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Основанное на методе Монте-Карло моделирование временных функций рассеяния точки и функций чувствительности для мезоскопической время-разрешенной флуоресцентной молекулярной томографии
С.И. Самарин 1, А.Б. Коновалов 1, В.В. Власов 1, И.Д. Соловьев 2, А.П. Савицкий 2, В.В. Тучин 2,3

1 ФГУП "Российский федеральный ядерный центр – ВНИИ технической физики им. академика Е.И. Забабахина",
456770, Россия, Челябинская обл., Снежинск, ул. Васильева, д. 13;
2 ФГУ "Федеральный исследовательский центр "Фундаментальные основы биотехнологии РАН",
119071, Россия, Москва, Ленинский проспект, д. 33, зд. 2;
3 Саратовский национальный исследовательский государственный университет им. Н.Г. Чернышевского,
410012, Россия, Саратов, ул. Астраханская, д. 83

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DOI: 10.18287/2412-6179-CO-1295

Страницы: 673-690.

Аннотация:
В статье описан алгоритм программы TurbidMC, реализующей пертурбационный метод Монте-Карло и предназначенной для моделирования временных функций рассеяния точки и функций чувствительности для задач время-разрешенной флуоресцентной молекулярной томографии (FMT). Программа ориентирована на работу с конкретным ранее опубликованным методом FMT ([22] в списке литературы), определяющим специфику расчета функций чувствительности. Согласно этому методу обратная задача изначально решается относительно обобщенной функции распределения параметров флуоресценции, а затем уже выполняется разделение распределений коэффициента поглощения флуорофора и времени жизни флуоресценции. Корректность работы программы проверена сравнением тестовых расчетов флуоресцентных временных функций рассеяния точки с данными эксперимента по сканированию фантома с флуорофором трехканальным зондом в мезоскопическом режиме обратного рассеяния. Также приведен пример восстановления распределений параметров флуоресценции, подтверждающий корректность расчетов функций чувствительности.

Ключевые слова:
программа TurbidMC, метод Монте-Карло, флуоресцентная молекулярная томография, временные функции рассеяния точки, функции чувствительности, коэффициент поглощения флуорофора, время жизни флуоресценции.

Цитирование:
Самарин, С.И. Основанное на методе Монте-Карло моделирование временных функций рассеяния точки и функций чувствительности для мезоскопической время-разрешенной флуоресцентной молекулярной томографии / С.И. Самарин, А.Б. Коновалов, В.В. Власов, И.Д. Соловьев, А.П. Савицкий, В.В. Тучин // Компьютерная оптика. – 2023. – Т. 47, № 5. – С. 673-690. – DOI: 10.18287/2412-6179-CO-1295.

Citation:
Samarin SI, Konovalov AB, Vlasov VV, Solovyev ID, Savitsky AP, Tuchin VV. Monte Carlo modeling of temporal point spread functions and sensitivity functions for mesoscopic time-resolved fluorescence molecular tomography. Computer Optics 2023; 47(5): 673-690. DOI: 10.18287/2412-6179-CO-1295.

References:
  1. Gao F, Zhao H-J, Tanikawa Y, Yamada Y. A linear, featured-data scheme for image reconstruction in time-domain fluorescence molecular tomography. Opt Express 2006; 14(16): 7109-7124. DOI: 10.1364/OE.14.007109.
  2. Kumar ATN, Raymond SB, Boverman G, Boas DA, Bacskai BJ. Time resolved fluorescence tomography of turbid media based on lifetime contrast. Opt Express 2006; 14(25): 12255-12270. DOI: 10.1364/OE.14.012225.
  3. Kumar ATN, Raymond SB, Dunn AK, Bacskai BJ, Boas DA. A time domain fluorescence tomography system for small animal imaging. IEEE Trans Med Imaging 2008; 27(8): 1152-1163. DOI: 10.1109/TMI.2008.918341.
  4. Nothdurft RE, Patwardhan SV, Akers W, Ye Y-P, Achilefu S, Culver JP. In vivo fluorescence lifetime tomography. J Biomed Opt 2009; 14(2): 024004. DOI: 10.1117/1.3086607.
  5. Gao F, Li J, Zhang L, Poulet P, Zhao H, Yamada Y. Simultaneous fluorescence yield and lifetime tomography from time-resolved transmittances of small-animal-sized phantom. Appl Opt 2010; 49(16): 3163-3172. DOI: 10.1364/AO.49.003163.
  6. Raymond SB, Boas DA, Bacskai BJ, Kumar ATN. Lifetime-based tomographic multiplexing. J Biomed Opt 2010; 15(4): 046011. DOI: 10.1117/1.3469797.
  7. Chen J, Venugopal V, Intes X. Monte Carlo based method for fluorescence tomographic imaging with lifetime multiplexing using time gates. Biomed Opt Express 2011; 2(4): 871-886. DOI: 10.1364/BOE.2.000871.
  8. Gao F, Li J, Zhang W, Yi X, Wang X, Zhang L, Zhou Z, Zhao H. A CT-analogous scheme for time-domain diffuse fluorescence tomography. J Xray Sci Technol 2012; 20(1): 91-105. DOI: 10.3233/XST-2012-0321.
  9. Rice WL, Kumar ATN. Preclinical whole body time domain fluorescence lifetime multiplexing of fluorescent proteins. J Biomed Opt 2014; 19(4): 046005. DOI: 10.1117/1.JBO.19.4.046005.
  10. Hou SS, Rice WL, Bacskai BJ, Kumar ATN. Tomographic lifetime imaging using combined early- and late-arriving photons. Opt Lett 2014; 39(5): 1165-1168. DOI: 10.1364/OL.39.001165.
  11. Rice WL, Shcherbakova DM, Verkhusha VV, Kumar ATN. In vivo tomographic imaging of deep-seated cancer using fluorescence lifetime contrast. Cancer Res 2015; 75(7): 1236-1243. DOI: 10.1158/0008-5472.CAN-14-3001.
  12. Cai C, Zhang L, Zhang J, Bai J, Luo J. Direct reconstruction method for time-domain fluorescence molecular lifetime tomography. Opt Lett 2015; 40(17): 4038-4041. DOI: 10.1364/OL.40.004038.
  13. Zhang L, Cai C, Lv Y, Luo J. Early-photon guided reconstruction method for time-domain fluorescence lifetime tomography. Chin Opt Lett 2016; 14(7): 071702. DOI: 10.3788/COL201614.071702.
  14. Cai C, Zhang L, Cai W, Zhang D, Lv Y, Luo J. Nonlinear greedy sparsity-constrained algorithm for direct reconstruction of fluorescence molecular lifetime tomography. Biomed Opt Express 2016; 7(4): 1210-1226. DOI: 10.1364/BOE.7.001210.
  15. Cai C, Cai W, Cheng J, Yang Y, Luo J. Self-guided reconstruction for time-domain fluorescence molecular lifetime tomography. J Biomed Opt 2016; 21(12): 126012. DOI: 10.1117/1.JBO.21.12.126012.
  16. Zhang P, Liu J, Hui H, An Y, Wang K, Yang X, Tian J. Linear scheme for the direct reconstruction of noncontact time-domain fluorescence molecular lifetime tomography. Appl Opt 2020; 59(26): 7961-7967. DOI: 10.1364/AO.398967.
  17. Cheng J, Zhang P, Cai C, Gao Y, Liu J, Hui H, Tian J, Luo J. Depth-recognizable time-domain fluorescence molecular tomography in reflective geometry. Biomed Opt Express 2021; 12(7): 3806-3818. DOI: 10.1364/BOE.430235.
