The paper presents a computationally efficient approach to mathematical modeling of the photon migration process in biological tissues. In this case, the tissues of living organisms are described as strongly scattering media with pronounced anisotropy and a relative refractive index higher than that of air. The proposed approach is a modified version of the Monte Carlo statistical testing method, in connection with which the calculation of the photon mean free path, the probability of an absorption or scattering act, energy loss during an absorption act, a new direction of motion in the case of an act of scattering and the behavior of a photon at the boundary of the modeled object or its separate relatively isolated section are performed according to classical formulas. The main distinctive feature of the proposed solution is the description of a photon packet as a tree-like fractal. In this case, the reference trajectory is calculated in the classical way, and the rest are completed according to the principle of self-similarity, adjusted for the presence or absence of areas of abrupt change in optical properties. This approach allows increasing the computing performance by reducing the number of photons in a packet with a proportional increase in the number of packets under consideration. The proposed solution is intended for use in the development of new and improvement of known methods of optical tomography and elastography.
1. Periyasamy V., Pramanik M. Advances in Monte Carlo Simulation for Light Propagation in Tissue. IEEE Reviews in Biomedical Engineering. 2017;10:122–135. https://doi.org/10.1109/RBME.2017.2739801
2. Bashkatov A.N., Genina E.A., Kochubey V.I., Tuchin V.V. Quantification of Tissue Optical Properties: Perspectives for Precise Optical Diagnostics, Phototherapy and Laser Surgery. Journal of Physics D: Applied Physics. 2016;49(50). https://doi.org/10.1088/0022-3727/49/50/501001
3. Kangasniemi J., Mozumder M., Pulkkinen A., Tarvainen T. Stochastic Gauss-Newton Method for Estimating Absorption and Scattering in Optical Tomography with the Monte Carlo Method for Light Transport. Biomedical Optics Express. 2024;15(8):4925–4942. https://doi.org/10.1364/BOE.528666
4. Moiseev A.A., Ksenofontov S.Yu., Terpelov D.A., Gelikonov G.V., Kiseleva E.B., Sirotkina M.A., Gladkova N.D., Yashin K.S. Optical Coherence Angiography without Motion Correction Preprocessing. Laser Physics Letters. 2019;16(4). https://doi.org/10.1088/1612-202X/aaf996
5. Palmer G.M., Ramanujam N. Monte Carlo-based Inverse Model for Calculating Tissue Optical Properties. Part I: Theory and Validation on Synthetic Phantoms. Applied Optics. 2006;45(5):1062–1071. https://doi.org/10.1364/AO.45.001062
6. Potlov A.Yu., Frolov S.V., Proskurin S.G. Numerical Simulation of Photon Migration in Homogeneous and Inhomogeneous Cylindrical Phantoms. Optics and Spectroscopy. 2020;128(6):835–842. https://doi.org/10.1134/S0030400X20060168
7. Palmer G.M., Zhu C., Breslin T.M., Xu F., Gilchrist K.W., Ramanujam N. Monte Carlo-based Inverse Model for Calculating Tissue Optical Properties. Part II: Application to Breast Cancer Diagnosis. Applied Optics. 2006;45(5):1072–1078. https://doi.org/10.1364/AO.45.001072
8. Plekhanov A.A., Gubarkova E.V., Sirotkina M.A., Sovetsky A.A., Vorontsov D.A., Matveev L.A., Kuznetsov S.S., Bogomolova A.Y., Vorontsov A.Y., Matveyev A.L., Gamayunov S.V., Zagaynova E.V., Zaitsev V.Y., Gladkova N.D. Compression OCT-Elastography Combined with Speckle-Contrast Analysis as an Approach to the Morphological Assessment of Breast Cancer Tissue. Biomedical Optics Express. 2023;14(6):3037–3056. https://doi.org/10.1364/BOE.489021
9. Frolov S.V., Potlov A.Yu., Petrov D.A., Proskurin S.G. Monte Carlo Simulation of a Biological Object with Optical Coherent Tomography Structural Images using a Voxel-based Geometry of a Medium. Quantum Electronics. 2017;47(4):347–354. https://doi.org/10.1070/QEL16204
10. Cook P.D., Bixler J.N., Thomas R.J., Early E.A. Prediction of Tissue Optical Properties using the Monte Carlo Modeling of Photon Transport in Turbid Media and Integrating Spheres. OSA Continuum. 2020;3(6):1456–1476. https://doi.org/10.1364/OSAC.377805
11. Yang X., Ren A., Zhu T., Hu F. A Novel Digital Phantom Using an Optical Noncontact Measurement System. IEEE Life Sciences Letters. 2016;2(1):1–4. https://doi.org/10.1109/LLS.2016.2568259
12. Schuetzenberger K., Pfister M., Messner A., Froehlich V., Garhoefer G., Hohenadl C., Schmetterer L., Werkmeister R.M. Comparison of Optical Coherence Tomography and High Frequency Ultrasound Imaging in Mice for the Assessment of Skin Morphology and Intradermal Volumes. Scientific Reports. 2019;9(1). https://doi.org/10.1038/s41598-019-50104-4
13. Frolov S.V., Potlov A.Yu. An Endoscopic Optical Coherence Tomography System with Improved Precision of Probe Positioning. Biomedical Engineering. 2019;53(1):6–10. https://doi.org/10.1007/s10527-019-09866-4
14. Mekonnen T., Cheng S., Kourmatzis A., Amatoury J. Simultaneous Multi-Spatial Scanning Optical Coherence Tomography (OCT) based on Spectrum-Slicing of a Broadband Source. Measurement Science and Technology. 2019;30(4). https://doi.org/10.1088/1361-6501/ab0c63
