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Abstract
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.
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.
Original language | English |
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Article number | 083003 |
Number of pages | 15 |
Journal | Journal of Biomedical Optics |
Volume | 27 |
Issue number | 8 |
DOIs | |
Publication status | Published - 4 Aug 2022 |
Keywords
- Monte Carlo
- Light transport
- Signed distance functions
- Geometry
- Meshless
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H2020-FETOPEN-2018-2020. DynAMic: H2020-FETOPEN-2018-2020. DynAMic
Dholakia, K. (PI)
1/01/20 → 31/12/23
Project: Standard
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M Squared - USTAN Biophotonics Nexus: M Sqaured - St Andrews Biophotonics Nexus
Dholakia, K. (PI)
1/11/17 → 31/10/22
Project: Standard