  18. Becker W. Fluorescence lifetime imaging – techniques and applications. J Microsc 2012; 247(Part2): 119-136. DOI: 10.1111/j.1365-2818.2012.03618.x.
  19. Wang XF, Periasamy A, Herman B, Coleman DM. Fluorescence lifetime imaging microscopy (FLIM): instrumentation and applications. Crit Rev Anal Chem 1992; 23(5): 369-395. DOI: 10.1080/10408349208051651.
  20. Datta R, Heaster TM, Sharick JT, Gillette AA, Skala MC. Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications. J Biomed Opt 2020; 25(7): 071203. DOI: 10.1117/1.JBO.25.7.071203.
  21. Dmitriev RI, Intes X, Barroso MM. Luminescence lifetime imaging of three-dimensional biological objects. J Cell Sci 2021; 134(9): jcs254763. DOI: 10.1242/jcs254763.
  22. Konovalov AB, Vlasov VV, Samarin SI, Soloviev ID, Savitsky AP, Tuchin VV. Reconstruction of fluorophore absorption and fluorescence lifetime using early photon mesoscopic fluorescence molecular tomography: a phantom study. J Biomed Opt 2022; 27(12): 126001. DOI: 10.1117/1.JBO.27.12.126001.
  23. Abou-Elkacem L, Bjorn S, Doleschel D, Ntziachristos V, Schulz R, Hoffman RM, Kiessling F, Lederle W. High accuracy of mesoscopic epi-fluorescence tomography for non-invasive quantitative volume determination of fluorescent protein-expressing tumours in mice. Eur Radiol 2012; 22(9): 1955-1962. DOI: 10.1007/s00330-012-2462-x.
  24. Ozturk MS, Lee VK, Zhao L, Dai G, Intes X. Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue. J Biomed Opt 2013; 18(10): 100501. DOI: 10.1117/1.JBO.18.10.100501.
  25. Yang F, Ozturk MS, Zhao L, Cong W, Wang G, Intes X. High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing. IEEE Trans Biomed Eng 2015; 62(1): 248-255. DOI: 10.1109/TBME.2014.2347284.
  26. Tang Q, Tsytsarev V, Frank A, Wu Y, Chen C-W, Erzurumlu RS, Chen Y. In vivo mesoscopic voltage-sensitive dye imaging of brain activation. Sci Rep 2016; 6: 25269. DOI: 10.1038/srep25269.
  27. Azimipour M, Sheikhzadeh M, Baumgartner R, Cullen PK, Helmstetter FJ, Chang W-J, Pashaie R. Fluorescence laminar optical tomography for brain imaging: system implementation and performance evaluation. J Biomed Opt 2017; 22(1): 016003. DOI: 10.1117/1.JBO.22.1.016003.
  28. Ozturk MS, Montero MG, Wang L, Chaible LM, Jechlinger M, Prevedel R. Intravital mesoscopic fluorescence molecular tomography allows non-invasive in vivo monitoring and quantification of breast cancer growth dynamics. Commun Biol 2021; 4: 556. DOI: 10.1038/s42003-021-02063-8.
  29. Arridge SR, Schotland JC. Optical tomography: forward and inverse problems. Inverse Probl 2009; 25(12): 123010. DOI: 10.1088/0266-5611/25/12/123010.
  30. Kuz’min VL, Val’kov AYu, Zubkov LA. Photon diffusion in random media and anisotropy of scattering in the Henyey-Greenstein and Rayleigh-Gans models. J Exp Theor Phys 2019; 128(3): 396-406. DOI: 10.1134/S1063776119020109.
  31. Lu Y, Zhu B, Shen H, Rasmussen JC, Wang G, Sevick-Muraca EM. A parallel adaptive finite element simplified spherical harmonics approximation solver for frequency domain fluorescence molecular imaging. Phys Med Biol 2010; 55(16): 4625-4645. DOI: 10.1088/0031-9155/55/16/002.
  32. Kim HK, Lee JH, Hielscher AH. PDE-constrained fluorescence tomography with the frequency-domain equation of radiative transfer. IEEE J Sel Top Quantum Electron 2010; 16(4): 793-803. DOI: 10.1109/JSTQE.2009.2038112.
