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: 7892749
• 负责人: Lilla Zollei
• 金额: $ 13.63万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-07 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7892749
• 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; Positioning Attribute; Premature Birth; Process; Property; Public Health; Publications; Research; Resources; Scheme; 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; improved; infancy; longitudinal analysis; myelination; neurodevelopment; neuroimaging; novel; programs; regional difference; statistics; symposium; tool; water diffusion; white matter

PROJECT SUMMARY (Provided by Applicant): This application 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, the candidate proposes to explicitly incorporate age into her quantitative image analysis tools. She will define and construct a four dimensional brain atlas that will summarize central tendencies and variations over time in normal infants. This atlas will then be used to compare groups of control and prematurely born subjects and describe pathological development processes. The candidate 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 among 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 this technique. The candidate 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 neurodevelopmental 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 the 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.

项目摘要（由申请人提供）：该应用程序旨在开发一种新颖的表示和计算工具，以更好地理解人类大脑的发育。 此类方法的需求量很大，因为之前引入的成人大脑分析技术对于此类目的要么不完整，要么不能直接应用于婴儿。 鉴于生命头两年神经解剖学的巨大变化，候选人建议将年龄明确纳入她的定量图像分析工具中。 她将定义并构建一个四维大脑图谱，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。 候选人还将介绍计算工具，这些工具可以将来自此类图谱的信息合并到基于模板的分割和配准算法中。 前者有助于在图像中分配解剖标签，后者在新观察到的数据之间建立空间对应关系时依赖于先前积累的图像统计数据。 由于髓鞘形成最快的时期发生在生命的头两年，因此有关白质的信息对于这项技术来说非常宝贵。 因此，候选人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，将根据神经发育假说和从婴儿数据集中采集的多模态图像来构建发育中大脑的年龄相关表示。 在独立阶段，将引入专门设计用于处理婴儿数据的图像处理工具，新模型将用于描述和比较正常和中断的大脑发育。 该项目与候选人的长期职业目标一致，即通过分析多模式医疗采购来建立一个有竞争力的独立研究计划，对人脑发育进行定量建模。 该项目还将促进候选人实现了解儿科神经科学和儿科 MR 成像知识的短期目标。 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成像中心进行，候选人将利用该中心的尖端成像设施、成像专业知识以及世界一流的教育机会其合作机构。 她的职业发展计划包括儿科神经解剖学、磁共振物理学的培训；神经科学课程以及参加研讨会和科学会议。