Abstract

Monte Carlo methods are commonly used as the gold standard in modeling photon transport through turbid media. With the rapid development of structured light applications, an accurate and efficient method capable of simulating arbitrary illumination patterns and complex detection schemes over large surface area is in great need. Here we report a generalized mesh-based Monte Carlo algorithm to support a variety of wide-field illumination methods, including spatial-frequency-domain imaging (SFDI) patterns and arbitrary 2-D patterns. The extended algorithm can also model wide-field detectors such as a free-space CCD camera. The significantly enhanced flexibility of source and detector modeling is achieved via a fast mesh retessellation process that combines the target domain and the source/detector space in a single tetrahedral mesh. Both simulations of complex domains and comparisons with phantom measurements are included to demonstrate the flexibility, efficiency and accuracy of the extended algorithm. Our updated open-source software is provided at http://mcx.space/mmc.

© 2015 Optical Society of America

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References

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2015 (5)

2014 (5)

A. Behrooz, A. A. Eftekhar, and A. Adibi, “Hadamard multiplexed fluorescence tomography,” Biomed. Opt. Express 5(3), 763–777 (2014).
[Crossref] [PubMed]

L. Zhao, K. Abe, S. Rajoria, Q. Pian, M. Barroso, and X. Intes, “Spatial light modulator based active wide-field illumination for ex vivo and in vivo quantitative NIR FRET imaging,” Biomed. Opt. Express 5(3), 944–960 (2014).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

A. R. Gardner, C. K. Hayakawa, and V. Venugopalan, “Coupled forward-adjoint Monte Carlo simulation of spatial-angular light fields to determine optical sensitivity in turbid media,” J. Biomed. Opt. 19(6), 065003 (2014).
[Crossref] [PubMed]

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

2013 (8)

C. Zhu and Q. Liu, “Review of Monte Carlo modeling of light transport in tissues,” J. Biomed. Opt. 18(5), 050902 (2013).
[Crossref] [PubMed]

M. S. Ozturk, V. K. Lee, L. Zhao, G. Dai, and X. Intes, “Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue,” J. Biomed. Opt. 18(10), 100501 (2013).
[Crossref] [PubMed]

V. Venugopal, J. Chen, and X. Intes, “Robust imaging strategies in time-resolved optical tomography,” Proc. SPIE 8578, 857827 (2013).

L. Cao and J. Peter, “Investigating line- versus point-laser excitation for three-dimensional fluorescence imaging and tomography employing a trimodal imaging system,” J. Biomed. Opt. 18(6), 066015 (2013).
[Crossref] [PubMed]

V. Venugopal and X. Intes, “Adaptive wide-field optical tomography,” J. Biomed. Opt. 18(3), 036006 (2013).
[Crossref] [PubMed]

A. T. N. Kumar, “Fluorescence lifetime detection in turbid media using spatial frequency domain filtering of time domain measurements,” Opt. Lett. 38(9), 1440–1442 (2013).
[Crossref] [PubMed]

J. Nguyen, C. K. Hayakawa, J. R. Mourant, and J. Spanier, “Perturbation Monte Carlo methods for tissue structure alterations,” Biomed. Opt. Express 4(10), 1946–1963 (2013).
[Crossref] [PubMed]

J. Vervandier and S. Gioux, “Single snapshot imaging of optical properties,” Biomed. Opt. Express 4(12), 2938–2944 (2013).
[Crossref] [PubMed]

2012 (5)

V. Venugopal, J. Chen, M. Barroso, and X. Intes, “Quantitative tomographic imaging of intermolecular FRET in small animals,” Biomed. Opt. Express 3(12), 3161–3175 (2012).
[Crossref] [PubMed]

Q. Fang and D. R. Kaeli, “Accelerating mesh-based Monte Carlo method on modern CPU architectures,” Biomed. Opt. Express 3(12), 3223–3230 (2012).
[Crossref] [PubMed]

M. Pimpalkhare, J. Chen, V. Venugopal, and X. Intes, “Ex vivo fluorescence molecular tomography of the spine,” Int. J. Biomed. Imaging 2012, 942326 (2012).
[Crossref] [PubMed]

J. Chen, Q. Fang, and X. Intes, “Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography,” J. Biomed. Opt. 17(10), 106009 (2012).
[Crossref] [PubMed]

