Age-Dependent Analysis Techniques for Pediatric Structural and Diffusion MRI Data

儿科结构和扩散 MRI 数据的年龄相关分析技术

• 批准号: 8495516
• 负责人: Lilla Zollei
• 金额: $ 24.9万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-05 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8495516
• 关键词: Adult; Age; Algorithms; Anatomy; Atlases; Award; Brain; Brain imaging; Childhood; Collaborations; Control Groups; Data; Data Set; Development; Development Plans; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Four-dimensional; Goals; Growth; Health; Human; Human Development; Image; Image Analysis; Infant; Institution; Investigation; Label; Life; Literature; MRI Scans; Magnetic Resonance; Magnetic Resonance Imaging; Measures; Medical; Mentors; Methods; Modality; Modeling; Nature; Neuroanatomy; Neurons; Neurosciences; Outcome; Pattern; Phase; Physics; Population; Premature Birth; Process; Property; Research; Research Proposals; Structure; Techniques; Technology; Testing; Time; Tissues; Training; Underserved Population; Variant; Weight; Work; age related; base; bioimaging; brain morphology; career; career development; computerized tools; design; experience; image processing; infancy; longitudinal analysis; myelination; neurodevelopment; neuroimaging; novel; programs; regional difference; statistics; symposium; tool; water diffusion; white matter

Age-Dependent Analysis Techniques for Pediatric Structural and Diffusion MRI Data Project summary: This research proposal aims to develop a novel representation and computational tools that enable a better understanding of human brain development. Such methods are in high demand as previously introduced techniques for adult brain analysis are either incomplete for such purposes or are not directly transferable to infants. Given the dramatic changes in neuroanatomy during the first two years of life, we propose to explicitly incorporate age into our quantitative image analysis tools. We will define and construct a four dimensional brain atlas that will summarize central tendencies and variations over time in normal infant s. This atlas will then be used to compare groups of control and prematurely born subjects and describe pathological development processes. We will also introduce computational tools that may incorporate information from such an atlas into a template-based segmentation and registration algorithm. The former assists in assigning anatomical labels in the images and the latter relies on previously accumulated image statistics when establishing spatial correspondences between newly observed data. As the most rapid period of myelination occurs in the first two years of life, information about the white matter will be invaluable for our techniques. We will therefore rely heavily on diffusion weighted MR images to compliment structural image information. During the mentored phase of the award, an age-dependent representation of the developing brain will be constructed relying on neuro-developmental hypothesis and multi-modal image acquisitions from infant data sets. In the independent phase, image processing tools will be introduced that are specifically designed to work with infant data and our new model will be used to describe and compare normal and disrupted brain development. This project is consistent with the long-term career goal of the candidate which is to establish a competitive and independent research program in quantitatively modeling human brain development by the analysis of multi-modal medical acquisitions. The project will also facilitate the candidate's short-term goal of becoming knowledgeable in pediatric neuroscience and pediatric MR imaging. The mentored phase of this work is to be performed at the MGH/Harvard/MIT Martinos Center for Biomedical Imaging where the candidate will take advantage of the cutting-edge imaging facilities, imaging expertise, as well as the world-class educational opportunities at its collaborating institutions. Her career development plan includes training in pediatric neuroanatomy, the physics of MR; coursework in neuroscience and participation in seminars and scientific conferences.

小儿结构和扩散MRI数据的年龄依赖性分析技术 项目摘要：该研究建议旨在开发一种新颖的表示和计算工具 使人们能够更好地了解人类脑发育。这种方法的需求量很高 引入成人大脑分析的技术要么不完整，要么不是直接 可转移给婴儿。鉴于在生命的头两年中神经解剖学发生了巨大变化，我们 建议将年龄明确纳入我们的定量图像分析工具。我们将定义并构建 四个维度的大脑图集将总结正常婴儿的中心趋势和随着时间的变化。 然后，该地图集将用于比较控制和过早出生的主题的组并描述 病理发展过程。我们还将介绍可能合并的计算工具 从此类地图集到基于模板的分割和注册算法的信息。前者 协助在图像中分配解剖标签，后者依赖于先前累积的图像 统计数据在新观察到的数据之间建立空间对应关系时。作为最快的时期 髓鞘形成发生在生命的头两年中，有关白质的信息对于我们来说将是无价的 技术。因此，我们将严重依靠扩散加权MR图像来补充结构图像 信息。在奖项的指导阶段，发展的代表 大脑将依靠神经发展假设和多模式图像获取来构建大脑 婴儿数据集。在独立阶段，将介绍特定的图像处理工具 旨在使用婴儿数据，我们的新模型将用于描述和比较正常， 大脑发育破坏。该项目与候选人的长期职业目标是一致的 在定量建模人脑中建立竞争性和独立的研究计划 通过分析多模式医学采集来开发。该项目还将促进候选人的 在小儿神经科学和小儿MR成像方面知识渊博的短期目标。这 这项工作的指导阶段将在MGH/Harvard/MIT Martinos生物医学中心进行 候选人将利用尖端成像设施，成像专业知识的成像，作为 以及其合作机构的世界一流的教育机会。她的职业发展计划 包括小儿神经解剖学的培训，MR的物理学；神经科学和参与课程 在研讨会和科学会议上。