The absence of detailed knowledge regarding mechanical loading on knee structures inhibits ourunderstanding of joint degeneration and injury. Information on the interrelationships between muscle activationsand tissue response is crucial to the development of tissue engineered cartilage and menisci, meniscus andligament 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 significantlyenhance orthopaedic medicine. In addition to providing a greater understanding of knee biomechanics andtissue function, tools with this capability would enable subject specific intervention strategies aimed atmodifying gait for targeted outcomes, such as reducing articular cartilage stress. The goal of this work is totranslate computational and experimental methods developed on canines and human cadavers to patientspecific 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 witharticular 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 simulationwith 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 frommultibody inputs, In vivo characterization of ligament bundle zero-load lengths, and Custom localizers thatregister the medical imaging coordinate system to the motion capture coordinate system.
The Specific Aims ofthis project are: 1.) Produce subject specific multiscale musculoskeletal models of the leg with anatomical kneemodels that include the menisci and simultaneous prediction of cartilage level stress on three healthy femalesubjects, and 2.) Run dynamic multiscale simulations that combine gait measurements, forward dynamicsmuscle force prediction and subject specific knee models. Simulation outputs will include tibio-femoral articularcartilage contact pressures and von mises stress, meniscus contact pressures and ligament loading during adual limb squat, walking and side-step maneuver.

Public Health Relevance

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

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Academic Research Enhancement Awards (AREA) (R15)
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Special Emphasis Panel (ZRG1-DTCS-A (81))
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Lester, Gayle E
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University of Missouri-Columbia
Other Health Professions
Schools of Allied Health Profes
United States
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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