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Anisotropic computational modelling of bony structures from CT data: An almost automatic procedure.

Ilaria Toniolo¹, Claudia Salmaso², Giovanni Bruno³

¹The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Department of Industrial Engineering, University of Padova, Italy.

Computer Methods and Programs in Biomedicine

|January 18, 2020

Summary

This study presents an automated method using CT scans to create accurate patient-specific bone models for biomechanical analysis. This improves surgical planning and understanding of bone mechanics.

Keywords:

Bone mechanics CT data Micromechanical modelling Orthotropic elasticity

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Area of Science:

Computational biomechanics
Biomaterials science
Medical imaging analysis

Background:

Finite Element (FE) models are increasingly used in computational biomechanics for surgical planning and non-invasive prediction of stress and strain fields.
A key challenge in developing these models is the accurate characterization of bone's mechanical behavior, particularly its anisotropic properties.
Existing methods often lack the precision needed for patient-specific applications.

Purpose of the Study:

To define an automated procedure for generating patient-specific computational models of bony structures.
To incorporate the actual anisotropic response of bone tissue into these models.
To improve the accuracy of biomechanical predictions for surgical planning and analysis.

Main Methods:

The procedure integrates computed tomography (CT) data with bone tissue micromechanics modeling.
It automatically detects directions of anisotropy by analyzing the distribution of Hounsfield Unit (HU) values.
Orthotropic elastic constants are determined from local HU values and their distribution.

Main Results:

The procedure successfully generates the distribution of the bone tissue orthotropic elasticity tensor, differentiating between cortical and trabecular bone.
Anisotropic principal directions align with experimental ultrasound data.
Material mapping from CT voxels to the FE model demonstrates high reliability with marginal errors (<10% for >90% of voxels).
Computational analyses yield strain values comparable to experimental strain gauge measurements.

Conclusions:

The developed procedure offers significant potential for creating accurate, patient-specific biomechanical models from CT data.
The accuracy and automation are crucial for developing real-time clinical tools.
Limitations include incomplete automation and reliability assessment primarily focused on cortical bone.