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.

持续开发婴儿大脑分析工具 抽象的： 婴儿脑MR图像的可用性增加，例如将通过婴儿收集的图像 Connectome Project（BCP，Shen博士是Co-Pi，专注于数据获取）， 空前的机遇，可以精确地绘制动态的早期大脑发育轨迹 了解规范和异常增长。但是，要完全受益于这些数据集，这是一个主要的障碍 需要克服的是，至关重要的是缺乏计算工具来准确处理和分析 婴儿MRI数据，通常在组织强度变化内显示不良组织对比，并且 区域性异构和动态变化。为了填补这个关键的空白，2012年，我们开创了 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）， 以及一组婴儿特定的图书馆，称为UNC 0-1-2婴儿地图集，并进一步自由公开地使其公开 可通过NITRC获得。在过去的四年中，Ibeat和UNC 0-1-2婴儿地图集已下载2900多 和5600次以上，并为160多个独立的研究论文做出了贡献。如30+所示 支持信，IBEAT现在正在推动许多实验室中大脑早期发展的MRI研究 全世界。国立心理研究所也强调了Ibeat产生的结果 卫生（NIMH）的2015 - 2020年战略计划。 该项目致力于通过 在全面的用户支持下开发创新的软件模块。为了实现这一目标，我们 提出四个目标。在AIM 1中，我们将创建一个创新的基于学习的多源信息 联合颅骨剥离和组织分割的整合框架，以进行准确的结构测量。 我们的方法采用随机森林来适应地学习最佳图像外观特征 多模式图像以及组织概率图的信息上下文特征。在AIM 2中，我们将 在多个时间点（即1、3、6、9和12个月大）构建纵向婴儿大脑图谱 对于T1-/T2加权和扩散加权MR图像。我们提出了一个纵向一致的 稀疏表示技术，以构建具有显着改善结构的代表性图谱 细节，即使在注册后，即使在图像之间明确处理可能的未对准。在AIM 3中，我们 将开发一种新型的基于学习的基于学习的方法来进行皮质拓扑校正和整合，并将其整合 我们以婴儿为中心的分析工具和皮质表面的地图集，以精确的映射为IBEAT 婴儿动态和复杂的皮质变化。与对婴儿大脑的现有工具不同，我们 将结合婴儿使用的工具，用于拓扑校正，表面重建，注册，分析， 和测量。我们将进一步整合配备有 基于生长轨迹的拟合。在AIM 4中，我们将大大增强Ibeat 软件功能以及通过系统外展和培训的用户支持。 最后，我们将采用IBEAT来处理BCP的所有成像数据，并将发布两个IBEAT 软件包和处理后的BCP数据通过NITRC向公众公开。