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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

基本信息

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
项目类别：
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

项目摘要

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