Abstract

Optical coherence tomography (OCT) provides both structural and angiographic imaging modes. Because of its unique capabilities, OCT-based angiography has been increasingly adopted into small animal and human subject imaging. To support the development of the signal and image processing algorithms on which OCT-based angiography depends, we describe here a Monte Carlo-based model of the imaging approach. The model supports arbitrary three-dimensional vascular network geometries and incorporates methods to simulate OCT signal temporal decorrelation. With this model, it will be easier to compare the performance of existing and new angiographic signal processing algorithms, and to quantify the accuracy of vascular segmentation algorithms. The quantitative analysis of key algorithms within OCT-based angiography may, in turn, simplify the selection of algorithms in instrument design and accelerate the pace of new algorithm development.

© 2014 Optical Society of America

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References

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  1. B. J. Vakoc, D. Fukumura, R. K. Jain, and B. E. Bouma, “Cancer imaging by optical coherence tomography: preclinical progress and clinical potential,” Nat. Rev. Cancer 12(5), 363–368 (2012).
    [Crossref] [PubMed]
  2. B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
    [Crossref] [PubMed]
  3. G. Yao and L. Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44(9), 2307–2320 (1999).
    [Crossref] [PubMed]
  4. I. Meglinski, M. Kirillin, V. Kuzmin, and R. Myllyla, “Simulation of polarization-sensitive optical coherence tomography images by a Monte Carlo method,” Opt. Lett. 33(14), 1581–1583 (2008)
    [Crossref] [PubMed]
  5. 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]
  6. L. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (Wiley, 2007).
  7. R. A. Freitas, Nanomedicine, Vol. I: Basic Capabilities (Landes Bioscience, 1999).
  8. M. P. Wiedeman, “Lengths and diameters of peripheral arterial vessels in the living animal,” Circ. Reach. 10, 686–690 (1962).
    [Crossref]
  9. M. Y. Kirillin, G. Parhat, E. A. Sergeeva, M. C. Kolios, and A. Vitkin, “Speckle statistics in OCT images: Monte Carlo simulations and experimental studies,” Opt. Lett. 39(12), 3472–3475 (2014)
    [Crossref] [PubMed]
  10. A. Doronin and I. Meglinski, “Online object oriented Monte Carlo computational tool for the needs of biomedical optics,” Biomed. Opt. Express 2(9), 2461–2469 (2011).
    [Crossref] [PubMed]
  11. A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).
    [Crossref]
  12. A. N. Yaroslavsky, I. V. Yaroslavsky, T. Goldbach, and H.-J. Schwarzmair, “The optical properties of blood in the near infrared spectral range,” Proc. SPIE 2678, 314–332 (1996).
    [Crossref]
  13. M. Friebel, A. Roggan, G. Muller, and M. Meinke, “Determination of optical properties of human blood in the spectral range of 250 to 1100 nm using Monte Carlo simulations with hematocrit-dependent effective scattering phase functions,” J. Biomed. Opt. 11(3), 034021 (2006).
    [Crossref]

2014 (1)

2012 (1)

B. J. Vakoc, D. Fukumura, R. K. Jain, and B. E. Bouma, “Cancer imaging by optical coherence tomography: preclinical progress and clinical potential,” Nat. Rev. Cancer 12(5), 363–368 (2012).
[Crossref] [PubMed]

2011 (2)

A. Doronin and I. Meglinski, “Online object oriented Monte Carlo computational tool for the needs of biomedical optics,” Biomed. Opt. Express 2(9), 2461–2469 (2011).
[Crossref] [PubMed]

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).
[Crossref]

2009 (1)

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

2008 (1)

2006 (1)

M. Friebel, A. Roggan, G. Muller, and M. Meinke, “Determination of optical properties of human blood in the spectral range of 250 to 1100 nm using Monte Carlo simulations with hematocrit-dependent effective scattering phase functions,” J. Biomed. Opt. 11(3), 034021 (2006).
[Crossref]

1999 (1)

G. Yao and L. Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44(9), 2307–2320 (1999).
[Crossref] [PubMed]

1996 (1)

A. N. Yaroslavsky, I. V. Yaroslavsky, T. Goldbach, and H.-J. Schwarzmair, “The optical properties of blood in the near infrared spectral range,” Proc. SPIE 2678, 314–332 (1996).
[Crossref]

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]

1962 (1)

M. P. Wiedeman, “Lengths and diameters of peripheral arterial vessels in the living animal,” Circ. Reach. 10, 686–690 (1962).
[Crossref]

Bartlett, L. A.

