Научные статьи \ Прикладные науки. Медицина. Технология \ Инженерное дело. Техника в целом \ Общее машиностроение. Ядерная технология. Электротехника. Технология машиностроения \ Электротехника

Основанное на методе Монте-Карло моделирование временных функций рассеяния точки и функций чувствительности для мезоскопической время-разрешенной флуоресцентной молекулярной томографии

Основанное на методе Монте-Карло моделирование временных функций рассеяния точки и функций чувствительности для мезоскопической время-разрешенной флуоресцентной молекулярной томографии

Автор: Самарин С.И., Коновалов А.Б., Власов В.В., Соловьев И.Д., Савицкий А.П., Тучин В.В.

Журнал: Компьютерная оптика @computer-optics

Рубрика: Дифракционная оптика, оптические технологии

Статья в выпуске: 5 т.47, 2023 года.

Бесплатный доступ

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.

Еще

Программа turbidmc, метод монте-карло, флуоресцентная молекулярная томография, временные функции рассеяния точки, функции чувствительности, коэффициент поглощения флуорофора, время жизни флуоресценции

Короткий адрес: https://sciup.org/140301852

IDR: 140301852   |   DOI: 10.18287/2412-6179-CO-1295

Monte Carlo modeling of temporal point spread functions and sensitivity functions for mesoscopic time-resolved fluorescence molecular tomography

The paper describes a TurbidMC code that implements a perturbative Monte Carlo method to model temporal point spread functions and sensitivity functions for time-resolved fluorescence molecular tomography (FMT). The code is aimed at working with a particular FMT method published earlier (Ref. [22]) which defines the specificity of sensitivity function calculation. The method solves the inverse problem first for a generalized fluorescence parameter distribution function and then calculates separate distributions for the fluorophore absorption coefficient and the fluorescence lifetime. The proper operation of the code was verified through a comparison between fluorescence temporal point spread functions from test calculations and data from experiments where a phantom with a fluorophore was scanned with a three-channel probe in the mesoscopic reflectance regime. An example is given on the reconstruction of fluorescence parameter distributions. It shows that the sensitivity functions are calculated correctly.

Еще

Список литературы Основанное на методе Монте-Карло моделирование временных функций рассеяния точки и функций чувствительности для мезоскопической время-разрешенной флуоресцентной молекулярной томографии

  • Gao F, Zhao H-J, Tanikawa Y, Yamada Y. A linear, featured- data scheme for image reconstruction in timedomain fluorescence molecular tomography. Opt Express 2006; 14(16): 7109-7124. DOI: 10.1364/OE.14.007109.
  • 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.
  • 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.
  • 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.
  • 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.
  • Raymond SB, Boas DA, Bacskai BJ, Kumar ATN. Lifetime-based tomographic multiplexing. J Biomed Opt 2010; 15(4): 046011. DOI: 10.1117/1.3469797.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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): 12361243. DOI: 10.1158/0008-5472.CAN-14-3001.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Becker W. Fluorescence lifetime imaging - techniques and applications. J Microsc 2012; 247(Part2): 119-136. DOI: 10.1111/j.1365-2818.2012.03618.x.
  • 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.
  • 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.
  • 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.
  • Konovalov AB, Vlasov VV, Samarin SI, Soloviev ID, Sa-vitsky 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.
  • 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.
  • 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.
  • 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.
  • Tang Q, Tsytsarev V, Frank A, Wu Y, Chen C-W, Er-zurumlu RS, Chen Y. In vivo mesoscopic voltage-sensitive dye imaging of brain activation. Sci Rep 2016; 6: 25269. DOI: 10.1038/srep25269.
  • 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.JB0.22.1.016003.
  • 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.
  • Arridge SR, Schotland JC. Optical tomography: forward and inverse problems. Inverse Probl 2009; 25(12): 123010. DOI: 10.1088/0266-5611/25/12/123010.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Crilly RJ, Cheong W-F, Wilson B, Spears JR. Forward-adjoint fluorescence model: Monte Carlo integration and experimental validation. Appl Opt 1997; 36(25): 65136519. DOI: 10.1364/AO.36.006513.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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): 3194831965. DOI: 10.1364/OE.22.031948.
  • Yao R, Intes X, Fang Q. Direct approach to compute Jaco-bians 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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): 17371744. DOI: 10.1016/S0969-8043(98)00055-4.
  • 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.
  • Briesmeister J. MCNP - a general Monte Carlo N-particle transport code. Los Alamos National Laboratory Report LA-13709-M 2000.
  • 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.
  • Lux I, Koblinger L. Monte-Carlo transport methods: Neutron and photon calculations. Boca Raton: CRC Press; 2000. ISBN: 0-8493-6074-9.
  • 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.
  • Samarin SI. Certificate of Governmental Registration of Computer Program in FIPS. No. 2018666251; 2018.
  • Born M, Wolf E. Principles of optics. 7th ed. Cambridge: Cambridge University Press; 1999. ISBN: 0-521-64222-1.
  • Sobol IM. Numerical Monte Carlo methods [In Russian]. Moscow: "Nauka" Publisher; 1973.
  • Henyey IG, Greenstein JI. Diffuse radiation in the galaxy. Astrophys J 1941; 93: 70-83. DOI: 10.1086/144246.
  • Akkerman AF. Modeling of charge particle trajectories in matter [In Russian]. Moscow: "Energoatomizdat" Publisher; 1991. ISBN: 5-283-02924-7.
  • 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.
  • 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: 10.1088/00319155/47/12/308.
  • 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): 60006008. DOI: 10.1016/j.ijleo.2013.04.111.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
Еще
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp