SI2:SSE-Collaborative Research: Advanced Software Infrastructure for Biomechanical Inverse Problems

SI2：SSE 合作研究：生物力学反问题的先进软件基础设施

• 批准号: 1148124
• 负责人: Paul Barbone
• 金额: $ 24.05万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1148124&HistoricalAwards=false
• 关键词: SI2; SSE; Collaborative; Research; Advanced

Biomechanical imaging refers to the remote measurement of the mechanical properties of tissues, in-situ and in-vivo. Images of the tissue can be thus created by visualizing the mechanical property distributions. This technique relies on imaging tissue while it is deformed by a set of externally applied forces. Through image processing, the displacement field everywhere in the region of interest is inferred. An inverse problem for the relevant mechanical properties is then solved, given the measured displacement fields, an assumed form of the tissue's constitutive equation, and the law of conservation of momentum. Images of reconstructed parameters find applications in the detection, diagnosis and treatment monitoring of disease, and in designing patient specific models for surgical training and planning. Over the last decade we have developed a software package (NLACE) to solve this inverse problem eciently in different application domains. Through this award we will make enhancements to NLACE that will make it easier to utilize and modify, and extend its user base to a wider community.The specific tasks for enhancing NLACE can be divided into two categories: (a) Steps to transition from a working prototype of NLACE to a software resource for the community. These include establishing an Input/Output standard for NLACE, creation of a GUI for input data and for monitoring the progress of the solution, hosting NLACE distributions, and creating sets of test data and documentation for its release. (b) Tasks that would enhance the functional capability of NLACE. These include the creation of a user-defined hyperelastic material model module to address a large class of tissue and material types, parallelization of NLACE on distributed memory, shared memory and GPU platforms, and quantifying uncertainty in the spatial distribution of the reconstructed parameters. We will measure our progress through user-feedback obtained during annual validation tests performed by a committed focus user-group that will test all aspects of the proposed research.The proposed improvements to NLACE will further its application in the detection, diagnosis and treatment monitoring of diseases, generation of patient-specic models for surgical planning and image-guidance applications, and studies in biomechanics and mechanobiology. The parallel and uncertainty quantification strategies developed for NLACE can be applied to a broad class of inverse problem with PDE constraints, including acoustic and electromagnetic scattering, seismic inversion, diffuse optical tomography, aquifer permeability and thermometry. Our outreach plans ensure the dissemination of NLACE to our focus group and to a broader community through hosting on the Simtk NIH center website. Finally, two graduate students will be trained in the elds of computational science and mathematics and biomechanics, and results from the proposed research will be presented at conferences on computational and biomedical science and engineering.

生物力学成像是指在原位和活体范围内远程测量组织的力学性能。因此，可以通过可视化机械性能分布来创建组织的图像。这项技术依赖于成像组织，而它是在一组外力作用下变形的。通过图像处理，推断出感兴趣区域内各处的位移场。然后，在给定测量的位移场、组织本构方程的假定形式和动量守恒定律的情况下，解决相关力学属性的反问题。重建参数的图像在疾病的检测、诊断和治疗监测中得到应用，并在设计用于外科培训和规划的患者特定模型中得到应用。在过去的十年里，我们开发了一个软件包(NLACE)来解决不同应用领域的逆问题。通过这个奖项，我们将对NLACE进行改进，使其更易于使用和修改，并将其用户基础扩展到更广泛的社区。增强NLACE的具体任务可分为两类：(A)从NLACE的工作原型过渡到社区的软件资源的步骤。其中包括为NLACE建立输入/输出标准，为输入数据和监控解决方案的进度创建一个图形用户界面，托管NLACE发行版，以及为其发布创建一组测试数据和文档。(B)将加强国家法律援助中心职能能力的任务。其中包括创建用户定义的超弹性材料模型模块以解决一大类组织和材料类型，在分布式内存、共享内存和GPU平台上并行化NLACE，以及量化重建参数的空间分布中的不确定性。我们将通过由一个承诺的Focus用户小组进行的年度验证测试中获得的用户反馈来衡量我们的进展，该用户小组将测试拟议研究的所有方面。对NLACE的拟议改进将进一步应用于疾病的检测、诊断和治疗监测，为手术计划和图像引导应用生成患者专用模型，以及生物力学和机械生物学研究。为NLACE开发的并行和不确定性量化策略可以应用于具有偏微分方程约束的一类广泛的反问题，包括声波和电磁散射、地震反演、扩散光学层析成像、含水层渗透率和温度测量。我们的外展计划确保通过在Simtk NIH中心网站上的托管，将NLACE传播到我们的焦点小组和更广泛的社区。最后，两名研究生将接受计算科学、数学和生物力学领域的培训，拟议研究的结果将在计算、生物医学科学和工程会议上公布。