Encoding scratch and scrape features for wear modeling of total joint replacements

Area of Science:

• Biomaterials Science
• Mechanical Engineering
• Orthopedic Surgery

Background:

• Total joint replacements (TJRs) face wear and damage on bearing surfaces.
• Traditional surface roughness parameters inadequately quantify TJR damage for wear prediction models.
• Microscopic scratches and scraping are common TJR surface damage features.

Purpose of the Study:

• To develop and report algorithms for quantifying microscopic surface damage on TJR components.
• To enable the incorporation of quantified damage features into predictive wear models.
• To analyze retrieved TJR components and simulate wear based on observed damage.

Main Methods:

• Utilized a diffused lighting technique for global visualization and automatic segmentation of implant surface damage.
• Employed high-resolution optical profilometry (OP) areal scans to quantify microscopic damage features.
• Developed algorithms to encode imaged damage data for finite element (FE) wear simulations.

Main Results:

• Successfully quantified a range of microscopic damage features on retrieved femoral heads from clinically failed implants.
• Demonstrated the capability to input quantified damage into FE wear simulations.
• Presented illustrative results from polyethylene wear computations based on analyzed damage.

Conclusions:

• The developed diffused lighting and OP-based method effectively quantifies TJR surface damage.
• This quantification enables more accurate FE wear simulations, improving prediction of polyethylene wear.
• The findings support enhanced understanding and modeling of wear in total joint replacements.