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Updated: Nov 26, 2025

Imaging of the Microstructural Failure Mechanism in the Human Hip
Published on: September 29, 2023
Characterization and finite element validation of transchondral strain in the human hip during static and dynamic
Jocelyn N Todd1, Alexandra N Allan1, Travis G Maak2
1Department of Biomedical Engineering, and Scientific Computing and Imaging Institute University of Utah, Salt Lake City, UT 84112, United States.
Abstract:
Distribution of strain through the thickness of articular cartilage, or transchondral strain, is highly dependent on the geometry of the joint involved. Excessive transchondral strain can damage the solid matrix and ultimately lead to osteoarthritis. Currently, high-resolution transchondral strain distribution is unknown in the human hip. Thus, knowledge of transchondral strain patterns is of fundamental importance to interpreting the patterns of injury that occur in prearthritic hip joints. This study had three main objectives. We sought to 1) quantify high-resolution transchondral strain in the native human hip, 2) determine differences in transchondral strain between static and dynamic loading conditions to better understand recovery and repressurization of cartilage in the hip, and 3) create finite element (FE) models of the experimental testing to validate a modeling framework for future analysis. The transchondral strain patterns found in this study provide insight on the localization of strain within cartilage of the hip. Most notably, the chondrolabral junction experienced high tensile and shear strain across all samples, which explains clinical data reporting it as the most common region of damage in cartilage of the hip. Further, the representative FE framework was able to match the experimental static results and predict the dynamic results with very good agreement. This agreement provides confidence for both experimental and computational measurement methods and demonstrates that the specific anisotropic biphasic FE framework used in this study can both describe and predict the experimental results.
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