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: 8687698
• 负责人: Lilla Zollei
• 金额: $ 24.2万
• 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 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8687698
• 关键词: Adult; Age; Algorithmic Software; 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; Geometry; 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.

儿科结构和弥散磁共振数据的年龄相关分析技术 项目概述：该研究提案旨在开发一种新的表示和计算工具 使我们能够更好地了解人脑的发育。像以前一样，这种方法的需求量很大 引入的成人脑分析技术要么不完全用于这种目的，要么不直接 可转移给婴儿。考虑到在生命的头两年，神经解剖学发生了巨大的变化，我们 建议将年龄明确纳入我们的定量图像分析工具。我们将定义并构建一个 四维脑图谱，将总结正常婴儿S的中枢趋势和随时间的变化。 这份图集随后将被用来比较对照组和早产受试者的组，并描述 病态发育过程。我们还将介绍计算工具，这些工具可能包含 将这种图集中的信息转换为基于模板的分割和配准算法。前者 协助分配图像中的解剖标签，后者依赖于先前积累的图像 在新观测数据之间建立空间对应时的统计。作为最快的时期 髓鞘形成发生在生命的头两年，关于白质的信息将对我们的 技巧。因此，我们将在很大程度上依赖扩散加权磁共振图像来补充结构图像 信息。在奖项的指导阶段，根据年龄的发展中的代表 大脑将依靠神经发育假说和从 婴儿数据集。在独立阶段，将介绍专门用于图像处理的工具 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 扰乱了大脑的发育。这个项目与候选人的长期职业目标是一致的 建立具有竞争力和独立性的人脑定量建模研究计划 通过分析多模式医疗收购的发展。该项目还将促进候选人的 短期目标是在儿科神经科学和儿科磁共振成像方面变得知识渊博。这个 这项工作的指导阶段将在麻省理工学院/哈佛/麻省理工学院马蒂诺斯生物医学中心进行 应聘者将利用尖端的成像设备、成像专业知识，如 以及其合作机构的世界级教育机会。她的职业发展计划 包括儿科神经解剖学、磁共振物理学方面的培训；神经科学方面的课程和参与 在研讨会和科学会议上。