N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
[Crossref] [PubMed]

2011 (4)

2010 (13)

T. J. Rudge, V. Y. Soloviev, and S. R. Arridge, “Fast image reconstruction in fluorescence optical tomography using data compression,” Opt. Lett. 35(5), 763–765 (2010).
[Crossref] [PubMed]

N. Ren, J. Liang, X. Qu, J. Li, B. Lu, and J. Tian, “GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues,” Opt. Express 18(7), 6811–6823 (2010).
[Crossref] [PubMed]

J. Chen, V. Venugopal, F. Lesage, and X. Intes, “Time-resolved diffuse optical tomography with patterned-light illumination and detection,” Opt. Lett. 35(13), 2121–2123 (2010).
[Crossref] [PubMed]

V. Venugopal, J. Chen, and X. Intes, “Development of an optical imaging platform for functional imaging of small animals using wide-field excitation,” Biomed. Opt. Express 1(1), 143–156 (2010).
[Crossref] [PubMed]

Q. Fang, “Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates,” Biomed. Opt. Express 1(1), 165–175 (2010).
[Crossref] [PubMed]

C. D’Andrea, N. Ducros, A. Bassi, S. Arridge, and G. Valentini, “Fast 3D optical reconstruction in turbid media using spatially modulated light,” Biomed. Opt. Express 1(2), 471–481 (2010).
[Crossref] [PubMed]

V. Venugopal, J. Chen, F. Lesage, and X. Intes, “Full-field time-resolved fluorescence tomography of small animals,” Opt. Lett. 35(19), 3189–3191 (2010).
[Crossref] [PubMed]

N. Ducros, C. D’andrea, G. Valentini, T. Rudge, S. Arridge, and A. Bassi, “Full-wavelet approach for fluorescence diffuse optical tomography with structured illumination,” Opt. Lett. 35(21), 3676–3678 (2010).
[Crossref] [PubMed]

H. Shen and G. Wang, “A tetrahedron-based inhomogeneous Monte Carlo optical simulator,” Phys. Med. Biol. 55(4), 947–962 (2010).
[Crossref] [PubMed]

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref] [PubMed]

V. Ntziachristos, “Going deeper than microscopy: the optical imaging frontier in biology,” Nat. Methods 7(8), 603–614 (2010).
[Crossref] [PubMed]

S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15, 016006 (2010).

J. Dutta, S. Ahn, A. A. Joshi, and R. M. Leahy, “Illumination pattern optimization for fluorescence tomography: theory and simulation studies,” Phys. Med. Biol. 55(10), 2961–2982 (2010).
[Crossref] [PubMed]

2009 (6)

2008 (3)

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt. 13(4), 041304 (2008).
[Crossref] [PubMed]

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13, 060504 (2008).

M. J. Niedre, R. H. de Kleine, E. Aikawa, D. G. Kirsch, R. Weissleder, and V. Ntziachristos, “Early photon tomography allows fluorescence detection of lung carcinomas and disease progression in mice in vivo,” Proc. Natl. Acad. Sci. U.S.A. 105(49), 19126–19131 (2008).
[Crossref] [PubMed]

2007 (3)

C. K. Hayakawa, J. Spanier, and V. Venugopalan, “Coupled Forward-Adjoint Monte Carlo Simulations of Radiative Transport for the Study of Optical Probe Design in Heterogeneous Tissues,” SIAM J. Appl. Math. 68(1), 253–270 (2007).
[Crossref]

J. Heiskala, K. Kotilahti, L. Lipiäinen, P. Hiltunen, P. E. Grant, and I. Nissilä, “Optical tomographic imaging of activation of the infant auditory cortex using perturbation Monte Carlo with anatomical a priori information,” Proc. SPIE 6629, 66290T (2007).
[Crossref]

E. Margallo-Balbás and P. J. French, “Shape based Monte Carlo code for light transport in complex heterogeneous Tissues,” Opt. Express 15(21), 14086–14098 (2007).
[Crossref] [PubMed]

2006 (2)

A. Joshi, W. Bangerth, and E. M. Sevick-Muraca, “Non-contact fluorescence optical tomography with scanning patterned illumination,” Opt. Express 14(14), 6516–6534 (2006).
[Crossref] [PubMed]

