Meshless Monte Carlo radiation transfer method for curved geometries using signed distance functions
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Significance: Monte Carlo radiation transfer (MCRT) is the gold standard for modeling light transport in turbid media. Typical MCRT models use voxels or meshes to approximate experimental geometry. A voxel-based geometry does not allow for the precise modeling of smooth curved surfaces, such as may be found in biological systems or food and drink packaging. Mesh-based geometry allows arbitrary complex shapes with smooth curved surfaces to be modeled. However, mesh-based models also suffer from issues such as the computational cost of generating meshes and inaccuracies in how meshes handle reflections and refractions. Aim: We present our algorithm, which we term signedMCRT (sMCRT), a geometry-based method that uses signed distance functions (SDF) to represent the geometry of the model. SDFs are capable of modeling smooth curved surfaces precisely while also modeling complex geometries. Approach: We show that using SDFs to represent the problem’s geometry is more precise than voxel and mesh-based methods. Results: sMCRT is validated against theoretical expressions, and voxel and mesh-based MCRT codes. We show that sMCRT can precisely model arbitrary complex geometries such as microvascular vessel network using SDFs. In comparison with the current state-of-the-art in MCRT methods specifically for curved surfaces, sMCRT is more precise for cases where the geometry can be defined using combinations of shapes. Conclusions: We believe that SDF-based MCRT models are a complementary method to voxel and mesh models in terms of being able to model complex geometries and accurately treat curved surfaces, with a focus on precise simulation of reflections and refractions. sMCRT is publicly available at https://github.com/lewisfish/signedMCRT.
McMillan , L T , Bruce , G D & Dholakia , K 2022 , ' Meshless Monte Carlo radiation transfer method for curved geometries using signed distance functions ' , Journal of Biomedical Optics , vol. 27 , no. 8 , 083003 . https://doi.org/10.1117/1.JBO.27.8.083003
Journal of Biomedical Optics
Copyright © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JBO.27.8.083003].
DescriptionFunding: Funding: The work was supported by funding from the UK Engineering and Physical Sciences Research Council (EP/R004854/1) and the European Union H2020 projects “Dynamic” (EC-GA 863203) and “Proscope” (871212). KD acknowledges support of the Australian Research Council through a Laureate Fellowship.
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