  33. Guo H, Hou Y, He X, Yu J, Cheng J, Pu X. Adaptive hp finite element method for fluorescence molecular tomography with simplified spherical harmonics approximation. J Innov Opt Health Sci 2014; 7(2): 1350057. DOI: 10.1142/S1793545813500570.
  34. He X, Guo H, Yu J, Zhang X, Hou Y. Effective and robust approach for fluorescence molecular tomography based on CoSaMP and SP3 model. J Innov Opt Health Sci 2016; 9(6): 1650024. DOI: 10.1142/S1793545816500243.
  35. Crilly RJ, Cheong W-F, Wilson B, Spears JR. Forward-adjoint fluorescence model: Monte Carlo integration and experimental validation. Appl Opt 1997; 36(25): 6513-6519. DOI: 10.1364/AO.36.006513.
  36. Finlay JC, Foster TH. Recovery of hemoglobin oxygen saturation and intrinsic fluorescence with a forward-adjoint model. Appl Opt 2005; 44(10): 1917-1933. DOI: 10.1364/AO.44.001917.
  37. Haykawa CK, Spanier J, Venugopalan V. Coupled forward-adjoint Monte Carlo simulations of radiative transport for the study of optical probe design in heterogeneous tissues. SIAM J Appl Math 2007; 68(1): 253-270. DOI: 10.1137/060653111.
  38. Chen J, Intes X. Time gated perturbation Monte Carlo for whole body functional imaging in small animals. Opt Express 2009; 17(22): 19566-19579. DOI: 10.1364/OE.17.019566.
  39. Chen J, Intes X. Comparison of Monte Carlo methods for fluorescence molecular tomography – computational efficiency. Med Phys 2011; 38(10): 5788-5798. DOI: 10.1118/1.3641827.
  40. Gardner AR, Haykawa CK, Venugopalan V. Coupled forward-adjoint Monte Carlo simulation of spatial-angular light fields to determine optical sensitivity in turbid media. J Biomed Opt 2014; 19(6): 065003. DOI: 10.1117/1.JBO.19.6.065003.
  41. Jiang X, Deng Y, Luo Z, Wang K, Lian L, Yang X, Meglinski I, Luo Q. Evaluation of path-history-based fluorescence Monte Carlo method for photon migration in heterogeneous media. Opt Express 2014; 22(26): 31948-31965. DOI: 10.1364/OE.22.031948.
  42. Yao R, Intes X, Fang Q. Direct approach to compute Jacobians for diffuse optical tomography using perturbation Monte Carlo-based photon “replay.” Biomed Opt Express 2018; 9(10): 4588-4603. DOI: 10.1364/BOE.9.004588.
  43. Wang L, Jacques SL, Zheng L. MCML – Monte Carlo modeling of light transport in multi-layered tissues. Comput Methods Programs Biomed 1995; 47(2): 131-146. DOI: 10.1016/0169-2607(95)01640-F.
  44. Welch AJ, Gardner C, Richards-Kortum R, Chan E, Criswell G, Pfefer J, Warren S. Propagation of fluorescent light. Lasers Surg Med 1997; 21(2): 166-178. DOI: 10.1002/(SICI)1096-9101(1997)21:2<166::AID-LSM8>3.0.CO;2-O
  45. Agostinelli S, et al. Geant4 – a simulation toolkit. Nucl Instrum Methods Phys Res A 2003; 506(3): 250-303. DOI: 10.1016/S0168-9002(03)01368-8.
  46. Doronin A, Meglinski I. Online object oriented Monte Carlo computational tool for the needs of biomedical optics. Biomed Opt Express 2011; 2(9): 2461-2469. DOI: 10.1364/BOE.2.002461.
  47. Ren S, Chen X, Wang H, Qu X, Wang G, Liang J, Tian J. Molecular Optical Simulation Environment (MOSE): A platform for the simulation of light propagation in turbid media. PLoS ONE 2013; 8(4): e61304. DOI: 10.1371/journal.pone.0061304.
  48. Leino AA, Pulkkinen A, Tarvainen T. ValoMC: a Monte Carlo software and MATLAB toolbox for simulating light transport in biological tissue. OSA Continuum 2019; 2(3): 957-972. DOI: 10.1364/OSAC.2.000957.
