Abstract
The mesh-based Monte Carlo (MMC) technique has grown tremendously since its initial publication nearly a decade ago. It is now recognized as one of the most accurate Monte Carlo (MC) methods, providing accurate reference solutions for the development of novel biophotonics techniques. In this work, we aim to further advance MMC to address a major challenge in biophotonics modeling, i.e. light transport within highly complex tissues, such as dense microvascular networks, porous media and multi-scale tissue structures. Although the current MMC framework is capable of simulating light propagation in such media given its generality, the run-time and memory usage grow rapidly with increasing media complexity and size. This greatly limits our capability to explore complex and multi-scale tissue structures. Here, we propose a highly efficient implicit mesh-based Monte Carlo (iMMC) method that incorporates both mesh- and shape-based tissue representations to create highly complex yet memory-efficient light transport simulations. We demonstrate that iMMC is capable of providing accurate solutions for dense vessel networks and porous tissues while reducing memory usage by greater than a hundred- or even thousand-fold. In a sample network of microvasculature, the reduced shape complexity results in nearly 3x speed acceleration. The proposed algorithm is now available in our open-source MMC software at http://mcx.space/#mmc.
Highlights
The Monte Carlo (MC) method is widely used as the gold standard for modeling light propagation in complex media such as human tissues [1,2]
We will separately describe our processing pipelines for 1) edge-based implicit mesh-based Monte Carlo (iMMC) (e-iMMC), where implicitly defined cylindrical domains are associated with selected edges to represent vessel/airway networks, 2) node-based iMMC (n-iMMC), where implicitly defined cavities are associated with vertices for modeling porous media, and 3) face-based iMMC (f-iMMC), where a thin membrane is associated with the selected triangular faces of tetrahedral elements to model ultra-thin tissue layers such as human epidermis
We first show a few simple benchmarks to demonstrate the essential features for the edge, node- and face-based iMMC
Summary
The Monte Carlo (MC) method is widely used as the gold standard for modeling light propagation in complex media such as human tissues [1,2]. The added capability to model 3-D voxel-based heterogeneous domains [3,13,14,15] represents a major milestone towards broader utilities. Using voxels to represent shapes with curved boundaries leads to expensive memory and computational costs because high voxel densities are usually needed to retain boundary accuracy. The MMC method is designed to overcome the limitations of voxel-based MC by performing ray-tracing computations in tetrahedral meshes. Because tetrahedral meshes present greater flexibility in representing arbitrary shapes, MMC can model light propagation in complex anatomical structures with high accuracy and memory efficiency
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