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（由申请人提供）：人类大脑在生命的第一年经历了一个动态的发育阶段，具有快速的结构和功能增长。深入了解这一发展的关键时期对于理解精神疾病的神经发育起源至关重要，因为大脑的改变与 精神病和其他主要的精神疾病通常发生在胎儿或新生儿生命的早期。最近可用的婴儿神经影像学数据是越来越可行的精确表征的发展模式在这段时间。然而，由于以下挑战，专用于此目的的计算工具仍然很少：（1）婴儿扫描 由于脑体积较小，空间分辨率明显较低;（2）受扫描时间的限制，弥散加权图像可达到的信噪比通常较低;（3）快速髓鞘形成过程导致不同脑的图像对比度显著变化 区域，这很容易混淆现有的计算方法;（4）为成人大脑分析开发的技术不能直接转移到婴儿身上。该项目肩负着克服重要技术障碍的挑战性任务，创造高精度的计算工具，将在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多模态引导的，基于水平集的框架，用于同时分割和注册从出生到一岁的连续脑扫描。这将允许低对比度图像（例如，等强度3个月和6个月扫描），以便通过从早期时间点（2周）和/或后期时间点（1年）扫描中借用多模态信息来更有效地分割。在目标2中，我们将创建一种4D皮质表面重建方法，用于在不同时间点进行一致的表面重建。这将有助于减轻由于低图像对比度而在表面重建过程中产生的结构模糊性的不精确性。在目标3中，我们将创建一个基于聚类的分层组织配准框架，该框架将利用图像群体的解剖学变化的多样性来有效地配准婴儿大脑。这将有助于有效地注册的图像与大的结构差异，以一个共同的空间，以人口为基础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补丁的稀疏表示技术在每个时间点为婴儿大脑创建超分辨率地图集。这些地图集，当用作大脑配准的模板时，由于其显著改善的结构清晰度，将导致显着的性能改善。所有创建的工具和超分辨率地图集将被集成到一个专用的婴儿大脑分析软件包中，并免费提供给研究界。