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Geometric modeling of the human torso using cubic hermite elements

C P Bradley1, A J Pullan, P J Hunter

  • 1Department of Engineering Science, University of Auckland, New Zealand.

Annals of Biomedical Engineering
|January 1, 1997
PubMed
Summary

This study presents a novel method for fitting human torso geometry using bicubic Hermite surface elements, enabling accurate anatomical models for medical simulations. The developed finite element/boundary element model enhances applications in electrocardiography and defibrillation.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Anatomy

Background:

  • Accurate geometric modeling of the human torso is crucial for various medical simulations.
  • Magnetic Resonance Imaging (MRI) provides detailed anatomical data but requires sophisticated methods for surface reconstruction.

Purpose of the Study:

  • To develop and evaluate a new nonlinear fitting procedure for creating high-order (bicubic Hermite) surface elements from MRI data.
  • To construct a combined finite element/boundary element model of the human torso with preserved C1 continuity.

Main Methods:

  • Fitting MRI-derived human torso geometric data using bicubic Hermite surface elements.
  • Employing a novel nonlinear fitting procedure with nonlinear constraints and Nonlinear Sobelov smoothing.

Related Experiment Videos

  • Assembling fitted surfaces into a hybrid finite element/boundary element torso model.

    • Main Results:

    • Achieved smooth, anatomically accurate surfaces with relatively few elements for structures like the epicardium, lungs, skeletal muscle, fat, and skin.
    • Quantified root mean-squared errors for fitted structures, ranging from 0.91 mm (epicardium) to 1.79 mm (fat layer).
    • Successfully integrated the model for applications including electrocardiography, defibrillation, radiation dosage, and heat transfer studies.

    Conclusions:

    • The proposed fitting method effectively generates high-quality, C1 continuous surface elements from MRI data.
    • The resulting hybrid torso model provides a robust platform for advanced biomedical simulations.
    • This approach offers significant advantages for computational modeling in medical research and clinical applications.