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: 8725738
• 负责人: Dinggang Shen
• 金额: $ 50.63万
• 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/8725738
• 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(申请人提供)：人脑经历了一个动态的发展阶段，在生命的第一年，结构和功能都迅速增长。洞察这一发育的关键时期对于理解精神疾病的神经发育起源是至关重要的，因为与 精神病和其他主要精神疾病通常发生在胎儿或新生儿的早期阶段。最近可获得的婴儿神经成像数据使精确描述这一时期的发育模式变得越来越可行。然而，由于以下挑战，专门用于此目的的计算工具仍然很少：(1)婴儿扫描 (2)受SCN时间的限制，扩散加权图像的信噪比通常较低；(3)快速髓鞘形成过程导致不同大脑的图像对比度有显著差异 (4)为成人大脑分析开发的技术不能直接移植到婴儿身上。该项目肩负着一项具有挑战性的任务，即在创建高精度计算工具方面克服重要的技术障碍，这些工具将在#年的第一年自动量化大脑发育 生活。在目标1中，我们将创建一个4D多模式引导、基于水平集的框架，用于同时分割和配准从出生到一岁的连续脑部扫描。这将允许通过从早期时间点(2周)和/或稍后的时间点(1年)扫描借用多模式信息来更有效地分割低对比度图像(例如，等强度的3个月和6个月扫描)。在目标2中，我们将创建一种4D皮质表面重建方法，以实现跨不同时间点的一致表面重建。这将有助于缓解由于图像对比度低而在曲面重建过程中由于结构模糊而产生的不精确。在目标3中，我们将创建一个基于集群的分层组织的注册框架，该框架将利用图像群体的各种解剖变异来有效地注册婴儿大脑。这将有助于将结构差异较大的图像有效地配准到一个公共空间，用于基于人群的早期大脑发育研究。在目标4中，我们将使用一种新的基于面片的稀疏表示技术来创建每个时间点的婴儿大脑的超分辨率地图集。当这些图谱被用作大脑注册的模板时，由于它们的结构清晰度显著提高，将导致显著的性能改进。所有创建的工具和超分辨率地图集将被集成到一个专门的婴儿大脑分析软件包中，并免费提供给研究界。