ITR: Collaborative Research - ASE - (sim+dmc): Image-based Biophysical Modeling: Scalable Registration and Inversion Algorithms and Distributed Computing

ITR：协作研究 - ASE - (sim dmc)：基于图像的生物物理建模：可扩展配准和反演算法以及分布式计算

• 批准号: 0427912
• 负责人: William Gropp
• 金额: --
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0427912&HistoricalAwards=false
• 关键词: ITR; Collaborative; Research; ASE; sim+dmc

Abstract for Collaboration 0427985, 0427464, 0427094,0427912,0427695 A multidisciplinary team of researchers from Argonne National Laboratory, Carnegie Mellon University, Columbia University, University of Chicago, Emory University, and University of Pennsylvania, with collaborators from the Universities of Graz and Lubek, will initiate a long term research project on image-driven, inversion-based biophysical modeling. The team includes expertise in numerical algorithms and scientific computing, fluid and solid biomechanics, PDE optimization, inverse problems, medical image analysis and processing, and distributed and grid computing necessary to tackle this class of problems. This project aims to create a framework for assimilating multimodal dynamic medical image data to produce highly-resolved, physically-realistic, patient-specific biomechanics models. While the computational and algorithmic aspects of the project are widely applicable, the target application will be the construction of patient-specific cardiac biomechanics models from 4D image datasets of heart motion. Such models are useful for medical diagnosis and surgical planning. This places a premium on quick turnaround of the computations, which mean they must be fast, scalable, and capable of exploiting grid-based computing. Research will focus on three key areas that undergird the project's overall goals: registration, inversion, and distributed computing. The registration research component will create multilevel algorithms to extract cardiac deformation histories from time-varying medical image datasets via the solution of sequences of 3D image registration problems. The inversion research component will develop multilevel algorithms that use these deformation field histories as virtual observations to solve inverse problems for cardiac biomechanical parameters. The distributed computing research component will create tools for performance prediction and resource scheduling that support simulations across distributed computational resources. Dovetailing with the research components, the project will undertake an educational program designed to communicate the fruits of its work and of the wider benefits of the integration of the biomedical sciences, computing sciences, and computational sciences, to a more general audience of students, disciplinary researchers, and the lay public. The professional activities of the team members in the inversion, image registration, grid computing, and computational science communities will be parlayed to organize workshops and international meetings, edit volumes, teach summer schools, develop university and short courses, and engage in outreach activities---as they have done in the past---but with greater emphasis on the field of computational biomedicine. The proposed image-based cardiac biomechanics modeling application will provide an excellent opportunity to demonstrate the benefits to health and welfare that advances in optimization-based registration and inversion algorithms and Grid computing can provide.

摘要协作0427985,0427464,0427094,0427912,0427695一个由来自阿贡国家实验室、卡内基梅隆大学、哥伦比亚大学、芝加哥大学、埃默里大学和宾夕法尼亚大学的多学科研究人员组成的团队，以及来自格拉茨大学和卢贝克大学的合作者，将启动一个关于图像驱动的、基于反演的生物物理模型的长期研究项目。该团队包括数值算法和科学计算、流体和固体生物力学、PDE优化、反问题、医学图像分析和处理以及解决这类问题所需的分布式和网格计算方面的专业知识。该项目旨在创建一个框架，用于同化多模式动态医学图像数据，以产生高度分辨率、物理逼真、患者特定的生物力学模型。虽然该项目的计算和算法方面被广泛应用，但目标应用将是从心脏运动的4D图像数据集构建特定于患者的心脏生物力学模型。这样的模型对医学诊断和外科手术规划很有用。这将重点放在计算的快速周转上，这意味着它们必须是快速的、可伸缩的，并且能够利用基于网格的计算。研究将集中在支撑该项目总体目标的三个关键领域：注册、反转和分布式计算。配准研究部分将创建多层算法，通过解决3D图像配准问题的序列，从时变的医学图像数据集中提取心脏变形历史。反演研究部分将开发多层次算法，将这些形变场历史用作虚拟观测，以解决心脏生物力学参数的反问题。分布式计算研究组件将创建支持跨分布式计算资源的模拟的性能预测和资源调度工具。与研究部分相匹配，该项目将开展一项教育计划，旨在向更广泛的学生、学科研究人员和普通公众传达其工作成果以及生物医学、计算科学和计算科学整合的更广泛好处。团队成员在反演、图像配准、网格计算和计算科学界的专业活动将被用于组织研讨会和国际会议、编辑卷、教授暑期课程、开发大学和短期课程，并参与外联活动-就像他们过去所做的那样-但更加重视计算生物医学领域。拟议的基于图像的心脏生物力学建模应用程序将提供一个极好的机会来展示基于优化的配准和反演算法以及网格计算的进步对健康和福利的好处。