Statistical methods for large and complex databases of ultra-high-dimensional

超高维大型复杂数据库的统计方法

• 批准号: 8890255
• 负责人: Russell Takeshi Shinohara
• 金额: $ 34.72万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-28 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8890255
• 关键词: Address; Alzheimer' s Disease; Anatomy; Applications Grants; Area; Attention deficit hyperactivity disorder; Basic Science; Behavior; Brain; Brain Pathology; Brain imaging; Clinical Management; Complex; Computer software; Computing Methodologies; Contrast Media; Data; Data Analyses; Databases; Development; Disease Marker; Disease Progression; Etiology; Failure; Goals; Grant; Health; Heterogeneity; Hospitals; Human; Image; Image Analysis; Incidence; Journals; Lesion; Machine Learning; Magnetic Resonance Imaging; Medical; Medical Imaging; Methodology; Methods; Modeling; Multicenter Studies; Multiple Sclerosis; National Institute of Neurological Disorders and Stroke; Pathology; Population Study; Positioning Attribute; Protocols documentation; Publishing; Research; Research Personnel; Resolution; Sampling; Scheme; Science; Site; Solutions; Statistical Data Interpretation; Statistical Methods; Statistical Models; Structure; Techniques; Technology; United States National Institutes of Health; Visualization software; Work; base; bioimaging; clinical practice; contrast enhanced; data visualization; design; falls; imaging Segmentation; imaging modality; member; neuroimaging; next generation; open source; skills; tool; white matter

DESCRIPTION: Medical imaging is a cornerstone of basic science and clinical practice. To discover new mechanisms and markers of disease and their crucial implications for clinical practice, large multi-center imaging studies are acquiring terabytes of complex multi-modality imaging data cross-sectionally and longitudinally over decades. The statistical analysis of data from such studies is challenging due to the complex structure of the imaging data acquired and the ultra-high dimensionality. Furthermore, the heterogeneity of anatomy, pathology, and imaging protocols causes instability and failure of many current state-of-the-art image analysis methods. This grant proposes statistical frameworks for studying populations through biomedical imaging, scalable and robust methods for the identification and accurate quantification of pathology, and analytic tools for the cross-sectional and longitudinal examination of etiology and disease progression. These techniques will be applied to address key goals of the motivating large and multi- center studies of multiple sclerosis and Alzheimer's disease conducted at Johns Hopkins Hospital, the National Institute of Neurological Disorders and Stroke, and across the globe. The project will create methods for uncovering and quantifying brain lesion pathology, incidence, and trajectory. Methods developed under this grant will be targeted towards these neuroimaging goals, but will form the basis for statistical image analysis methods applicable broadly in the biomedical sciences.

描述：医学成像是基础科学和临床实践的基石。为了发现疾病的新机制和标志物及其对临床实践的重要意义，大型多中心成像研究正在数十年来横向和纵向采集TB级复杂的多模态成像数据。由于所获取的成像数据的复杂结构和超高维度，来自此类研究的数据的统计分析具有挑战性。此外，解剖学、病理学和成像协议的异质性导致许多当前最先进的图像分析方法的不稳定性和失败。该补助金提出了通过生物医学成像研究人群的统计框架，用于识别和准确量化病理的可扩展和强大的方法，以及用于病因学和疾病进展的横截面和纵向检查的分析工具。这些技术将被应用于解决在约翰霍普金斯医院、国家神经疾病和中风研究所以及地球仪进行的多发性硬化症和阿尔茨海默病的激励性大型和多中心研究的关键目标。该项目将创建用于揭示和量化脑损伤病理学，发病率和轨迹的方法。根据该资助开发的方法将针对这些神经成像目标，但将构成广泛适用于生物医学科学的统计图像分析方法的基础。