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发展 摘要： 越来越多的婴儿大脑MR图像的可用性，例如将通过婴儿收集的图像。 连接组项目（BCP，沈博士是该项目的联合PI，专注于数据采集）， 为精确绘制动态早期大脑发育轨迹提供了前所未有的机会， 了解正常和异常的增长。然而，要充分受益于这些数据集，一个主要的障碍 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，其通常表现出较差的组织对比度，组织内强度变化大， 区域异质性和动态变化。为了填补这一关键空白，2012年，我们率先创建了 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）， 以及一套专门针对婴儿的地图集，称为"100 - 1 - 2婴儿地图集"，并进一步免费公开制作 可通过NITRC获得。在过去的4年里，iBEAT和iBE0 - 1 - 2婴儿护理软件的下载量已超过2900次 和5600+次，贡献了160+篇独立研究论文。30+表示 支持信，iBEAT现在正在推动许多实验室对早期大脑发育的MRI研究 在全世界都有。iBEAT产生的结果也在国家心理研究所得到了强调。 卫生部（NIMH）2015 - 2020战略计划。 该项目致力于iBEAT的持续开发、强化和传播， 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，用于精确的结构测量。 我们的方法采用随机森林自适应学习最佳的图像外观特征， 多模态图像以及来自组织概率图的信息背景特征。在目标2中，我们将 在多个时间点构建纵向婴儿脑图谱（即，1、3、6、9和12月龄） T1/T2加权和弥散加权MR图像。我们提出了一个一致的 稀疏表示技术，用于构建具有显著改进的结构的代表性地图集 通过明确地处理图像之间可能的未对准，即使在配准之后，也可以提供细节。在目标3中，我们 将开发一种新的基于学习的皮层拓扑校正方法，并将其与沿着 我们以婴儿为中心的分析工具和皮质表面地图集，到iBEAT精确映射 动态和复杂的皮质变化。与现有的对婴儿大脑表现不佳的工具不同，我们 将结合婴儿专用工具，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， 基于生长轨迹的包裹。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用户支持。 最后，我们将使用iBEAT处理BCP的所有成像数据，并将发布iBEAT 软件包和处理过的BCP数据通过NITRC向公众公布。