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: 0849301
• 负责人: William Gropp
• 金额: $ 8.53万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-11-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0849301&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.

合作摘要0427985、0427464、0427094、0427912、0427695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团队，以及来自格拉兹大学和卢贝克大学的合作者，将启动一个长期的研究项目，图像驱动，基于反演的生物物理建模。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，逆问题，医学图像分析和处理以及解决此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，用于同化多模态动态医学图像数据，以产生高分辨率，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具有广泛的适用性，但目标应用将是从心脏运动的4D图像数据集构建患者特定的心脏生物力学模型。这些模型对于医学诊断和手术规划是有用的。这就要求计算的快速周转，这意味着它们必须是快速的、可扩展的，并且能够利用基于网格的计算。研究将集中在三个关键领域，巩固该项目的总体目标：注册，反演和分布式计算。配准研究组件将创建多级算法，通过解决3D图像配准问题序列，从随时间变化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级算法，使用这些变形场的历史作为虚拟观察，以解决心脏生物力学参数的反演问题。分布式计算研究部分将创建用于性能预测和资源调度的工具，以支持跨分布式计算资源的模拟。该项目将与研究组成部分密切相关，将开展一项教育计划，旨在将其工作成果以及生物医学科学，计算科学和计算科学整合的更广泛利益传达给学生，学科研究人员和普通公众。团队成员在反演，图像配准，网格计算和计算科学社区的专业活动将被用于组织研讨会和国际会议，编辑卷，教授暑期学校，开发大学和短期课程，并从事推广活动-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很好的机会，以证明健康和福利的好处，在基于优化的配准和反演算法和网格计算的进步可以提供。