Light transport modeling in highly complex tissues using the implicit mesh-based Monte Carlo algorithm.

Area of Science:

• Biophotonics
• Computational Modeling
• Medical Physics

Background:

• Mesh-based Monte Carlo (MMC) is a key method for accurate light transport simulation in biophotonics.
• Current MMC methods face challenges with computational cost (runtime and memory) for complex tissues like dense microvascular networks.
• This limits the exploration of multi-scale and intricate tissue structures in biophotonics research.

Purpose of the Study:

• To develop a more efficient Monte Carlo method for light transport simulation in complex biological tissues.
• To address the limitations of existing MMC methods regarding memory usage and runtime for complex geometries.
• To enable accurate and efficient modeling of light propagation in challenging biological structures.

Main Methods:

• Introduction of the implicit mesh-based Monte Carlo (iMMC) method.
• Integration of both mesh-based and shape-based tissue representations within the MMC framework.
• Development of algorithms to handle complex tissue structures like dense microvascular networks and porous media.

Main Results:

• iMMC significantly reduces memory requirements (100-1000 fold) for complex tissues.
• Achieved nearly 3x speed acceleration in simulations of microvasculature networks due to reduced shape complexity.
• Demonstrated accurate light transport solutions for dense vessel networks and porous tissues.

Conclusions:

• The iMMC method offers a highly efficient and memory-sparing approach for light transport simulations in complex biological tissues.
• This advancement overcomes previous computational limitations, enabling deeper investigation into biophotonics phenomena in intricate structures.
• The iMMC algorithm is available in the open-source MMC software, promoting further research and development in the field.