ArticleJournal of biomedical optics2022
MCDataset: a public reference dataset of Monte Carlo simulated quantities for multilayered and voxelated tissues computed by massively parallel PyXOpto Python package.
Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- DrSVision: A Machine Learning Tool for Cortical Region-Specific fNIRS Calibration Based on Cadaveric Head MRI.Sensors (Basel, Switzerland) · 2025Article
- The Relationship Between the Optical Properties of Articular Cartilage and the Biomechanical Parameters of the Tissue.Journal of biophotonics · 2025Article
- Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering.Journal of biomedical optics · 2024Article
- Application limits of the scaling relations for Monte Carlo simulations in diffuse optics. Part 2: results.Optics express · 2024Article
- scatterBrains: an open database of human head models and companion optode locations for realistic Monte Carlo photon simulations.Journal of biomedical optics · 2023Article
- Importance sampling-accelerated simulation of full-spectrum backscattered diffuse reflectance.Biomedical optics express · 2023Article
- Fluence rate directly derived from photon pathlengths: a tool for Monte Carlo simulations in biomedical optics.Biomedical optics express · 2023Article
- Monte Carlo-based full-wavelength simulator of Fourier-domain optical coherence tomography.Biomedical optics express · 2022Article
- Special Section Guest Editorial: Introduction to the Special Section Celebrating 30 years of Open Source Monte Carlo Codes in Biomedical Optics.Journal of biomedical optics · 2022Article
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Authors and funding
3 authors.
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Abstract
significanceCurrent open-source Monte Carlo (MC) method implementations for light propagation modeling are many times tedious to build and require third-party licensed software that can often discourage prospective researchers in the biomedical optics community from fully utilizing the light propagation tools. Furthermore, the same drawback also limits rigorous cross-validation of physical quantities estimated by various MC codes.
aimProposal of an open-source tool for light propagation modeling and an easily accessible dataset to encourage fruitful communications amongst researchers and pave the way to a more consistent comparison between the available implementations of the MC method. APPROACH: The PyXOpto implementation of the MC method for multilayered and voxelated tissues based on the Python programming language and PyOpenCL extension enables massively parallel computation on numerous OpenCL-enabled devices. The proposed implementation is used to compute a large dataset of reflectance, transmittance, energy deposition, and sampling volume for various source, detector, and tissue configurations.
resultsThe proposed PyXOpto agrees well with the original MC implementation. However, further validation reveals a noticeable bias introduced by the random number generator used in the original MC implementation.
conclusionsEstablishing a common dataset is highly important for the validation of existing and development of MC codes for light propagation in turbid media.
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