Multimodal image analysis and modeling of thin bone structures in the human skeleton

人体骨骼中薄骨结构的多模态图像分析和建模

• 批准号: RGPIN-2016-06543
• 负责人: Whyne, Cari
• 金额: $ 2.77万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668507
• 关键词: Multimodal; image; analysis; modeling; thin

Computer modeling of the human anatomy is a powerful approach to understanding structure and function. The finite element (FE) method enables representation of geometric and material complexities of the musculoskeletal system, specifically in models generated from 3D medical imaging data. Experimental validation has demonstrated that FE analysis can model musculoskeletal mechanical behaviour under simplified loading and boundary conditions. We have developed image processing methods that accurately reflect thin bone geometry and density necessary for specimen-specific FE modeling, despite resolution limitations imposed with clinical CT imaging. This, combined with highly automated workflows, has allowed the generation of multiple FE models that quantify the mechanical behaviour of these intricate structures. Yet, to represent physiologic behaviour of the human musculoskeletal anatomy requires complex load and boundary conditions. Further, understanding the mechanical behaviour of these structures is critical even after failure has occurred. This research seeks to integrate multimodal image analysis and FE modeling to accurately depict the mechanical behaviour of thin bone structures under complex physiologic loading and post fracture stability. In this work we propose to address the following specific aims:****1. To integrate multimodal information identified through medical imaging and musculoskeletal modeling simulations to yield an improved realization of the domain of physiologic load and boundary conditions in thin bone structures, specifically the craniomaxillofacial skeleton (CMFS) and pelvis.****2. To quantify the sensitivity of thin bone models to load and boundary conditions utilizing a Design of Experiments approach to recognize critical fracture risk scenarios.****3. To evaluate post yield stability in thin bone structures utilizing sequential image analysis and create constitutive models which can represent failure and compaction in FE analysis.****Ultimately this research aims to establish a robust experimentally validated platform that provides accurate representation of the physiological mechanical behaviour of human thin bone structures. This has important application to understanding the impact of injury and disease on thin bone structures, the utilization of new and existing technologies for their repair or regeneration and will motivate the continued use of FE modeling in biomedical research and design.**

人体解剖学的计算机建模是理解结构和功能的有力方法。有限元（FE）方法能够表示肌肉骨骼系统的几何和材料复杂性，特别是在3D医学成像数据生成的模型中。实验验证表明，有限元分析可以模拟简化载荷和边界条件下的肌肉骨骼力学行为。尽管临床CT成像存在分辨率限制，但我们已经开发了图像处理方法，可以准确反映样品特异性FE建模所需的薄骨几何形状和密度。这与高度自动化的工作流程相结合，可以生成多个有限元模型，量化这些复杂结构的力学行为。然而，要表征人体肌肉骨骼解剖的生理行为，需要复杂的载荷和边界条件。此外，即使在发生破坏之后，了解这些结构的力学行为也是至关重要的。本研究旨在整合多模态图像分析和有限元建模，以准确描述薄骨结构在复杂生理载荷和骨折后稳定性下的力学行为。在这项工作中，我们建议解决以下具体目标：****1。整合通过医学成像和肌肉骨骼建模模拟识别的多模态信息，以改善薄骨结构，特别是颅颌面骨骼（CMFS）和骨盆的生理负载和边界条件领域的实现。****2。利用实验设计方法来识别临界断裂风险情景，量化薄骨模型对载荷和边界条件的敏感性。****利用序列图像分析来评估薄骨结构屈服后的稳定性，并在有限元分析中创建可以代表破坏和压实的本构模型。****本研究的最终目的是建立一个强大的实验验证平台，提供人类薄骨结构的生理力学行为的准确表示。这对于理解损伤和疾病对薄骨结构的影响，利用新的和现有的技术进行修复或再生具有重要的应用价值，并将激励在生物医学研究和设计中继续使用有限元模型