SHAPE OPTIMIZING DIFFEOMORPHISMS FOR COMPUTATIONAL BIOLOGY

计算生物学的形状优化微分形

• 批准号: 8363477
• 负责人: JAMES C GEE
• 金额: $ 2.03万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8363477
• 关键词: Algorithms; Atlases; Behavioral; Brain Mapping; Collaborations; Communities; Computational Biology; Data; Data Set; Databases; Diagnosis; Disease; Educational process of instructing; Environment; Funding; Genetic; Genotype; Goals; Grant; Image Analysis; Individual; Injury; Knowledge; Longevity; Maps; Medical Imaging; Methodology; Methods; Mind; Modeling; National Center for Research Resources; Nerve Degeneration; Neuroanatomy; Pathology; Phenotype; Population; Principal Investigator; Probability; Reproducibility; Research; Research Infrastructure; Resolution; Resources; Sampling; Shapes; Source; Specificity; Structure; System; Techniques; Time; United States National Institutes of Health; Variant; Work; age effect; cohort; computational anatomy; computerized tools; cost; design; neuroimaging; normal aging; novel; pathological aging; research clinical testing; spatiotemporal; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. This proposal will contribute a new collaboration for implementing rigorous spatiotemporal medical image analysis in a large scale computing environment. The system will dramatically enhance the neuroimaging community's quantitative understanding of normal and pathological aging and correlated variables. Medical images capture the changes that occur in an individual over time and samples the range of anatomical and functional differences visible in a population lifespan. Our goal is to associate these differences with causes, for example, innate population variability, injury, pathology, or the effects of genotype on phenotype. The recently proposed Diffeomorphometry (DM) system quantifies and relates these variables to an optimal spatiotemporal coordinate system. This novel technique allows the atlas to evolve in time along with the population to statistically capture effects of age, disease or other factors. This common, evolving map space gives a wealth of prior knowledge, allowing one to build probabilities describing ranges and types of variation in shape and function. These aggregate population attributes may then be studied and visualized, used in research, as well as teaching and diagnosis. Our DM method is designed with the axioms of symmetry (the algorithms must be symmetric) and specificity (the analysis should be optimal in the study space) in mind and with the ability to automatically generate database-specific atlases. The rigorous and symmetric definition of change given by DM captures differences in neuroanatomy with superlative accuracy, reproducibility and high level of detail. Consequently, a DM study maximizes the information extracted from a neuroimaging cohort, especially when correlated with, for instance, genetic or behavioral variables. Furthermore, DM satisfies pressing research needs in neuroimaging: DM derives optimal atlases from arbitrarily sized databases and gives large deformation optimization of anatomical correspondence, through landmark and statistical guidance. The resources in the UCLA Center for Computational Biology (CCB) will allow these methods to be applied on the large datasets they were designed for and at an unprecedented resolution and scale. The proposed work has three distinctive aims: collaboration, methodology and clinical evaluation/application: Instantiate a collaboration between UPenn and UCLA, where data and algorithms are shared and disseminated via the Center for Computational Biology; Develop Diffeomorphometry into a cutting-edge, large-scale, publicly available computational tool; Evaluate and refine the developed methodology, as well as compare with CCB brain mapping tools, on neuroimaging studies of structure-function associations under neurodegenerative conditions

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的主要研究者可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 该提案将为在大规模计算环境中实施严格的时空医学图像分析做出新的合作。该系统将极大地增强神经影像学界对正常和病理性衰老及相关变量的定量理解。医学图像捕捉个体随时间发生的变化，并对人群生命周期中可见的解剖和功能差异范围进行采样。我们的目标是将这些差异与原因联系起来，例如先天群体变异、损伤、病理或基因型对表型的影响。最近提出的微分形态测量（DM）系统量化了这些变量并将其与最佳时空坐标系相关联。这项新技术使图谱能够随着人口的变化而及时发展，以统计方式捕捉年龄、疾病或其他因素的影响。这种共同的、不断发展的地图空间提供了丰富的先验知识，使人们能够建立描述形状和功能变化的范围和类型的概率。然后可以对这些总体人口属性进行研究和可视化，用于研究以及教学和诊断。我们的 DM 方法在设计时考虑了对称性公理（算法必须是对称的）和特异性（分析应该在研究空间中是最佳的），并且能够自动生成特定于数据库的图集。 DM 给出的变化的严格且对称的定义以最高的准确性、可重复性和高细节水平捕捉了神经解剖学的差异。因此，DM 研究最大化了从神经影像队列中提取的信息，尤其是与遗传或行为变量相关时。此外，DM 满足了神经影像学领域紧迫的研究需求：DM 从任意大小的数据库中导出最佳图集，并通过界标和统计指导对解剖对应关系进行大变形优化。加州大学洛杉矶分校计算生物学中心 (CCB) 的资源将使这些方法能够以前所未有的分辨率和规模应用于其设计的大型数据集。拟议的工作有三个独特的目标：合作、方法论和临床评估/应用：实例化宾夕法尼亚大学和加州大学洛杉矶分校之间的合作，通过计算生物学中心共享和传播数据和算法；将微分形态学发展成为尖端的、大规模的、公开可用的计算工具；评估和完善所开发的方法，并与 CCB 脑图工具进行比较，用于神经退行性疾病条件下结构-功能关联的神经影像研究