Continued Development of Infant Brain Analysis Tools

婴儿大脑分析工具的持续开发

• 批准号: 9919645
• 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/9919645
• 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; large scale data; 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.

婴幼儿脑分析工具的持续发展 摘要： 婴儿大脑磁共振图像的可用性不断增加，例如将通过Baby收集的图像 Connectome Project(BCP，沈博士是该项目的合作伙伴，专注于数据采集)提供了 为精确绘制动态早期大脑发育轨迹提供了前所未有的机遇 理解正常和异常增长。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计算工具来准确处理和分析 婴儿核磁共振数据，通常显示组织对比度差，组织内强度差异大，以及 区域--异质性和动态变化。为了填补这一关键空白，我们在2012年率先创建了 以婴儿为中心的磁共振成像处理软件包，称为婴儿脑提取和分析工具(IBEAT)， 和一套专门针对婴儿的地图集，称为UNC 0-1-2婴儿地图集，并进一步自由和公开地制作了它们 可通过NITRC获得。在过去的4年里，iBEAT和UNC 0-1-2婴儿地图集已被下载2900+ 和5600+次，发表独立研究论文160多篇。如30+所示 支持信，iBEAT现在正在推动许多实验室对早期大脑发育的核磁共振研究 在世界各地。IBEAT产生的结果也在国家心理研究所得到了强调 《S 2015-2020年战略规划》。 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，我们将创建一个创新的基于学习的多源信息 用于关节颅骨剥离和组织分割的集成框架，以实现准确的结构测量。 该方法利用随机森林自适应地学习图像的最优外观特征 多模式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)的婴儿纵向脑图谱 用于T1/T2加权和扩散加权MR图像。我们提出了一个纵向一致的 具有显著改进结构的代表性地图集的稀疏表示技术 通过明确地处理图像之间可能的未对齐，即使在配准之后也是如此。在目标3中，我们 将开发一种新的基于学习的皮质拓扑校正方法，并将其与 我们以婴儿为中心的分析工具和皮质表面图谱，整合到iBEAT中，用于精确绘制 婴儿的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、曲面重建、配准、分割、 和尺寸。我们将进一步整合配备有 基于增长轨迹的分割。在目标4中，我们将在以下方面显著增强iBEAT 软件功能以及通过系统的外联和培训提供的用户支持。 最后，我们将使用iBEAT来处理来自BCP的所有图像数据，并将发布iBEAT和iBEAT 软件包和处理后的BCP数据通过NITRC向公众公布。