权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

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
项目类别：
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

项目摘要

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.

儿童结构和弥散MRI数据的年龄相关分析技术