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INSPIRE: Quantitative Estimation of Space-Time Processes in Volumetric Data (QUEST)

INSPIRE：体积数据中时空过程的定量估计 (QUEST)

基本信息

批准号：
1550405
负责人：
Lawrence Frank
金额：
$ 99.96万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-01-01 至 2019-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1550405&HistoricalAwards=false
关键词：
INSPIRE Quantitative Estimation Space Time

项目摘要

This INSPIRE project is jointly funded by the Division of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science, Physics of Living Systems in the Division of Physics in the Directorate for Math and Physical Science, Physical and Dynamic Meteorology in the Division of Atmospheric and Geospace Sciences in the Directorate for Geoscience, and the INSPIRE program in the Office of Integrative Activities.Advances in scientific instrumentation and computational hardware and software have resulted in an unprecedented ability to acquire, simulate, and visualize time resolved three-dimensional (3D) volumes of data, offering the promise of a greater understanding of complex systems previously beyond our technical grasp. However, as the size and complexity of these data increase, analyzing them becomes increasingly problematic, inhibiting scientific discovery and limiting the utility of the data acquired at great expense and effort. Two particularly cogent examples come from two seemingly disparate scientific fields: neuroscience and meteorology. Magnetic resonance imaging (MRI) scanners can now acquire functional MRI (FMRI) volumes of brain activity in almost real time, while mobile Doppler radar (MDR) systems are capable of acquiring time-dependent volumetric images of thunderstorms during tornado formation. In this project, entitled QUantitative Estimation of Space-Time processes in volumetric data (QUEST), the University of California, San Diego, Center for Scientific Computation in Imaging (CSCI), in partnership with the Center for Severe Weather Research (CSWR) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS) will develop a novel framework for the analysis of time-varying 3D volumes, guided by large scale numerical simulations, to investigate two of the outstanding scientific questions of our age: What is the relationship between brain structure and function?, and How do strong, long-track tornadoes form? The resulting computational platform will be disseminated to the NSF community through the open source analysis and visualization platform (STK) to improve the ability of researchers to quantitatively analyze, visualize, and explore complex time varying volumetric datasets. This INSPIRE project develops advanced methods for automated quantitative characterization of subtle space-time patterns embedded within spatio-temporal data from 3D voxel-based digital imaging modalities based upon the team's recently formulated entropy field decomposition (EFD) theory, a probabilistic method efficiently that employs the information field theoretic approach with prior information supplied using the team's entropy spectrum pathways theory, in conjunction with numerical simulations designed both to constrain results to physically realizable solutions. The cross-disciplinary approach focuses on two outstanding problems in the respective fields of neuroscience and severe weather meteorology: 1) The identification of structural and functional modes of the human brain from high resolution anatomical MRI, diffusion tensor MRI, functional MRI data from the Human Connectome Project combined with numerical simulations of diffusion and functionally weighted MRI signals, and 2) The identification of signatures of tornado genesis and maintenance from MDR data from the Doppler-On-Wheels network in conjunction with tornado simulations using the CM1 model. Significant social impact would result from the ability to categorize states of brain activity in normal and diseased populations and the ability to reduce the lead time between tornado formation and warning to threatened populations. More generally, this novel methodology has the potential to transform the way analysis is conducted in a wide range of disciplines by enabling automated, quantitative detection of important, though perhaps subtle, variations in large, complex datasets undetectable by current traditional techniques.

该INSPIRE项目由计算机信息科学局高级网络基础设施处、&数学和物理科学局物理学处生命系统物理学、地球科学局大气和地球空间科学处物理和动力气象学以及综合活动办公室INSPIRE计划共同资助。科学仪器和计算硬件和软件的进步导致了前所未有的获取、模拟、和可视化时间分辨三维（3D）数据量，提供了对以前超出我们技术掌握的复杂系统的更好理解的承诺。然而，随着这些数据的规模和复杂性的增加，分析它们变得越来越成问题，抑制了科学发现，并限制了花费巨大代价和努力获得的数据的效用。两个特别有说服力的例子来自两个看似完全不同的科学领域：神经科学和气象学。磁共振成像（MRI）扫描仪现在几乎可以真实的实时获取功能性MRI（FMRI）脑活动的体积，而移动的多普勒雷达（MDR）系统能够在龙卷风形成期间获取雷暴的时间依赖性体积图像。在这个项目中，题为定量估计时空过程的体积数据（QUEST），加州大学圣地亚哥分校，科学计算成像中心（CSCI），与中心的合作伙伴关系恶劣天气研究（CSWR）和气象卫星研究合作研究所（CIMSS）将开发一个新的框架，分析随时间变化的三维体积，在大规模数值模拟的指导下，研究我们这个时代两个突出的科学问题：大脑结构和功能之间的关系是什么？以及强而长的龙卷风是如何形成的由此产生的计算平台将通过开源分析和可视化平台（STK）传播给NSF社区，以提高研究人员定量分析，可视化和探索复杂时变体积数据集的能力。这个INSPIRE项目开发了先进的方法，用于自动定量表征嵌入在基于三维体素的数字成像模式的时空数据中的微妙时空模式，该方法基于团队最近制定的熵场分解（EFD）理论，这是一种有效利用信息场理论方法的概率方法，该方法使用团队的熵谱路径理论提供先验信息，结合数值模拟，设计两者以将结果约束为物理上可实现的解决方案。跨学科的方法侧重于神经科学和灾害性天气气象学各自领域的两个突出问题：1）从来自人类连接组计划的高分辨率解剖MRI、扩散张量MRI、功能MRI数据结合扩散和功能加权MRI信号的数值模拟来识别人脑的结构和功能模式，2）从来自多普勒-车轮网络的MDR数据中识别龙卷风发生和维持的特征，并结合使用CM 1模式进行的龙卷风模拟。对正常人群和患病人群的大脑活动状态进行分类的能力，以及缩短龙卷风形成和向受威胁人群发出警报之间的间隔时间的能力，将产生重大的社会影响。更一般地说，这种新的方法有可能改变分析的方式进行了广泛的学科，使自动化，定量检测的重要，虽然可能是微妙的，变化的大型复杂的数据集无法检测到目前的传统技术。