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

合作摘要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分布式和网格计算方面的专业知识，这些都是解决这类问题所必需的。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，以生成高分辨率、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计划是有用的。这意味着计算的快速周转非常重要，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级别算法，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，使用这些形变场历史作为虚拟观测来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模拟的性能预测和资源调度工具。与研究组件一起，该项目将承担一个教育项目，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和生物医学、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-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注册和反转算法和网格计算的进步可以为健康和福利带来的好处。

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.