V. Hubert-Tremblay, L. Archambault, D. Tubic, R. Roy, and L. Beaulieu, “Octree indexing of DICOM images for voxel number reduction and improvement of Monte Carlo simulation computing efficiency,” Med. Phys. 33(8), 2819–2831 (2006).
[Crossref] [PubMed]

2005 (1)

2004 (1)

Y. P. Kumar and R. M. Vasu, “Reconstruction of optical properties of low-scattering tissue using derivative estimated through perturbation Monte-Carlo method,” J. Biomed. Opt. 9(5), 1002–1012 (2004).
[Crossref] [PubMed]

2003 (1)

I. Wald, T. J. Purcell, J. Schmittler, C. Benthin, and P. Slusallek, “Realtime ray tracing and its use for interactive global illumination,” Eurographics State of the Art Reports 1, 5 (2003).

2002 (1)

2001 (2)

1997 (2)

R. J. Crilly, W.-F. Cheong, B. Wilson, and J. R. Spears, “Forward-adjoint fluorescence model: Monte Carlo integration and experimental validation,” Appl. Opt. 36(25), 6513–6519 (1997).
[Crossref] [PubMed]

J. Wu, L. Perelman, R. R. Dasari, and M. S. Feld, “Fluorescence tomographic imaging in turbid media using early-arriving photons and Laplace transforms,” Proc. Natl. Acad. Sci. U.S.A. 94(16), 8783–8788 (1997).
[Crossref] [PubMed]

1995 (1)

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[Crossref] [PubMed]

1989 (1)

L. P. Chew, “Constrained delaunay triangulations,” Algorithmica 4(1-4), 97–108 (1989).
[Crossref]

Abe, K.

Abran, M.

S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15, 016006 (2010).

Adibi, A.

Ahn, S.

J. Dutta, S. Ahn, A. A. Joshi, and R. M. Leahy, “Illumination pattern optimization for fluorescence tomography: theory and simulation studies,” Phys. Med. Biol. 55(10), 2961–2982 (2010).
[Crossref] [PubMed]

Aikawa, E.

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J. Chen, Q. Fang, and X. Intes, “Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography,” J. Biomed. Opt. 17(10), 106009 (2012).
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J. Chen, V. Venugopal, and X. Intes, “Monte Carlo based method for fluorescence tomographic imaging with lifetime multiplexing using time gates,” Biomed. Opt. Express 2(4), 871–886 (2011).
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J. Chen, V. Venugopal, F. Lesage, and X. Intes, “Time-resolved diffuse optical tomography with patterned-light illumination and detection,” Opt. Lett. 35(13), 2121–2123 (2010).
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M. S. Ozturk, V. K. Lee, L. Zhao, G. Dai, and X. Intes, “Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue,” J. Biomed. Opt. 18(10), 100501 (2013).
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J. Wu, L. Perelman, R. R. Dasari, and M. S. Feld, “Fluorescence tomographic imaging in turbid media using early-arriving photons and Laplace transforms,” Proc. Natl. Acad. Sci. U.S.A. 94(16), 8783–8788 (1997).
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W. L. Rice, D. M. Shcherbakova, V. V. Verkhusha, and A. T. Kumar, “In Vivo Tomographic Imaging of Deep-Seated Cancer Using Fluorescence Lifetime Contrast,” Cancer Res. 75(7), 1236–1243 (2015).
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A. R. Gardner, C. K. Hayakawa, and V. Venugopalan, “Coupled forward-adjoint Monte Carlo simulation of spatial-angular light fields to determine optical sensitivity in turbid media,” J. Biomed. Opt. 19(6), 065003 (2014).
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C. K. Hayakawa, J. Spanier, F. Bevilacqua, A. K. Dunn, J. S. You, B. J. Tromberg, and V. Venugopalan, “Perturbation Monte Carlo methods to solve inverse photon migration problems in heterogeneous tissues,” Opt. Lett. 26(17), 1335–1337 (2001).
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Verkhusha, V. V.

W. L. Rice, D. M. Shcherbakova, V. V. Verkhusha, and A. T. Kumar, “In Vivo Tomographic Imaging of Deep-Seated Cancer Using Fluorescence Lifetime Contrast,” Cancer Res. 75(7), 1236–1243 (2015).
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Vervandier, J.

Wald, I.