  49. Serov I, John T, Hoogenboom J. A new effective Monte Carlo midway coupling method in MCNP applied to a well logging problem. Appl Radiat Isot 1998; 49(12): 1737-1744. DOI: 10.1016/S0969-8043(98)00055-4.
  50. Serov I, John T, Hoogenboom J. A midway forward-adjoint coupling method for neutron and photon Monte Carlo transport. Nucl Sci Eng 1999; 133(9): 55-72. DOI: 10.13182/NSE99-A2072.
  51. Briesmeister J. MCNP – a general Monte Carlo N-particle transport code. Los Alamos National Laboratory Report LA-13709-M 2000.
  52. Kandiev YaZ, Malyshkin GN, Zatsepin OV. Monte Carlo code PRIZMA for calculation of particle transport problems. Proc Joint Int Conf on Supercomputing in Nuclear Applications and Monte Carlo [CD-ROM]; 2010.
  53. Lux I, Koblinger L. Monte-Carlo transport methods: Neutron and photon calculations. Boca Raton: CRC Press; 2000. ISBN: 0-8493-6074-9.
  54. Dorosev AS, Kostjuchenko VI, Samarin SI. Direct account of experimental data uncertainties in modeling of a 160 MeV proton beam interaction with a multilayer Faraday cup [In Russian]. Meditsinskaya Fizika 2014; 2(62): 24-31.
  55. Samarin SI. Certificate of Governmental Registration of Computer Program in FIPS. No. 2018666251; 2018.
  56. Born M, Wolf E. Principles of optics. 7th ed. Cambridge: Cambridge University Press; 1999. ISBN: 0-521-64222-1.
  57. Sobol IM. Numerical Monte Carlo methods [In Russian]. Moscow: "Nauka" Publisher; 1973.
  58. Henyey IG, Greenstein JI. Diffuse radiation in the galaxy. Astrophys J 1941; 93: 70-83. DOI: 10.1086/144246.
  59. Akkerman AF. Modeling of charge particle trajectories in matter [In Russian]. Moscow: "Energoatomizdat" Publisher; 1991. ISBN: 5-283-02924-7.
  60. Konovalov AB, Vlasov VV, Uglov AS. Early-photon reflectance fluorescence molecular tomography for small animal imaging: Mathematical model and numerical experiment. Int J Numer Method Biomed Eng 2021; 37(1): e3408. DOI: 10.1002/cnm.3408.
  61. Lyubimov VV, Kalintsev AG, Konovalov AB, Lyamtsev OV, Kravtsenyuk OV, Murzin AG, Golubkina OV, Mordvinov GB, Soms LN, Yavorskaya LM. Application of the photon average trajectories method to real-time reconstruction of tissue inhomogeneities in diffuse optical tomography of strongly scattering media. Phys Med Biol 2002; 47(12): 2109-2128. DOI: 10.1088/0031-9155/47/12/308.
  62. Konovalov AB, Vlasov VV, Lyubimov VV. Statistical characteristics of photon distributions in a semi-infinite
    turbid medium: Analytical expressions and their application to optical tomography. Optik 2013; 124(23): 6000-6008. DOI: 10.1016/j.ijleo.2013.04.111.
  63. Gordon R, Bender R, Herman GT. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. J Theor Biol 1970; 29(3): 471-482. DOI: 10.1016/0022-5193(70)90109-8.
  64. Yu H, Wang G. Compressed sensing based interior tomography. Phys Med Biol 2009; 54(9): 2791-2805. DOI: 10.1088/0031-9155/54/9/014.
  65. Vlasov VV, Konovalov AB, Kolchugin SV. Hybrid algorithm for few-views computed tomography of strongly absorbing media: algebraic reconstruction, TV-regularization, and adaptive segmentation. J Electron Imaging 2018; 27(4): 043006. DOI: 10.1117/1.JEI.27.4.043006.
  66. Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2009; 2(1): 183-202. DOI: 10.1137/080716542.
  67. Paige CC, Sanders MA. LSQR: An algorithm for sparse linear equations and sparse least squares. ACM Trans Math Softw 1982; 8(1): 43-71. DOI: 10.1145/355984.355989.

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