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Bashkatov, A. N.

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).
[Crossref]

Bouma, B. E.

B. J. Vakoc, D. Fukumura, R. K. Jain, and B. E. Bouma, “Cancer imaging by optical coherence tomography: preclinical progress and clinical potential,” Nat. Rev. Cancer 12(5), 363–368 (2012).
[Crossref] [PubMed]

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Doronin, A.

Freitas, R. A.

R. A. Freitas, Nanomedicine, Vol. I: Basic Capabilities (Landes Bioscience, 1999).

Friebel, M.

M. Friebel, A. Roggan, G. Muller, and M. Meinke, “Determination of optical properties of human blood in the spectral range of 250 to 1100 nm using Monte Carlo simulations with hematocrit-dependent effective scattering phase functions,” J. Biomed. Opt. 11(3), 034021 (2006).
[Crossref]

Fukumura, D.

B. J. Vakoc, D. Fukumura, R. K. Jain, and B. E. Bouma, “Cancer imaging by optical coherence tomography: preclinical progress and clinical potential,” Nat. Rev. Cancer 12(5), 363–368 (2012).
[Crossref] [PubMed]

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Genina, E. A.

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).
[Crossref]

Goldbach, T.

A. N. Yaroslavsky, I. V. Yaroslavsky, T. Goldbach, and H.-J. Schwarzmair, “The optical properties of blood in the near infrared spectral range,” Proc. SPIE 2678, 314–332 (1996).
[Crossref]

Jacques, S. 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).
[Crossref] [PubMed]

Jain, R. K.

B. J. Vakoc, D. Fukumura, R. K. Jain, and B. E. Bouma, “Cancer imaging by optical coherence tomography: preclinical progress and clinical potential,” Nat. Rev. Cancer 12(5), 363–368 (2012).
[Crossref] [PubMed]

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Kirillin, M.

Kirillin, M. Y.

Kolios, M. C.

Kuzmin, V.

Lanning, R. M.

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Meglinski, I.

Meinke, M.

M. Friebel, A. Roggan, G. Muller, and M. Meinke, “Determination of optical properties of human blood in the spectral range of 250 to 1100 nm using Monte Carlo simulations with hematocrit-dependent effective scattering phase functions,” J. Biomed. Opt. 11(3), 034021 (2006).
[Crossref]

Muller, G.

M. Friebel, A. Roggan, G. Muller, and M. Meinke, “Determination of optical properties of human blood in the spectral range of 250 to 1100 nm using Monte Carlo simulations with hematocrit-dependent effective scattering phase functions,” J. Biomed. Opt. 11(3), 034021 (2006).
[Crossref]

Munn, L. L.

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Myllyla, R.

Padera, T. P.

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Parhat, G.

Roggan, A.

M. Friebel, A. Roggan, G. Muller, and M. Meinke, “Determination of optical properties of human blood in the spectral range of 250 to 1100 nm using Monte Carlo simulations with hematocrit-dependent effective scattering phase functions,” J. Biomed. Opt. 11(3), 034021 (2006).
[Crossref]

Schwarzmair, H.-J.

A. N. Yaroslavsky, I. V. Yaroslavsky, T. Goldbach, and H.-J. Schwarzmair, “The optical properties of blood in the near infrared spectral range,” Proc. SPIE 2678, 314–332 (1996).
[Crossref]

Sergeeva, E. A.

Stylianopoulos, T.

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Tearney, G. J.

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Tuchin, V. V.

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).
[Crossref]

Tyrrell, J. A.

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Vakoc, B. J.

B. J. Vakoc, D. Fukumura, R. K. Jain, and B. E. Bouma, “Cancer imaging by optical coherence tomography: preclinical progress and clinical potential,” Nat. Rev. Cancer 12(5), 363–368 (2012).
[Crossref] [PubMed]

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Vitkin, A.

Wang, L.

G. Yao and L. Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44(9), 2307–2320 (1999).
[Crossref] [PubMed]

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]

L. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (Wiley, 2007).

