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dc.contributor.authorMcMillan, Lewis Thomas
dc.contributor.authorBruce, Graham David
dc.contributor.authorDholakia, Kishan
dc.date.accessioned2022-08-05T16:30:03Z
dc.date.available2022-08-05T16:30:03Z
dc.date.issued2022-08-04
dc.identifier.citationMcMillan , 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.27.8.083003en
dc.identifier.issn1083-3668
dc.identifier.otherPURE: 277099216
dc.identifier.otherPURE UUID: aa2ee1a4-2e17-4310-a87b-5eccb9eed6f0
dc.identifier.otherORCID: /0000-0003-3403-0614/work/116910151
dc.identifier.otherORCID: /0000-0002-7725-5162/work/116910217
dc.identifier.urihttp://hdl.handle.net/10023/25785
dc.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.en
dc.description.abstractSignificance: 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.
dc.format.extent15
dc.language.isoeng
dc.relation.ispartofJournal of Biomedical Opticsen
dc.rightsCopyright © 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].en
dc.subjectMonte Carloen
dc.subjectLight transporten
dc.subjectSigned distance functionsen
dc.subjectGeometryen
dc.subjectMeshlessen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQC Physicsen
dc.subjectQH301 Biologyen
dc.subjectDASen
dc.subject.lccQA75en
dc.subject.lccQCen
dc.subject.lccQH301en
dc.titleMeshless Monte Carlo radiation transfer method for curved geometries using signed distance functionsen
dc.typeJournal articleen
dc.contributor.sponsorEPSRCen
dc.contributor.sponsorEuropean Commissionen
dc.contributor.sponsorEuropean Commissionen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen
dc.contributor.institutionUniversity of St Andrews. Centre for Biophotonicsen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.identifier.doihttps://doi.org/10.1117/1.JBO.27.8.083003
dc.description.statusPeer revieweden
dc.identifier.grantnumberEP/R004854/1en
dc.identifier.grantnumber863203en
dc.identifier.grantnumber871212en


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