Infant Brain Measurement and Super-Resolution Atlas Construction

婴儿大脑测量和超分辨率图谱构建

• 批准号: 8583365
• 负责人: Dinggang Shen
• 金额: $ 58.38万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-26 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8583365
• 关键词: 1 year old; 2 year old; Address; Adult; Age-Years; Algorithms; Atlases; Birth; Birth Intervals; Brain; Brain imaging; Brain region; Brain scan; Communities; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Development; Diffusion Magnetic Resonance Imaging; Diffusion weighted imaging; Due Process; Environment; Future; Gray unit of radiation dose; Growth; Housing; Human; Image; Infant; Informatics; Knowledge; Lead; Life; Magnetic Resonance Imaging; Maps; Measurement; Measures; Medical Staff; Methods; Neonatal; Neurodevelopmental Disorder; Noise; Pattern; Performance; Phase; Population; Process; Psychotic Disorders; Public Health; Research; Resolution; Resources; Scanning; Shapes; Shoulder; Signal Transduction; Structure; Surface; Techniques; Time; Tissues; Variant; Weight; base; brain size; computerized tools; critical period; fetal; follow-up; image registration; imaging Segmentation; imaging modality; improved; insight; multimodality; myelination; neonate; neuroimaging; novel; population based; public health relevance; reconstruction; stem; tool; white matter change

DESCRIPTION (provided by applicant): The human brain undergoes a dynamic phase of development with rapid structural and functional growth in the first year of life. Insight into thi critical period of development is of paramount importance for understanding the neurodevelopmental origins of psychiatric illness, since brain alterations that are associated with psychosis and other major psychiatric illnesses often occur early during fetal or neonatal life. The recent availability of infant neuroimaging data is making increasingly feasible the precise characterization of development patterns in this period of time. However, computational tools that are dedicated to this purpose are still rare due to the following challenges: (1) Infant scans suffer from significantly lower spatial resolution due to the smaller brain size; (2) Limited by scn time, the achievable signal-to-noise ratio for diffusion-weighted images is typically low; (3) The rapid myelination process results in significant variation of image contrast across different brain regions, which can easily confuse existing computational methods; (4) Techniques developed for adult brain analysis are not directly transferable to infants. This project shoulders the challenging task of overcoming important technological hurdles in creating high- precision computational tools that will automate the quantification of brain development in the first year of life. In Aim 1, we will create a 4D multimodality-guided, level-set-based framework for simultaneous segmentation and registration of serial brain scans acquired from birth to one year of age. This will allow low-contrast images (e.g., the isointense 3- and 6-month scans) to be segmented more effectively by borrowing multimodality information from early time-point (2-week) and/or later time-point (1-year) scans. In Aim 2, we will create a 4D cortical surface reconstruction method for consistent surface reconstruction across different time points. This will help alleviate the imprecision stemming from structural ambiguities in the surface reconstruction process due to low image contrast. In Aim 3, we will create a clustering-based hierarchically organized registration framework that will harness the manifold of anatomical variation of the image population for effective registration of infant brains. This will aid effectve registration of images with large structural differences to a common space for population-based early brain development studies. In Aim 4, we will create super-resolution atlases for infant brains at each time point by using a novel patch-based sparse representation technique. These atlases, when used as templates for brain registration, will lead to significant performance improvement due to their significantly improved structural clarity. All created tools and super-resolution atlases will be integrated into a dedicated infant-brain-analysis software package and made freely available to the research community.

描述（由申请人提供）：人脑在生命的第一年中经历了动态发展阶段，结构和功能增长迅速。洞悉至关重的发展时期对于理解精神病的神经发育起源至关重要，因为与之相关的大脑改变 精神病和其他主要的精神病经常发生在胎儿或新生儿生活中。婴儿神经影像数据的最新可用性使得越来越可行的是在此期间的开发模式的精确表征。但是，由于以下挑战，专门用于此目的的计算工具仍然很少见：（1）婴儿扫描 由于大脑尺寸较小，因此空间分辨率明显降低； （2）受SCN时间的限制，扩散加权图像的可实现的信噪比通常很低； （3）快速的髓鞘化过程导致不同大脑之间的图像对比显着变化 区域很容易混淆现有的计算方法； （4）用于成人大脑分析的技术无法直接转移到婴儿。该项目应涵盖克服重要技术障碍的具有挑战性 生活。在AIM 1中，我们将创建一个4D多模式引导的，基于级别的基于级别的框架，以同时对从出生到一岁的串行脑扫描进行分割和注册。这将允许通过从早期时间点（2周）和/或较晚的时间点（1年）扫描中借用多模式信息来更有效地分割低对比度图像（例如，3个月和6个月的均值扫描）。在AIM 2中，我们将创建一个4D皮层表面重建方法，以跨不同时间点进行一致的表面重建。这将有助于减轻由于图像对比度低而导致的表面重建过程中结构性歧义所造成的不精确。在AIM 3中，我们将创建一个基于聚类的层次结构组织的注册框架，该框架将利用图像群体的解剖学变异的多种形式来有效地注册婴儿的大脑。这将有助于影响基于人群早期大脑发展研究的共同空间的结构差异的图像的注册。在AIM 4中，我们将使用一种新型的基于贴片的稀疏表示技术在每个时间点创建婴儿大脑的超分辨率地图。当将这些图像用作大脑注册的模板时，由于其结构清晰度显着提高，将导致大幅提高性能。所有创建的工具和超分辨率地图集都将集成到专用的婴儿 - 脑分析软件包中，并免费提供给研究社区。