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: 10471785
• 负责人: Andrew Edward Anderson
• 金额: $ 56.5万
• 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 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10471785
• 关键词: 3-Dimensional; Adoption; Algorithms; 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; 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).

项目总结 组织的形态和力学特征是理解 人体的发育、动态平衡和病理学。在上一次资助期间，我们 发展了统计形状建模(SSM)方法，并将其应用于结构性髋部疾病的研究。 我们还开发了将SSM与有限元(FE)分析相结合的初始框架，以使研究成为可能 形状和力学的结合。如果结合到临床实践中，SSM和FE分析可以确定 可能导致损伤、重塑或修复的解剖学特征。SSM和FE所需的几何图形 模型通常是通过分割体积成像数据来生成的。这一步可能会非常缓慢， 容易出错，成本高昂，这阻碍了这些计算技术的临床应用。我们 我创造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DepSSM应用于 通过区分结构性髋关节疾病在解剖学上的差异，提高临床对其认识 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 最能说明疾病的情况，从而指导诊断的改进。计算技术的进步已经 简化了生成患者特定FE模型的过程，从而实现了以临床为重点的研究。然而， 没有框架来统一可视化、比较和解释(即，后处理)来自多个 有限元模型。目前，主题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， 解释并统计分析多个受试者和研究小组的FE结果。我们将映射FE结果 到使用对应模型表示统计上有意义的分布的合成解剖学。 将应用统计参数图通过统计测试来保存解剖细节。我们会 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，体积图像提供了一种 以熟悉的格式提供给医生的丰富信息。然而，还没有可用的工具来解释 模型数据与来自体积图像的临床发现。在目标3中，我们将开发评估方法 通过综合成像收集的形态、力学和临床表现之间的关系 使用标准本体的统计测试和半自动医学图像标注工具。 髋关节的定量CT和MRI图像，用于评估骨密度和软骨超微结构， 将分别作为测试数据集进行评估。为了产生广泛的影响，我们将传播我们的方法来 社区是开源软件，它将调用现有开源软件提供的核心功能 拥有庞大用户基础的软件(FEBio、ShapeWorks)。