Heuristic absorption calculation in bilayered media from a white Monte Carlo dataset

Area of Science:

• Biomedical Optics
• Computational Physics
• Medical Imaging

Background:

• Monte Carlo (MC) simulations are crucial for modeling photon migration in complex biological tissues.
• Detailed photon trajectory data from MC simulations can lead to computationally intensive storage and retrieval challenges.
• Efficient data representation is needed for applications like inverse problem-solving in optical imaging.

Purpose of the Study:

• To develop and evaluate a heuristic approach for reducing the computational burden of MC simulations in layered media.
• To assess the accuracy of the proposed method by comparing it with established metrics like the time point spread function (TPSF).
• To demonstrate the utility of the simplified dataset for applications in inverse problems.

Main Methods:

• Implementing a heuristic method to store only the average photon path length per detected layer.
• Conducting MC simulations for photon migration in a bilayered medium.
• Comparing the heuristic method's results with the exact time point spread function (TPSF) for validation.

Main Results:

• The heuristic approach significantly reduces the stored dataset size compared to full trajectory data.
• The method demonstrates high accuracy, especially for minor variations in absorption coefficients.
• Results closely approximate the exact TPSF for the evaluated bilayered medium.

Conclusions:

• The proposed heuristic method offers a computationally efficient alternative for handling MC simulation data in layered media.
• This simplified data representation is suitable for constructing lookup tables for inverse problems in optical tomography and related fields.
• The method provides a practical solution for managing large datasets in photon migration studies.