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 项目概述：本研究计划旨在开发一种新颖的表示和计算工具， 更好地了解人类大脑的发育。这种方法的需求量很大， 为成人大脑分析引入的技术对于这样的目的是不完整的，或者不是直接的。 可转移到婴儿身上。考虑到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。我们将定义并构建一个 四维脑图谱，将总结正常婴儿随时间的中枢倾向和变化。 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。我们还将介绍计算工具， 将来自这样的图谱的信息转换为基于模板的分割和配准算法。前 有助于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，作为最迅速的时期 髓鞘的形成发生在生命的头两年，关于白色物质的信息对我们来说是无价的。 技术.因此，我们将在很大程度上依赖于扩散加权MR图像，以补充结构图像 信息.在该奖项的指导阶段，发展中国家的年龄代表 大脑将依赖于神经发育假设和多模态图像采集来构建， 婴儿数据集。在独立阶段，将介绍专门用于 设计用于婴儿数据，我们的新模型将用于描述和比较正常 扰乱大脑发育该项目符合候选人的长期职业目标， 建立一个有竞争力的和独立的研究计划，在定量建模人脑 通过对多模式医疗收购的分析，该项目还将促进候选人的 短期目标是了解儿科神经科学和儿科MR成像。的 这项工作的指导阶段将在MGH/哈佛/麻省理工学院马蒂诺斯生物医学中心进行 成像，候选人将利用尖端的成像设施、成像专业知识，例如 以及其合作机构提供的世界级教育机会。她的职业发展计划 包括儿科神经解剖学、MR物理学、神经科学课程和参与 在研讨会和科学会议上。

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.