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1Novel analytical and numerical models for spectral and fluorescence optical modalities
M.Yu. Kirillin1*, D.A. Kurakina1, A.A. Getmanskaya12, A.V. Khilov1, V.V. Perekatova1, V.A. Shishkova1, M.G. Kalashnikov12, I.V. Turchin1, E.A. Sergeeva1
1-A.V Gaponov-Grekhov Institute of Applied Physics RAS, Nizhny Novgorod, Russia
2- N.I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
Improvement of modern optical biomedical diagnostic modalities require development of novel refined models of light transport in biotissues. Among modalities aimed functional diagnostics, spectral and fluorescence optical modalities could be distinguished. Spectral modalities provide high sensitivity to variations in concentrations of different chromophores owing to their unique shapes of absorption spectra [1,2], while fluorescence modalities exhibit high sensitivity to the presence of particular fluorophores featuring unique excitation and emission spectra [3]. Moreover, spectral approach could be extended to fluorescence techniques to obtain additional information, for example, about fluorophore localization [3].
The choice of a model for light transport in biotissue is governed by two basic factors: accuracy of the model and calculation speed that determines the time requires to get the desired solution using the chosen model. The last aspect plays a crucial role in the recent decade, when the machine learning approaches became widely employed, and the construction of a training set could require considerable time. Traditionally, analytical models were implemented into reconstruction algorithms of optical diagnostics modalities owing to their speed. However, this speed comes at the expense of accuracy, since the radiative transfer equation in general form has no general analytic solution, while different approximation has limited applicability. Modern development in computational technologies resulted in active employment of numerical models of light transport that have higher versatility as compared to analytical approaches. Numerical approaches usually include finite-difference solutions of light transport equation or Monte Carlo technique [1].
In this paper application of different analytical models and Monte Carlo technique for forward and inverse problems in diffuse optical spectroscopy (DOS) and fluorescence imaging (FI) are reviewed. In DOS studies the novel refined analytical model [4] is compared to the results of Monte Carlo simulations providing the estimations of the range of parameters where analytical model could be employed without loss in accuracy, while for other sets of parameters Monte-Carlo generated look-up tables could be generated.
In FI models of light transport in biological tissues could help in quantification of the location of a fluorescing object within biotissue. The most of the drugs for photodynamic therapy exhibit fluorescence, which make fluorescence imaging a convenient tool for procedure monitoring. Moreover, other drug that are delivered through intravenous injection could be monitored using FI, if drug and fluorescent marker are combined in a nanoconstruct [5]. In this paper the results of Monte Carlo simulations of signal formation in dual-wavelength FI are reported together with simulations of FI in complex and anatomical geometries. The models for FI are based on a dual-step approach consisting in simulating of absorption map of exciting radiation followed by simulation of fluorescence emission propagation, while the distributed fluorescence source is built based on the simulated excitation absorption map.
The study is supported by the Russian Science Foundation (project 24-15-00175).
[1] D. Kurakina, V. Perekatova, E. Sergeeva, A. Kostyuk, I. Turchin, M. Kirillin, Probing depth in diffuse reflectance spectroscopy of biotissues: a Monte Carlo study, Laser Physics Letters, vol. 19, p. 035602 (2022).
[2] I. Turchin, et al, Multimodal optical monitoring of auto and allografts of skin on a burn wound, Biomedicines, vol. 11, p. 351 (2023).
[3] M. Kirillin, et al, Dual-wavelength fluorescence monitoring of photodynamic therapy: from analytical models to clinical studies, Cancers, 13(22), 5807 (2021).
[4] E. Sergeeva, D. Kurakina, I. Turchin, M. Kirillin, A refined analytical model for reconstruction problems in diffuse reflectance spectroscopy, Journal of Innovative Optical Health Sciences, 2342002 (2023).
[5] I. Turchin, et al, Combined Fluorescence and Optoacoustic Imaging for Monitoring Treatments against CT26 Tumors with Photoactivatable Liposomes, Cancers, 14 (1), 197 (2022).
