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来自阿尔波尼国家实验室，卡内基·梅隆大学，哥伦比亚大学，哥伦比亚大学，芝加哥大学，埃默里大学，埃默里大学和宾夕法尼亚大学的跨越培养基的跨学科研究人员的多学科研究人员基于图像驱动的基于反转的生物物理建模的项目。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，逆问题，医疗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的专业知识。该项目旨在创建一个框架，以吸收多模式动态医学图像数据，以产生高度分辨，身体现实，患者特定的生物力学模型。尽管该项目的计算和算法方面广泛适用，但目标应用将是从心脏运动的4D图像数据集中构建患者特定的心脏生物力学模型。这样的模型可用于医学诊断和手术计划。这对计算的快速周转进行了溢价，这意味着它们必须快速，可扩展并且能够利用基于网格的计算。研究将重点关注该项目总体目标的三个关键领域：注册，反转和分布式计算。注册研究组件将创建多级算法，以通过随时间变化的医学图像数据集提取心脏变形历史，该算法通过3D图像注册问题的序列解决方案。反演研究组件将开发多级算法，这些算法使用这些变形场历史作为虚拟观察，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用于性能预测和资源计划的工具，以支持分布式计算资源的模拟。该项目将与研究组成部分相吻合，旨在将生物医学科学，计算科学和计算科学融合的更广泛益处传达给更普遍的学生，学科研究人员和外行公众。团队成员在倒置，图像注册，电网计算和计算科学社区的专业活动将被选为组织研讨会和国际会议，编辑卷，教暑期学校，发展大学和短期课程，并从事外展活动，并从事宣传活动 - 但在过去的情况下，他们更加强调了计算机化的领域。提出的基于图像的心脏生物力学建模应用程序将提供一个绝佳的机会，以证明对健康和福利的好处，以基于优化的注册和反转算法和网格计算可以提供。