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.

儿科结构和扩散 MRI 数据的年龄相关分析技术 项目摘要：本研究提案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大脑的发育。与以前一样，此类方法的需求量很大 引入的成人大脑分析技术对于此类目的要么不完整，要么不直接 可转移给婴儿。鉴于生命头两年神经解剖学的巨大变化，我们 建议将年龄明确纳入我们的定量图像分析工具中。我们将定义并构建一个 四维大脑图谱将总结正常婴儿随时间的中心趋势和变化。 然后，该图谱将用于比较对照组和早产受试者组并描述 病理发展过程。我们还将介绍可能包含以下内容的计算工具： 将此类图谱中的信息转化为基于模板的分割和配准算法。前者 协助在图像中分配解剖标签，后者依赖于先前积累的图像 在新观察到的数据之间建立空间对应关系时进行统计。作为最快时期 髓鞘形成发生在生命的头两年，有关白质的信息对于我们来说非常宝贵 技术。因此，我们将严重依赖扩散加权 MR 图像来补充结构图像 信息。在该奖项的指导阶段，发展中的年龄相关代表 大脑将根据神经发育假说和多模态图像采集来构建 婴儿数据集。在独立阶段，将引入专门针对图像处理工具 旨在处理婴儿数据，我们的新模型将用于描述和比较正常和 扰乱大脑发育。该项目与候选人的长期职业目标一致 建立一个有竞争力的独立研究项目，对人脑进行定量建模 通过对多模式医疗采购的分析进行开发。该项目还将促进候选人 短期目标是了解儿科神经科学和儿科 MR 成像。这 这项工作的指导阶段将在麻省总医院/哈佛大学/麻省理工学院马蒂诺斯生物医学中心进行 成像候选人将利用最先进的成像设施、成像专业知识，例如 以及其合作机构提供的世界一流的教育机会。她的职业发展规划 包括儿科神经解剖学、磁共振物理学方面的培训；神经科学和参与课程 在研讨会和科学会议中。