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: 0427985
• 负责人: Omar Ghattas
• 金额: --
• 依托单位: Carnegie-Mellon 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 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0427985&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.

协作摘要0427985，0427464，0427094,0427912,0427695A来自阿尔波尼·纳瓦尔（Argonne nationallaboratory），卡内基·梅尔（Carnegie Mellaboratory）的多学科研究人员，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，埃默里大学，埃默里大学，埃默里大学，及其途径，及其合作培训。关于图像驱动的，基于反演的生物物理建模的术语研究项目。该团队包括无知的算法和科学计算，流动性和固体力学，PDE优化，逆问题，医学成像分析和处理以及分布式和网格计算所需的问题来解决这类问题。该项目旨在创建一个框架，以创建一个框架，以吸收多个动态的医学图像模型，以使其具有性能性化度为机械，并具有高度差异化的型号。虽然该项目的计算和算法方面是可广泛应用的，但目标应用程序将是从HEART MOTION的4D图像数据集中构建特定于患者的心脏生物力学模型。这样的模型可用于医学诊断和外科计划。这对造成计算的快速转变进行了溢价，这意味着它们必须快速，可扩展且能够探索基于网格的计算。研究将重点关注三个关键领域，这些领域是项目“提出目标：注册，倒置和分布计算”的三个关键领域。该研究组件将通过3D图像登记问题的序列解决时变化的医学成像算术来创建多级算法，从而从时变的医学成像中提取心脏变形历史。反转研究组件将开发多级别的载体，这些多级别的载体将这些变形场历史记录用作虚拟观察，以解决心脏生物力学参数的反问题。分布式的计算研究组件将用于绩效预测和资源调度的著作，这些计划支持分布式计算资源跨越分布式的计算资源。依次进行研究，该项目将承担旨在传达其工作的成果，旨在传达其旨在传达生物医学和计算科学和计算科学界，更多计算机科学和计算学家的综合效果的成果的教育计划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.拟议的基于图像的心脏动力学建模应用程序将提供出色的企业，以证明可以提供基于优化的注册和反转算法和网格计算的健康和福利的好处。