Computational and Statistical Framework to Model Tissue Shape and Mechanics

组织形状和力学建模的计算和统计框架

• 批准号: 10612478
• 负责人: Andrew Edward Anderson
• 金额: $ 55.13万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612478
• 关键词: 3-Dimensional; Adoption; Algorithms; Anatomic Models; Anatomy; Architecture; Bone Density; Cardiology; Cartilage; Characteristics; Charge; Clinical; Communities; Computational Technique; Computer Models; Computer software; Computing Methodologies; Data; Data Set; Deformity; Development; Diagnosis; Disease; Elements; Finite Element Analysis; Foundations; Funding; Geometry; Glean; Grooming; Hip Joint; Hip region structure; Homeostasis; Human Pathology; Human body; Image; Individual; Injury; Intuition; Libraries; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mechanics; Medical Imaging; Methodology; Methods; Modeling; Monitor; Morphology; Neurology; Ontology; Orthopedics; Pathology; Patient imaging; Patients; Performance; Physicians; Procedures; Process; Publishing; Quantitative Evaluations; Research; Resources; Scheme; Shapes; Structure; System; Techniques; Technology; Testing; Tissue Model; Tissues; Training; Validation; Visualization; X-Ray Computed Tomography; annotation system; base; clinical application; clinical practice; convolutional neural network; cost; data modeling; disease diagnosis; improved; in vivo; machine learning algorithm; neural network; novel; open source; predictive modeling; preservation; repaired; shape analysis; simulation; three-dimensional modeling; tool

PROJECT SUMMARY The morphologic and mechanical characteristics of a tissue are fundamental to understanding the development, homeostasis, and pathology of the human body. During the previous period of funding, we developed statistical shape modeling (SSM) methods and applied these to the study of structural hip disease. We also developed the initial framework to integrate SSM with finite element (FE) analysis to enable the study of shape and mechanics together. If incorporated into clinical practice, SSM and FE analysis could identify features of the anatomy likely responsible for injury, remodeling, or repair. Geometry needed for SSM and FE models is typically generated by segmentation of volumetric imaging data. This step can be painstakingly slow, error prone, and cost prohibitive, which hampers clinical application of these computational techniques. We have created a deep machine learning algorithm ‘DeepSSM’ that uses a convolutional neural network to establish the correspondence model directly from unsegmented images. In Aim 1 we will apply DepSSM to improve clinical understanding of structural hip disease by characterizing differences in anatomy between symptomatic and asymptomatic individuals; these morphometric comparisons will identify anatomic features most telling of disease, thereby guiding improvements in diagnosis. Computational advancements have simplified the process to generate patient-specific FE models, enabling clinically focused research. However, there is no framework to collectively visualize, compare, and interpret (i.e., post-process) results from multiple FE models. Currently, inter-subject comparisons require oversimplifications such as averaging results over subjectively defined regions. In Aim 2 we will develop new post-processing methods to collectively visualize, interpret and statistically analyze FE results across multiple subjects and study groups. We will map FE results to synthetic anatomies representing statistically meaningful distributions using the correspondence model. Statistical parametric mapping will be applied to preserve anatomic detail through statistical testing. We will use our published FE models of hip joint mechanics as the test system. Finally, volumetric images provide a wealth of information that is delivered to physicians in a familiar format. Yet, tools are not available to interpret model data with clinical findings from volumetric images. In Aim 3, we will develop methods that evaluate relationships between shape, mechanics, and clinical findings gleaned from imaging through integrated statistical tests and semi-automatic medical image annotation tools that utilize standard ontologies. Quantitative CT and MRI images of the hip, which estimate bone density and cartilage ultrastructure, respectively, will be evaluated as test datasets. To impart broad impact, we will disseminate our methods to the community as open source software that will call core functionality provided by existing, open source software that has a large user base (FEBio, ShapeWorks).

项目概要 组织的形态和机械特征是理解组织的基础 人体的发育、稳态和病理学。在前期融资期间，我们 开发了统计形状模型（SSM）方法并将其应用于结构性髋关节疾病的研究。 我们还开发了将 SSM 与有限元 (FE) 分析相结合的初始框架，以支持研究 形状和力学结合在一起。如果纳入临床实践，SSM 和 FE 分析可以识别 可能导致损伤、重塑或修复的解剖学特征。 SSM 和 FE 所需的几何形状 模型通常是通过体积成像数据的分割生成的。这一步可能会非常缓慢， 容易出错且成本高昂，这阻碍了这些计算技术的临床应用。我们 创建了一种深度机器学习算法“DeepSSM”，它使用卷积神经网络来 直接从未分割的图像建立对应模型。在目标 1 中，我们将应用 DepSSM 来 通过表征之间的解剖差异来提高对结构性髋关节疾病的临床理解 有症状和无症状的个体；这些形态测量比较将识别解剖特征 最能说明疾病，从而指导诊断的改进。计算的进步已经 简化了生成患者特定有限元模型的过程，从而实现了临床重点研究。然而， 没有框架来集体可视化、比较和解释（即后处理）多个结果 有限元模型。目前，受试者间比较需要过度简化，例如对结果进行平均 主观定义的区域。在目标 2 中，我们将开发新的后处理方法来集体可视化， 解释和统计分析多个受试者和研究组的有限元结果。我们将映射有限元结果 使用对应模型来表示具有统计意义的分布的合成解剖结构。 将应用统计参数映射来通过统计测试保留解剖细节。我们将 使用我们发布的髋关节力学有限元模型作为测试系统。最后，体积图像提供了 以熟悉的格式向医生提供大量信息。然而，还没有工具可以解释 模型数据与体积图像的临床发现。在目标 3 中，我们将开发评估方法 通过整合从成像中收集的形状、力学和临床结果之间的关系 利用标准本体的统计测试和半自动医学图像注释工具。 髋部的定量 CT 和 MRI 图像，可估计骨密度和软骨超微结构， 分别将被评估为测试数据集。为了产生广泛的影响，我们将把我们的方法传播给 作为开源软件的社区将调用现有开源提供的核心功能 拥有大量用户群的软件（FEBio、ShapeWorks）。