Rapid personalized computational modeling of the wrist

Area of Science:

• Biomechanics
• Medical Imaging
• Computational Science

Background:

• Developing computational models for hand and wrist injuries is time-consuming and requires significant expertise.
• Existing models often lack the ability to vary material properties, limiting their personalization.
• There is a need for efficient methods to create patient-specific computational models for injury research.

Purpose of the Study:

• To develop an automated workflow for creating personalized finite element models of the hand and wrist.
• To demonstrate the utility of these personalized models in investigating clinical questions related to joint injury.
• To enable faster creation of patient-specific models for future research and treatment development.

Main Methods:

• Combined morphing with algorithmic techniques to automate the creation of personalized finite element models.
• Utilized four-dimensional computed tomography (4DCT) data to generate three personalized hand and wrist models.
• Employed the models for ligament property calibration and Monte Carlo analysis of ligament injury impacts.

Main Results:

• Successfully created three personalized finite element models using the automated workflow.
• Demonstrated the models' ability to investigate ligament property calibration and the effects of ligament injury on joint contact pressure.
• Achieved rapid model creation (2 hours) and simulation times (45 seconds).

Conclusions:

• The automated workflow significantly reduces the time and expertise needed to create personalized finite element models.
• These patient-specific models are valuable tools for investigating clinical questions and understanding joint injury mechanisms.
• The publicly available data, models, and code promote reproducibility and facilitate future research in computational biomechanics.