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: 0427464
• 负责人: David Keyes
• 金额: --
• 依托单位: Columbia University
• 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 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0427464&HistoricalAwards=false
• 关键词: ITR; Collaborative; Research; ASE; sim+dmc

Abstract for Collaboration0427985, 0427464, 0427094,0427912,0427695A multidisciplinary team of researchers from Argonne NationalLaboratory, Carnegie Mellon University, Columbia University,University of Chicago, Emory University, and University ofPennsylvania, with collaborators from the Universities of Graz andLubek, will initiate a long term research project on image-driven,inversion-based biophysical modeling. The team includes expertise innumerical algorithms and scientific computing, fluid and solidbiomechanics, PDE optimization, inverse problems, medical imageanalysis and processing, and distributed and grid computing necessaryto tackle this class of problems.This project aims to create a framework for assimilating multimodaldynamic medical image data to produce highly-resolved,physically-realistic, patient-specific biomechanics models. While thecomputational and algorithmic aspects of the project are widelyapplicable, the target application will be the construction ofpatient-specific cardiac biomechanics models from 4D image datasets ofheart motion. Such models are useful for medical diagnosis andsurgical planning. This places a premium on quick turnaround of thecomputations, which mean they must be fast, scalable, and capable ofexploiting grid-based computing.Research will focus on three key areas that undergird the project'soverall goals: registration, inversion, and distributed computing. Theregistration research component will create multilevel algorithms toextract cardiac deformation histories from time-varying medical imagedatasets via the solution of sequences of 3D image registrationproblems. The inversion research component will develop multilevelalgorithms that use these deformation field histories as virtualobservations to solve inverse problems for cardiac biomechanicalparameters. The distributed computing research component will createtools for performance prediction and resource scheduling that supportsimulations across distributed computational resources.Dovetailing with the research components, the project will undertakean educational program designed to communicate the fruits of its workand of the wider benefits of the integration of the biomedicalsciences, computing sciences, and computational sciences, to a moregeneral audience of students, disciplinary researchers, and the laypublic. The professional activities of the team members in theinversion, image registration, grid computing, and computationalscience communities will be parlayed to organize workshops andinternational meetings, edit volumes, teach summer schools, developuniversity and short courses, and engage in outreach activities---asthey have done in the past---but with greater emphasis on the field ofcomputational biomedicine. The proposed image-based cardiacbiomechanics modeling application will provide an excellentopportunity to demonstrate the benefits to health and welfare thatadvances in optimization-based registration and inversion algorithmsand 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小组与来自格拉兹大学和卢贝克大学的合作者将启动一个关于图像驱动、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，用于吸收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是广泛适用的，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计划是有用的。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。研究将集中在支撑该项目所有目标的三个关键领域：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，从时变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测来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提供性能预测和资源调度工具，支持跨分布式计算资源的模拟。与研究部分相配合，该项目将开展一项教育计划，旨在向更广泛的学生群体传达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好处，学科研究人员和外行人。团队成员在反演、图像配准、网格计算和计算科学领域的专业活动将被用于组织研讨会和国际会议、编辑卷册、教授暑期学校、开发大学和短期课程，以及从事外展活动-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，基于优化的配准和反演算法和网格计算的进步可以提供。