I. Wald, T. J. Purcell, J. Schmittler, C. Benthin, and P. Slusallek, “Realtime ray tracing and its use for interactive global illumination,” Eurographics State of the Art Reports 1, 5 (2003).

Wang, G.

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

H. Shen and G. Wang, “A tetrahedron-based inhomogeneous Monte Carlo optical simulator,” Phys. Med. Biol. 55(4), 947–962 (2010).
[Crossref] [PubMed]

Wang, L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
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Weissleder, R.

M. J. Niedre, R. H. de Kleine, E. Aikawa, D. G. Kirsch, R. Weissleder, and V. Ntziachristos, “Early photon tomography allows fluorescence detection of lung carcinomas and disease progression in mice in vivo,” Proc. Natl. Acad. Sci. U.S.A. 105(49), 19126–19131 (2008).
[Crossref] [PubMed]

Wilson, B.

Wu, J.

J. Wu, L. Perelman, R. R. Dasari, and M. S. Feld, “Fluorescence tomographic imaging in turbid media using early-arriving photons and Laplace transforms,” Proc. Natl. Acad. Sci. U.S.A. 94(16), 8783–8788 (1997).
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Yang, H.

Yao, R.

Yodh, A. G.

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
[Crossref] [PubMed]

You, J. S.

Yuan, S.

Zhao, L.

Zheng, L.

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
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Zhu, C.

C. Zhu and Q. Liu, “Review of Monte Carlo modeling of light transport in tissues,” J. Biomed. Opt. 18(5), 050902 (2013).
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ACM Trans. Math. Softw. (1)

H. Si, “TetGen, a Delaunay-based quality tetrahedral mesh generator,” ACM Trans. Math. Softw. 41(2), 11 (2015).
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Algorithmica (1)

L. P. Chew, “Constrained delaunay triangulations,” Algorithmica 4(1-4), 97–108 (1989).
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Appl. Opt. (1)

Biomed. Opt. Express (12)

V. Venugopal, J. Chen, and X. Intes, “Development of an optical imaging platform for functional imaging of small animals using wide-field excitation,” Biomed. Opt. Express 1(1), 143–156 (2010).
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V. Venugopal, J. Chen, M. Barroso, and X. Intes, “Quantitative tomographic imaging of intermolecular FRET in small animals,” Biomed. Opt. Express 3(12), 3161–3175 (2012).
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J. Chen, V. Venugopal, and X. Intes, “Monte Carlo based method for fluorescence tomographic imaging with lifetime multiplexing using time gates,” Biomed. Opt. Express 2(4), 871–886 (2011).
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Q. Fang, “Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates,” Biomed. Opt. Express 1(1), 165–175 (2010).
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J. Nguyen, C. K. Hayakawa, J. R. Mourant, and J. Spanier, “Perturbation Monte Carlo methods for tissue structure alterations,” Biomed. Opt. Express 4(10), 1946–1963 (2013).
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Q. Fang and D. R. Kaeli, “Accelerating mesh-based Monte Carlo method on modern CPU architectures,” Biomed. Opt. Express 3(12), 3223–3230 (2012).
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A. Behrooz, A. A. Eftekhar, and A. Adibi, “Hadamard multiplexed fluorescence tomography,” Biomed. Opt. Express 5(3), 763–777 (2014).
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Q. Fang, “Comment on “A study on tetrahedron-based inhomogeneous Monte-Carlo optical simulation”,” Biomed. Opt. Express 2(5), 1258–1264 (2011).
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J. Vervandier and S. Gioux, “Single snapshot imaging of optical properties,” Biomed. Opt. Express 4(12), 2938–2944 (2013).
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R. Yao, Q. Pian, and X. Intes, “Wide-field fluorescence molecular tomography with compressive sensing based preconditioning,” Biomed. Opt. Express 6(12), 4887–4898 (2015).
[Crossref]

C. D’Andrea, N. Ducros, A. Bassi, S. Arridge, and G. Valentini, “Fast 3D optical reconstruction in turbid media using spatially modulated light,” Biomed. Opt. Express 1(2), 471–481 (2010).
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L. Zhao, K. Abe, S. Rajoria, Q. Pian, M. Barroso, and X. Intes, “Spatial light modulator based active wide-field illumination for ex vivo and in vivo quantitative NIR FRET imaging,” Biomed. Opt. Express 5(3), 944–960 (2014).
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Cancer Res. (1)

W. L. Rice, D. M. Shcherbakova, V. V. Verkhusha, and A. T. Kumar, “In Vivo Tomographic Imaging of Deep-Seated Cancer Using Fluorescence Lifetime Contrast,” Cancer Res. 75(7), 1236–1243 (2015).
[Crossref] [PubMed]

Comput. Methods Programs Biomed. (1)

L. Wang, S. L. Jacques, and L. Zheng, “MCML--Monte Carlo modeling of light transport in multi-layered tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[Crossref] [PubMed]

Eurographics State of the Art Reports (1)

I. Wald, T. J. Purcell, J. Schmittler, C. Benthin, and P. Slusallek, “Realtime ray tracing and its use for interactive global illumination,” Eurographics State of the Art Reports 1, 5 (2003).

Int. J. Biomed. Imaging (1)

M. Pimpalkhare, J. Chen, V. Venugopal, and X. Intes, “Ex vivo fluorescence molecular tomography of the spine,” Int. J. Biomed. Imaging 2012, 942326 (2012).
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Inverse Probl. (1)

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
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J. Biomed. Opt. (10)

C. Zhu and Q. Liu, “Review of Monte Carlo modeling of light transport in tissues,” J. Biomed. Opt. 18(5), 050902 (2013).
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M. S. Ozturk, V. K. Lee, L. Zhao, G. Dai, and X. Intes, “Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue,” J. Biomed. Opt. 18(10), 100501 (2013).
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A. R. Gardner, C. K. Hayakawa, and V. Venugopalan, “Coupled forward-adjoint Monte Carlo simulation of spatial-angular light fields to determine optical sensitivity in turbid media,” J. Biomed. Opt. 19(6), 065003 (2014).
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E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt. 13(4), 041304 (2008).
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E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13, 060504 (2008).

Y. P. Kumar and R. M. Vasu, “Reconstruction of optical properties of low-scattering tissue using derivative estimated through perturbation Monte-Carlo method,” J. Biomed. Opt. 9(5), 1002–1012 (2004).
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J. Chen, Q. Fang, and X. Intes, “Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography,” J. Biomed. Opt. 17(10), 106009 (2012).
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S. Bélanger, M. Abran, X. Intes, C. Casanova, and F. Lesage, “Real-time diffuse optical tomography based on structured illumination,” J. Biomed. Opt. 15, 016006 (2010).

V. Venugopal and X. Intes, “Adaptive wide-field optical tomography,” J. Biomed. Opt. 18(3), 036006 (2013).
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L. Cao and J. Peter, “Investigating line- versus point-laser excitation for three-dimensional fluorescence imaging and tomography employing a trimodal imaging system,” J. Biomed. Opt. 18(6), 066015 (2013).
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J. Opt. Soc. Am. A (1)

Med. Phys. (2)

J. Chen and X. Intes, “Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency,” Med. Phys. 38(10), 5788–5798 (2011).
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V. Hubert-Tremblay, L. Archambault, D. Tubic, R. Roy, and L. Beaulieu, “Octree indexing of DICOM images for voxel number reduction and improvement of Monte Carlo simulation computing efficiency,” Med. Phys. 33(8), 2819–2831 (2006).
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Nat. Methods (1)

V. Ntziachristos, “Going deeper than microscopy: the optical imaging frontier in biology,” Nat. Methods 7(8), 603–614 (2010).
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Opt. Express (8)

D. Boas, J. Culver, J. Stott, and A. Dunn, “Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head,” Opt. Express 10(3), 159–170 (2002).
[Crossref] [PubMed]

J. Chen and X. Intes, “Time-gated perturbation Monte Carlo for whole body functional imaging in small animals,” Opt. Express 17(22), 19566–19579 (2009).
[Crossref] [PubMed]

E. Margallo-Balbás and P. J. French, “Shape based Monte Carlo code for light transport in complex heterogeneous Tissues,” Opt. Express 15(21), 14086–14098 (2007).
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N. Ren, J. Liang, X. Qu, J. Li, B. Lu, and J. Tian, “GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues,” Opt. Express 18(7), 6811–6823 (2010).
[Crossref] [PubMed]

Q. Fang and D. A. Boas, “Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units,” Opt. Express 17(22), 20178–20190 (2009).
[Crossref] [PubMed]

S. D. Konecky, A. Mazhar, D. Cuccia, A. J. Durkin, J. C. Schotland, and B. J. Tromberg, “Quantitative optical tomography of sub-surface heterogeneities using spatially modulated structured light,” Opt. Express 17(17), 14780–14790 (2009).
[Crossref] [PubMed]

A. Joshi, W. Bangerth, and E. M. Sevick-Muraca, “Non-contact fluorescence optical tomography with scanning patterned illumination,” Opt. Express 14(14), 6516–6534 (2006).
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V. Durán, F. Soldevila, E. Irles, P. Clemente, E. Tajahuerce, P. Andrés, and J. Lancis, “Compressive imaging in scattering media,” Opt. Express 23(11), 14424–14433 (2015).
[Crossref] [PubMed]

Opt. Lett. (11)

S. Yuan, Q. Li, J. Jiang, A. Cable, and Y. Chen, “Three-dimensional coregistered optical coherence tomography and line-scanning fluorescence laminar optical tomography,” Opt. Lett. 34(11), 1615–1617 (2009).
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N. Ducros, C. D’andrea, G. Valentini, T. Rudge, S. Arridge, and A. Bassi, “Full-wavelet approach for fluorescence diffuse optical tomography with structured illumination,” Opt. Lett. 35(21), 3676–3678 (2010).
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Q. Pian, R. Yao, L. Zhao, and X. Intes, “Hyperspectral time-resolved wide-field fluorescence molecular tomography based on structured light and single-pixel detection,” Opt. Lett. 40(3), 431–434 (2015).
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T. J. Rudge, V. Y. Soloviev, and S. R. Arridge, “Fast image reconstruction in fluorescence optical tomography using data compression,” Opt. Lett. 35(5), 763–765 (2010).
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J. Chen, V. Venugopal, F. Lesage, and X. Intes, “Time-resolved diffuse optical tomography with patterned-light illumination and detection,” Opt. Lett. 35(13), 2121–2123 (2010).
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V. Venugopal, J. Chen, F. Lesage, and X. Intes, “Full-field time-resolved fluorescence tomography of small animals,” Opt. Lett. 35(19), 3189–3191 (2010).
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A. T. N. Kumar, “Fluorescence lifetime detection in turbid media using spatial frequency domain filtering of time domain measurements,” Opt. Lett. 38(9), 1440–1442 (2013).
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D. J. Cuccia, F. Bevilacqua, A. J. Durkin, and B. J. Tromberg, “Modulated imaging: quantitative analysis and tomography of turbid media in the spatial-frequency domain,” Opt. Lett. 30(11), 1354–1356 (2005).
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A. Sassaroli, “Fast perturbation Monte Carlo method for photon migration in heterogeneous turbid media,” Opt. Lett. 36(11), 2095–2097 (2011).
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C. K. Hayakawa, J. Spanier, F. Bevilacqua, A. K. Dunn, J. S. You, B. J. Tromberg, and V. Venugopalan, “Perturbation Monte Carlo methods to solve inverse photon migration problems in heterogeneous tissues,” Opt. Lett. 26(17), 1335–1337 (2001).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences (1)

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Phys. Med. Biol. (4)

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
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H. Shen and G. Wang, “A tetrahedron-based inhomogeneous Monte Carlo optical simulator,” Phys. Med. Biol. 55(4), 947–962 (2010).
[Crossref] [PubMed]

N. Ducros, C. D’Andrea, A. Bassi, G. Valentini, and S. Arridge, “A virtual source pattern method for fluorescence tomography with structured light,” Phys. Med. Biol. 57(12), 3811–3832 (2012).
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Proc. Natl. Acad. Sci. U.S.A. (2)

J. Wu, L. Perelman, R. R. Dasari, and M. S. Feld, “Fluorescence tomographic imaging in turbid media using early-arriving photons and Laplace transforms,” Proc. Natl. Acad. Sci. U.S.A. 94(16), 8783–8788 (1997).
[Crossref] [PubMed]

M. J. Niedre, R. H. de Kleine, E. Aikawa, D. G. Kirsch, R. Weissleder, and V. Ntziachristos, “Early photon tomography allows fluorescence detection of lung carcinomas and disease progression in mice in vivo,” Proc. Natl. Acad. Sci. U.S.A. 105(49), 19126–19131 (2008).
[Crossref] [PubMed]

Proc. SPIE (2)

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V. Venugopal, J. Chen, and X. Intes, “Robust imaging strategies in time-resolved optical tomography,” Proc. SPIE 8578, 857827 (2013).

Rep. Prog. Phys. (1)

T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010).
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SIAM J. Appl. Math. (1)

C. K. Hayakawa, J. Spanier, and V. Venugopalan, “Coupled Forward-Adjoint Monte Carlo Simulations of Radiative Transport for the Study of Optical Probe Design in Heterogeneous Tissues,” SIAM J. Appl. Math. 68(1), 253–270 (2007).
[Crossref]

Other (2)

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng.in press.

Q. Fang and D. A. Boas, “Tetrahedral mesh generation from volumetric binary and grayscale images,” in Biomedical Imaging: From Nano to Macro,2009. ISBI '09. IEEE Int. Symp. on, 2009), 1142–1145.

Supplementary Material (3)

NameDescription
» Visualization 1: AVI (29040 KB)      Comparison of time-dependent fluence profiles between collimated and non-collimated beam for the first 2.5 ns.
» Visualization 2: AVI (19312 KB)      The entire set of 36 experimental illumination patterns.
» Visualization 3: AVI (35632 KB)      Comparisons of simulation and experimental TPSF curves for 36 additional source-detector pairs.

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Figures (8)

Fig. 1
Fig. 1

Sample retessellated meshes for (a-c) a square-aperture wide-field source outside of a complex mouse atlas, and (d-f) a circular-aperture wide-field source with a square-shaped camera detector for an adult head model, where (a,d) show the object mesh before retessellation, (b,e) show the mesh after retessellation, and (c,f) show cross-cut views of the final meshes Notations: S – source domain; M – original object mesh; O – circumscribed circle of S; S’ – circumscribed triangle of S; C – convex hull of all nodes in M and S’; T – extended mesh bounded by C and the exterior surface of M; M’ – the final mesh after merging T with M; E0 – photon-launching element; D – a wide-field detector.

Fig. 2
Fig. 2

Summary of mesh retessellation and photon simulation run-times for two wide-field MMC implementations under various mesh density settings. (a) Time (s) for mesh retessellation, in log-log scale; (b) time (s) for simulation of AAPB-MMC and MR-MMC.

Fig. 3
Fig. 3

Comparisons between the simulation run-times for AAPB-MMC and MR-MMC at various illumination area sizes.

Fig. 4
Fig. 4

Simulation of a heterogeneous numerical phantom with a cubic inclusion, (a) cross-sectional view of the retessellated mesh, (b) CW fluence contour plots along plane y = 29.5 mm with 5 dB spacing and (c) TPSFs (log-scale shown in inset) measured at the black cross in (b) for MR-MMC, AAPB-MMC and MCX. The inclusion is shown in dashed lines.

Fig. 5
Fig. 5

Simulation of a heterogeneous numerical phantom with a spherical inclusion, (a) cross-sectional view of the retessellated mesh, (b) CW fluence contour plots along plane y = 29.5 mm with 5 dB spacing and (c) TPSFs (log-scale shown in inset) measured at the black cross in (b) for MR-MMC, AAPB-MMC and MCX. The inclusion is shown in dashed lines.

Fig. 6
Fig. 6

Comparison between collimated and non-collimated beam fluence profiles. (a) Retessellated mesh model with imaginary point source of a non-collimated beam, (b) TPSF curves at the detector with peak normalized to 1, and fluence contours of (c) CW, (d) early gates and (e) late gates. An animated time-dependent fluence profiles for the first 2.5 ns can be viewed in Visualization 1.

Fig. 7
Fig. 7

Retessellated mesh model and sample illumination patterns. (a) Mesh model in 3-D view; (b) side-view of the mesh model; (c) sample illumination pattern moving along the x-direction; (d) sample illumination pattern moving along the y-direction. The entire set of the illumination patterns are shown in Visualization 2.

Fig. 8
Fig. 8

Comparison of simulation and experimentally measured TPSF curves. (a) The 5th illumination and 6th detection pattern; (b) the 19th illumination and 28th detection pattern. Comparisons of 36 additional source-detector pairs are provided in Visualization 3.

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