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A Clinically Compatible Method for Generating Preoperative Finite Element Models to Simulate Facial Appearance and

Marie-Charlotte Picard1,2,3, Mohammad Ali Nazari1,4, Pascal Perrier2

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This study introduces a novel method for creating patient-specific 3D facial models to predict orthognathic surgery outcomes. This approach enhances surgical planning by integrating detailed bone and soft tissue anatomy for improved patient care.

Keywords:
3D image registrationdigital twinsfacial musclespatient‐specific face model

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Anatomy

Background:

  • Orthognathic surgery significantly impacts facial aesthetics and function.
  • Precise, patient-tailored surgical planning tools are crucial for optimal outcomes.
  • Current methods may lack the detailed anatomical representation needed for accurate prediction.

Purpose of the Study:

  • To develop an innovative, nearly automatic method for generating patient-specific 3D facial soft tissue models.
  • To integrate these models into clinical workflows for predicting orthognathic surgery consequences.
  • To validate the method through Finite Element simulations of surgical procedures.

Main Methods:

  • Development of a 3D reference face model incorporating bone (mandible, maxilla) and soft tissues (skin, fat, muscles).
  • A semi-automated fitting process using 34 manually selected landmarks from clinical data.
  • Automated generation of patient-specific models from CT imaging data.
  • Finite Element Method (FEM) simulations using Ansys APDL for surgical prediction and muscle contraction analysis.

Main Results:

  • Successful generation of patient-specific 3D facial models with integrated anatomical structures.
  • Demonstration of the model's utility in simulating orthognathic surgery outcomes.
  • Validation of the method's accuracy and potential for clinical integration through simulation results.

Conclusions:

  • The proposed method offers a powerful tool for patient-specific 3D facial modeling in orthognathic surgery.
  • Integration into clinical practice can significantly improve surgical planning and outcome prediction.
  • This approach holds promise for enhancing both the aesthetic and functional results of orthognathic procedures.