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: 8614974
• 负责人: Russell Takeshi Shinohara
• 金额: $ 37.34万
• 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 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8614974
• 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; Heterogeneity; Hospitals; Human; Image; Image Analysis; Imagery; Incidence; Journals; Lesion; Machine Learning; Magnetic Resonance Imaging; Medical; Medical Imaging; Methodology; Methods; Modeling; 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; design; falls; imaging Segmentation; imaging modality; member; neuroimaging; next generation; open source; public health relevance; skills; tool; white matter

Abstract 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.

摘要 医学影像是基础科学和临床实践的基石。发现新的 疾病的机制和标记物及其对临床实践的重要影响， 大型多中心成像研究正在获取TB级的复杂多模式 几十年的横截面和纵向成像数据。 由于这类研究的复杂性，对数据的统计分析是具有挑战性的 获取的成像数据的结构和超高维。此外， 解剖、病理和成像方案的异质性导致不稳定和 许多当前最先进的图像分析方法都失败了。这项拨款建议 通过生物医学成像研究人群的统计框架，可扩展和 用于识别和准确量化病理的可靠方法，以及 病因学横断面和纵向检查的分析工具 疾病的发展。 这些技术将被应用于解决激励大型和多企业的关键目标 约翰斯中心对多发性硬化症和阿尔茨海默病的研究 霍普金斯医院，国家神经疾病和中风研究所，以及 环球网。该项目将创造发现和量化脑损伤的方法。 病理、发病和轨迹。根据这笔赠款开发的方法将成为目标 这些神经成像目标，但将形成统计图像分析的基础 在生物医学科学中广泛适用的方法。