Meshless Monte Carlo radiation transfer method for curved geometries using signed distance functions.
J Biomed Opt
; 27(8)2022 08.
Article
em En
| MEDLINE
| ID: mdl-35927789
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
Tipo de estudo:
Health_economic_evaluation
Idioma:
En
Revista:
J Biomed Opt
Assunto da revista:
ENGENHARIA BIOMEDICA
/
OFTALMOLOGIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Reino Unido