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Automatic generation of finite element meshes from computed tomography data.

Marco Viceconti1, Fulvia Taddei

  • 1Laboratorio di Tecnologia Medica, Istituti Ortopedici Rizzoli, Bologna, Italy. viceconti@tecno.ior.it

Critical Reviews in Biomedical Engineering
|February 18, 2004
PubMed
Summary

Generating finite element models (FEM) for clinical use is slow. This review examines automatic mesh generation algorithms, finding none fully meet clinical needs but some are applicable now.

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Area of Science:

  • Biomedical Engineering
  • Computational Science

Background:

  • The finite element method (FEM) is crucial for biomedical applications, but its clinical adoption is hindered by slow model generation.
  • Automatic mesh generation algorithms have advanced, yet their integration into clinical practice remains limited.

Purpose of the Study:

  • To review and critically discuss principal automatic mesh generation algorithms.
  • To assess their applicability within the biomedical field, particularly for clinical practice.

Main Methods:

  • A comprehensive review of specialized literature on numerical methods for automatic mesh generation.
  • Classification of published methods for generating finite element models of organs from computed tomography data.
  • Critical evaluation of each method's suitability for clinical settings.

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Main Results:

  • No single automatic mesh generation method currently fulfills all clinical requirements for automation, generality, accuracy, and robustness.
  • Several existing methods demonstrate successful application in specific clinical contexts.
  • Guidelines for selecting and applying these methods in practice were developed.

Conclusions:

  • Despite current limitations, advancements in automatic mesh generation show promise for accelerating FEM adoption in clinical biomedical applications.
  • Further development is needed to meet the stringent demands of real-time clinical workflows.
  • The identified methods offer practical solutions for certain applications, guiding future research and implementation.