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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 项目由计算机和信息科学局高级网络基础设施处、数学和物理科学局物理处生命系统物理学处、地球科学局大气和地球空间科学处物理和动态气象学以及综合活动办公室 INSPIRE 项目共同资助。科学仪器和计算硬件和软件的进步导致了前所未有的能力获取、模拟和可视化时间分辨的三维 (3D) 数据量，为我们更好地理解以前超出我们技术掌握的复杂系统提供了希望。然而，随着这些数据的规模和复杂性的增加，对它们的分析变得越来越困难，阻碍了科学发现并限制了花费大量金钱和精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实时获取大脑活动的功能性 MRI (FMRI) 体积，而移动多普勒雷达 (MDR) 系统能够获取龙卷风形成期间雷暴的时间相关体积图像。在这个题为体积数据时空过程的定量估计（QUANTITIVE Estimation of Space-Time process in Volumetric Data，QUEST）的项目中，加州大学圣地亚哥分校成像科学计算中心（CSCI）与恶劣天气研究中心（CSWR）和气象卫星研究合作研究所（CIMSS）合作，将开发一个新的框架，用于分析时变 3D 体积，以大比例尺为指导。数值模拟，调查我们这个时代的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、可视化和探索复杂的时变体积数据集的能力。该 INSPIRE 项目开发了先进的方法，用于自动定量表征嵌入在基于 3D 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，这是一种有效的概率方法，采用信息场理论方法，并结合数值方法提供使用团队的熵谱路径理论提供的先验信息。模拟设计都将结果限制为物理上可实现的解决方案。这种跨学科方法重点关注神经科学和恶劣天气气象学各自领域的两个突出问题：1）从高分辨率解剖MRI、扩散张量MRI、人类连接组项目的功能MRI数据中识别人脑的结构和功能模式，并结合扩散和功能加权MRI信号的数值模拟；2）龙卷风特征的识别来自多普勒轮子网络的 MDR 数据的起源和维护以及使用 CM1 模型的龙卷风模拟。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告之间的准备时间的能力，将产生重大的社会影响。更一般地说，这种新颖的方法有可能改变在广泛学科中进行分析的方式，通过自动定量检测当前传统技术无法检测到的大型复杂数据集中的重要（尽管可能是微妙的）变化。