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Mesh Optimization for Monte Carlo-Based Optical Tomography.

Andrew Edmans1, Xavier Intes1

  • 1Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th street, Troy, NY 12180, USA.

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|November 14, 2015
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Summary
This summary is machine-generated.

This study introduces an optimized mesh discretization for Monte Carlo-based optical tomography, improving computational efficiency without sacrificing accuracy in light propagation modeling.

Keywords:
Monte Carlofluorescence molecular tomographymesh optimizationmesh-based Monte Carlooptical tomographytime-gated optical tomography

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Area of Science:

  • Biomedical Optics
  • Computational Imaging
  • Medical Physics

Background:

  • Mesh-based Monte Carlo (MMC) methods accurately model light propagation in biological tissues.
  • MMC is increasingly used as a forward model in optical tomography.
  • Adaptive mesh discretization is lacking in current MMC tomography frameworks.

Purpose of the Study:

  • To develop and validate a methodology for optimizing mesh discretization in MMC-based optical tomography.
  • To analytically rescale the Jacobian matrix based on forward model characteristics.
  • To enable efficient mesh adaptation for improved computational performance.

Main Methods:

  • Proposed a novel methodology for mesh discretization optimization.
  • Developed analytical rescaling of the Jacobian matrix.
  • Validated the approach using temporal datasets in optical tomography simulations.

Main Results:

  • The proposed method maintains forward model accuracy with adaptive mesh refinement/coarsening.
  • Significant computational efficiency gains were achieved through mesh optimization.
  • Accuracy was preserved even with temporal data, crucial for dynamic imaging.

Conclusions:

  • The developed methodology effectively integrates adaptive mesh discretization into MMC-based optical tomography.
  • This approach enhances computational efficiency while ensuring the accuracy of light propagation models.
  • The findings pave the way for more robust and scalable optical tomography applications.