 中，我们将创建一个基于学习的创新多源信息 用于关节头骨剥离和组织分割的集成框架，以进行精确的结构测量。 我们的方法采用随机森林来自适应地学习最佳图像外观特征 多模态图像以及来自组织概率图的信息上下文特征。在目标 2 中，我们将 构建多个时间点（即 1、3、6、9 和 12 个月大）的纵向婴儿脑图谱 对于 T1/T2 加权和扩散加权 MR 图像。我们提出一个纵向一致的 稀疏表示技术构建具有显着改进结构的代表性图集 即使在注册后，也可以通过显式处理图像之间可能存在的错位来详细说明。在目标 3 中，我们 将开发一种新颖的基于学习的皮层拓扑校正方法并将其集成 我们以婴儿为中心的分析工具和皮质表面图集，导入 iBEAT 中以进行精确映射 婴儿动态和复杂的皮质变化。与对婴儿大脑表现不佳的现有工具不同，我们 将包含婴儿专用工具，用于拓扑校正、表面重建、配准、分割、 和测量。我们将进一步整合纵向婴儿皮质表面图谱，配备 基于增长轨迹的分区。在目标 4 中，我们将显着增强 iBEAT 软件功能以及通过系统的推广和培训提供的用户支持。 最后，我们将使用 iBEAT 来处理来自 BCP 的所有成像数据，并将发布 iBEAT 软件包和处理后的 BCP 数据通过 NITRC 向公众公开。