Statistical Shape Modelling in Ankle Osteoarthritis

Osteoarthritis (OA) is a degenerative joint disease causing pain and disability affecting weight-bearing joints. Unlike knee and hip OA, ankle OA is linked to trauma and remains under-researched despite its impact on mobility and quality of life. Joint kinematics play a key role in OA progression, as altered movement patterns accelerate cartilage degeneration by changing localized stress. Computational techniques like Discrete Element Analysis (DEA) estimate cartilage stress using accurate joint morphology, but cartilage geometry acquisition is limited by MRI’s cost and accessibility.

This project addresses this challenge using Statistical Shape Modeling (SSM) to predict cartilage distribution from bone shape. SSM identifies 3D geometric patterns across populations, linking tibia and talus bone shapes to cartilage geometry via statistical regression models. High-resolution CT scans and MRI datasets will train the SSM to predict cartilage thickness and distribution, bypassing the need for direct soft tissue imaging.

The predicted cartilage geometry will integrate into DEA models, enabling simulations of joint mechanics. By combining SSM and DEA, this research offers new insights into ankle OA progression, improving diagnosis, personalized treatments, and outcomes. This multimodal approach bridges a critical gap in understanding the interplay between bone shape and cartilage health in OA.

Faculty Supervisor:

Koren Roach

Student:

Partner:

University of Utah

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

University of Calgary

Program: