The absence of detailed knowledge regarding mechanical loading on knee structures inhibits our understanding of joint degeneration and injury. Information on the interrelationships between muscle activations and tissue response is crucial to the development of tissue engineered cartilage and menisci, meniscus and ligament injury and repair, and our understanding of degenerative joint disease, specifically osteoarthritis. Personalized prediction of joint and tissue level loading during ambulation has the potential to significantly enhance orthopedic medicine. In addition to providing a greater understanding of knee biomechanics and tissue function, tools with this capability would enable subject specific intervention strategies aimed at modifying gait for targeted outcomes, such as reducing articular cartilage stress. The goal of this work is to translate computational and experimental methods developed on canines and human cadavers to patient specific multiscale musculoskeletal models that concurrently simulate muscle forces and tissue level loading. To our knowledge, no simulation tool exists that incorporates body level neuromusculoskeletal function with articular cartilage tissue level parameters within a concurrent and computationally efficient framework. Innovations that will be employed for this work include: Body level muscle driven forward dynamic simulation with a natural knee, Anatomical and functional representation of the menisci within the multibody framework, Discrete body representation of articular cartilage, Surrogate models that predict tissue level stress from multibody inputs, In vivo characterization of ligament bundle zero-load lengths, and Custom localizers that register the medical imaging coordinate system to the motion capture coordinate system.
The Specific Aims of this project are: 1.) Produce subject specific multiscale musculoskeletal models of the leg with anatomical knee models that include the menisci and simultaneous prediction of cartilage level stress on three healthy female subjects, and 2.) Run dynamic multiscale simulations that combine gait measurements, forward dynamics muscle force prediction and subject specific knee models. Simulation outputs will include tibio-femoral articular cartilage contact pressures and von mises stress, meniscus contact pressures and ligament loading during a dual limb squat, walking and side- step maneuver.

Public Health Relevance

The proposed work will translate previously developed computational and experimental methods to produce tools that predict patient specific loading on knee structures and tissues during ambulation. This technology would enable greater understanding of knee biomechanics and tissue function and enable personalized intervention strategies aimed at modifying gait to reduce stress on knee cartilage.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Academic Research Enhancement Awards (AREA) (R15)
Project #
1R15AR061698-01
Application #
8179962
Study Section
Special Emphasis Panel (ZRG1-DTCS-A (81))
Program Officer
Lester, Gayle E
Project Start
2011-08-01
Project End
2013-09-01
Budget Start
2011-08-01
Budget End
2013-09-01
Support Year
1
Fiscal Year
2011
Total Cost
$308,602
Indirect Cost
Name
University of Missouri Kansas City
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
010989619
City
Kansas City
State
MO
Country
United States
Zip Code
64110
Guess, Trent M; Razu, Swithin (2017) Loading of the medial meniscus in the ACL deficient knee: A multibody computational study. Med Eng Phys 41:26-34
Guess, Trent M; Razu, Swithin; Jahandar, Hamidreza (2016) Evaluation of Knee Ligament Mechanics Using Computational Models. J Knee Surg 29:126-37
Erdemir, Ahmet; Guess, Trent M; Halloran, Jason P et al. (2016) Commentary on the integration of model sharing and reproducibility analysis to scholarly publishing workflow in computational biomechanics. IEEE Trans Biomed Eng 63:2080-2085
Guess, Trent M; Razu, Swithin; Jahandar, Hamidreza et al. (2015) Predicted loading on the menisci during gait: The effect of horn laxity. J Biomech 48:1490-8
Kia, Mohammad; Stylianou, Antonis P; Guess, Trent M (2014) Evaluation of a musculoskeletal model with prosthetic knee through six experimental gait trials. Med Eng Phys 36:335-44
Guess, Trent M; Stylianou, Antonis P; Kia, Mohammad (2014) Concurrent prediction of muscle and tibiofemoral contact forces during treadmill gait. J Biomech Eng 136:021032
Lu, Yunkai; Pulasani, Palgun Reddy; Derakhshani, Reza et al. (2013) Application of neural networks for the prediction of cartilage stress in a musculoskeletal system. Biomed Signal Process Control 8:475-482