dissertation · St Andrews Research Repository (St Andrews Research Repository) · 2020
Advanced 3D Monte Carlo algorithms for biophotonic and medical applications
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Abstract
The Monte Carlo radiation transfer (MCRT) method can simulate the transport of light through turbid media. MCRT allows the modelling of multiple anisotropic scattering events, as well as a range of microphysics such as polarisation and fluorescence. This thesis concerns the development of several MCRT algorithms to solve various biophotonic and medically-related problems including modelling of tissue ablation and autofluorescent signals. An extension of the MCRT method through a theoretical quasi-wave/particle model is also demonstrated, allowing beam shapes with arbitrary phase profiles to be propagated. Tissue ablation can be used to treat acne scarring, Rhinophyma, and it can also be used to help enhance topical drug delivery. Currently the depth of ablation is not easily elucidated from a given laser or laser power setting. Therefore, a numerical tissue ablation model is developed using a combination of MCRT, a heat diffusion model, and a numerical tissue damage model to assess ablation crater depth and thermal damage to the surrounding tissue. Autofluorescence is the natural fluorescence of biological structures in tissue. Autofluorescence can be used as a biomarker of several diseases including: cardiovascular diseases, Alzheimers, and diabetes. However, the origin of the autofluorescence signal is not completely clear. The effect of tissue optics on the signal, which fluorophores contribute to the signal and by how much, and how different locations on the body can affect the signal are not well understood. This thesis presents a study of the effect of tissue optics on the autofluorescent signal. As part of this study, AmoebaMCRT was created to determine the relative concentrations of fluorophores for a given autofluorescent signal. Finally, we developed an extension to the MCRT method which allows the simulation of quasi-wave/particles. This method relies on the Huygens-Fresnel principle and the tracking of the phase of each individual photon packet. The extension, φMC, allows the modelling of complex beams that require the wave properties of light such as arbitrary order Bessel beams and Gaussian beams. We then use φMC to predict which beam, Bessel or Gaussian, performs “better" in a highly turbid medium.
Cite this paper
McMillan, L. (2020). Advanced 3D Monte Carlo algorithms for biophotonic and medical applications. St Andrews Research Repository (St Andrews Research Repository). https://doi.org/10.17630/sta/35
McMillan, Lewis. “Advanced 3D Monte Carlo algorithms for biophotonic and medical applications.” St Andrews Research Repository (St Andrews Research Repository), 2020, https://doi.org/10.17630/sta/35.
McMillan, Lewis. 2020. “Advanced 3D Monte Carlo algorithms for biophotonic and medical applications.” St Andrews Research Repository (St Andrews Research Repository). https://doi.org/10.17630/sta/35.
McMillan, L. (2020) ‘Advanced 3D Monte Carlo algorithms for biophotonic and medical applications’, St Andrews Research Repository (St Andrews Research Repository). Available at: https://doi.org/10.17630/sta/35.
@phdthesis{mcmillan2020advanced,
title = {{Advanced 3D Monte Carlo algorithms for biophotonic and medical applications}},
author = {McMillan, Lewis},
school = {St Andrews Research Repository (St Andrews Research Repository)},
year = {2020},
doi = {10.17630/sta/35}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.