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A GPU based high-resolution multilevel biomechanical head and neck model for validating deformable image

J Neylon1, X Qi1, K Sheng1

  • 1Department of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, California 90095.

Medical Physics
|January 8, 2015
PubMed
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This study developed a biomechanical head and neck model to simulate realistic patient anatomy changes for adaptive radiotherapy. The real-time model validates deformable image registration (DIR) methods, improving accuracy in radiation dose delivery.

Area of Science:

  • Medical Physics
  • Biomechanical Modeling
  • Radiotherapy

Background:

  • Accurate patient positioning is crucial for effective head and neck (HN) radiotherapy.
  • Adaptive radiotherapy (RT) requires reliable methods to account for anatomical changes during treatment.
  • Deformable image registration (DIR) is a key technology for adapting RT to daily anatomical variations.

Purpose of the Study:

  • To develop a methodology for generating biomechanically realistic ground-truth data for validating DIR algorithms in HN radiotherapy.
  • To create a high-resolution deformable biomechanical HN model from planning CT data.
  • To simulate deformations representing interfraction posture changes and physiological regression for generating realistic CT images.

Main Methods:

  • A biomechanical HN model was constructed using kVCT datasets and contours, with bony anatomy as rigid and soft tissues modeled using mass-spring-damper systems.

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  • A GPU-based algorithm facilitated rapid voxel clustering and deformation simulation using implicit Euler integration.
  • Simulations included skeletal articulations for posture changes and volume reduction for tumor regression, with a novel approach for generating artifact-free CT images.

    • Main Results:

    • The GPU framework achieved real-time model deformation at 60 frames/s, enabling interactive simulations.
    • Soft tissue response accuracy exceeded R(2) >0.98 compared to ground-truth deformations.
    • Model deformations closely matched clinically observed HN anatomy changes (average correlation coefficient 0.956) with high stability (<0.01 mm uncertainty).

    Conclusions:

    • The real-time deformable biomechanical HN model facilitates interactive simulation and visualization of posture and physiological changes.
    • This model enables patient-specific validation of DIR methods.
    • The developed model shows significant potential as an aid for adaptive radiotherapy techniques in HN cancer treatment.