ArticleBiomedical optics express2021
Light transport modeling in highly complex tissues using the implicit mesh-based Monte Carlo algorithm.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 24 citations in OpenAlex.
- Sensing Through Tissues Using Diffuse Optical Imaging and Genetic Programming.Sensors (Basel, Switzerland) · 2026Article
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- Propagation of Laguerre-Gaussian beam intensities through optically thick turbid media.Scientific reports · 2025Article
- NIRSTORM: a Brainstorm extension dedicated to functional near-infrared spectroscopy data analysis, advanced 3D reconstructions, and optimal probe design.Neurophotonics · 2025Article
- Revisiting equivalent optical properties for cerebrospinal fluid to improve diffusion-based modeling accuracy in the brain.Neurophotonics · 2025Article
- Revisiting equivalent optical properties for cerebrospinal fluid to improve diffusion-based modeling accuracy in the brain.bioRxiv : the preprint server for biology · 2024Article
- Multiplexed near infrared fluorescence lifetime imaging in turbid media.Journal of biomedical optics · 2024Article
- 4D spectral-spatial computational photoacoustic dermoscopy.Photoacoustics · 2023Article
- Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.Journal of biomedical optics · 2022Article
- Optical imaging and spectroscopy for the study of the human brain: status report.Neurophotonics · 2022Article
- Meshless Monte Carlo radiation transfer method for curved geometries using signed distance functions.Journal of biomedical optics · 2022Article
- MCDataset: a public reference dataset of Monte Carlo simulated quantities for multilayered and voxelated tissues computed by massively parallel PyXOpto Python package.Journal of biomedical optics · 2022Article
- BlenderPhotonics: an integrated open-source software environment for three-dimensional meshing and photon simulations in complex tissues.Journal of biomedical optics · 2022Article
- High-resolution three-dimensional blood flow tomography in the subdiffuse regime using laser speckle contrast imaging.Journal of biomedical optics · 2022Article
- MCX Cloud-a modern, scalable, high-performance and in-browser Monte Carlo simulation platform with cloud computing.Journal of biomedical optics · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.