Development of Subject Specific Computational Methodology to Study Knee Biomechanics

研究膝关节生物力学的学科特定计算方法的发展

• 批准号: RGPIN-2018-03971
• 负责人: Chandrashekar, Naveen
• 金额: $ 2.33万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712443
• 关键词: Development; Subject; Specific; Computational; Methodology

Osteoarthritis affects about 30 million North Americans and costs around $200 billion per year to treat it. A recent report found that more than 50% of the population affected by knee osteoarthritis are less than 65 years old. Ligament and meniscal injury during athletic activities result in knee osteoarthritis within 10 years of the injury. Since no cure exists for osteoarthritis, prevention of knee injury is the only way to prevent post-traumatic osteoarthritis. However, a clear understanding of the risk factors associated with knee injuries is lacking. Further, there exists no reliable method to assess a specific subject with respect to their knee injury risk. Investigating the external and anatomic risk factors associated with knee injuries is challenging. Deformation in knee tissues cannot be measured through in-vivo methods. In-vitro specimens cannot be loaded multiple times for parametric studies. Creating subject-specific computational models is laborious. An easily implementable methodology to assess knee injury risk in individuals is needed to prevent knee injuries. The goal of this research program is to develop a computational methodology to better understand the knee injury risk factors. The objectives include development and validation of a technique to build subject-specific finite element (FE) model of human knee, which could be used to screen a specific subject for knee injury risk. Further, a validated FE model library will be developed to study the extrinsic and anatomic risk factors associated with knee injury. The program makes use of the unique in-vivo/in-silico/in-vitro method and the unique dynamic knee simulator system pioneered in our lab. A novel mesh-morphing technique will be applied to build 10 distinct FE models of cadaver knees from their MRI scans. The material properties of the tissues will be calibrated to match knee kinematics during experimental loading. These models will be validated using our unique dynamic knee simulator system. This method can then be applied to create individual FE models for live persons. The models will be used to study the effect of individual geometric parameters and tissue properties on knee biomechanics and to build a risk factor model. The proposed research will result in a validated methodology that could be used to screen individuals (ex:aspiring athletes) for knee injury risk. Additionally the library of experimentally validated distinct FE models will be used to perform parametric study on intrinsic/extrinsic factors and to develop knee injury risk factor models. This approach will provide evidence-based outcomes which could potentially lower the incidence of early osteoarthritis, reduce health care costs and increase the quality of life. The research program offers the HQP opportunity to work on unique research techniques in interdisciplinary areas of biomedical engineering.

骨关节炎影响了大约3000万北美人，每年的治疗费用约为2000亿美元。最近的一份报告发现，超过50%的膝关节骨关节炎患者年龄在65岁以下。在运动中韧带和半月板损伤可在损伤后10年内导致膝关节骨性关节炎。由于无法治愈骨关节炎，预防膝关节损伤是预防创伤后骨关节炎的唯一途径。