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

描述：医学影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横截面和纵向收集数十年来TB级的复杂多模式成像数据。由于所获得的成像数据的复杂结构和超高维，对来自这类研究的数据进行统计分析是具有挑战性的。此外，解剖、病理和成像方案的异质性导致了许多当前最先进的图像分析方法的不稳定和失败。这笔赠款提出了通过生物医学成像研究人口的统计框架，用于识别和准确量化病理的可扩展和稳健的方法，以及用于病因学和疾病进展的横断面和纵向检查的分析工具。这些技术将被应用于解决约翰·霍普金斯医院、国家神经疾病和中风研究所以及全球范围内对多发性硬化症和阿尔茨海默病进行的激励性大型和多中心研究的关键目标。该项目将创建揭示和量化脑损伤病理、发病率和轨迹的方法。根据这笔赠款开发的方法将针对这些神经成像目标，但将形成在生物医学科学中广泛应用的统计图像分析方法的基础。

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.