Wiedeman, M. P.

M. P. Wiedeman, “Lengths and diameters of peripheral arterial vessels in the living animal,” Circ. Reach. 10, 686–690 (1962).
[Crossref]

Wu, H.-I.

L. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (Wiley, 2007).

Yao, G.

G. Yao and L. Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44(9), 2307–2320 (1999).
[Crossref] [PubMed]

Yaroslavsky, A. N.

A. N. Yaroslavsky, I. V. Yaroslavsky, T. Goldbach, and H.-J. Schwarzmair, “The optical properties of blood in the near infrared spectral range,” Proc. SPIE 2678, 314–332 (1996).
[Crossref]

Yaroslavsky, I. V.

A. N. Yaroslavsky, I. V. Yaroslavsky, T. Goldbach, and H.-J. Schwarzmair, “The optical properties of blood in the near infrared spectral range,” Proc. SPIE 2678, 314–332 (1996).
[Crossref]

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).
[Crossref] [PubMed]

Biomed. Opt. Express (1)

Circ. Reach. (1)

M. P. Wiedeman, “Lengths and diameters of peripheral arterial vessels in the living animal,” Circ. Reach. 10, 686–690 (1962).
[Crossref]

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]

J. Biomed. Opt. (1)

M. Friebel, A. Roggan, G. Muller, and M. Meinke, “Determination of optical properties of human blood in the spectral range of 250 to 1100 nm using Monte Carlo simulations with hematocrit-dependent effective scattering phase functions,” J. Biomed. Opt. 11(3), 034021 (2006).
[Crossref]

J. Innov. Opt. Health Sci. (1)

A. N. Bashkatov, E. A. Genina, and V. V. Tuchin, “Optical properties of skin, subcutaneous, and muscle tissues: a review,” J. Innov. Opt. Health Sci. 4(1), 9–38 (2011).
[Crossref]

Nat. Med. (1)

B. J. Vakoc, R. M. Lanning, J. A. Tyrrell, T. P. Padera, L. A. Bartlett, T. Stylianopoulos, L. L. Munn, G. J. Tearney, D. Fukumura, R. K. Jain, and B. E. Bouma, “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat. Med. 15(10), 1219–1223 (2009).
[Crossref] [PubMed]

Nat. Rev. Cancer (1)

B. J. Vakoc, D. Fukumura, R. K. Jain, and B. E. Bouma, “Cancer imaging by optical coherence tomography: preclinical progress and clinical potential,” Nat. Rev. Cancer 12(5), 363–368 (2012).
[Crossref] [PubMed]

Opt. Lett. (2)

Phys. Med. Biol. (1)

G. Yao and L. Wang, “Monte Carlo simulation of an optical coherence tomography signal in homogeneous turbid media,” Phys. Med. Biol. 44(9), 2307–2320 (1999).
[Crossref] [PubMed]

Proc. SPIE (1)

A. N. Yaroslavsky, I. V. Yaroslavsky, T. Goldbach, and H.-J. Schwarzmair, “The optical properties of blood in the near infrared spectral range,” Proc. SPIE 2678, 314–332 (1996).
[Crossref]

Other (2)

L. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (Wiley, 2007).

R. A. Freitas, Nanomedicine, Vol. I: Basic Capabilities (Landes Bioscience, 1999).

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Figures (6)

Fig. 1
Fig. 1 Absorption distribution in a two-layered medium simulated using 10 million photon packets. (a) Model of the two layered medium. (b) Absorption distribution in the model simulated with an existing MC code for multilayer samples [5]. (c) Model of the two layered medium comprising two different cylindrical regions at a depth of 750 μm and 250 μm. Optical properties of the cylindrical regions are identical to surrounding tissue. (d) Absorption distribution in the model simulated with the new MC model. Scale bars correspond to 100 μm. Colorbar is in logarithmic scale (dB).
Fig. 2
Fig. 2 Simulated A-line signals. (a) Interference signal, ID(k), as a function of wavelength and corresponding (b) OCT signal, iD(z), as a function of depth following Fourier transform.
Fig. 3
Fig. 3 Simulated blood vessels. (a) Two blood vessels modeled at a depth of 250 μm and 200 μm. (b) Structural OCT image of two cylindrical blood vessels (outlined in yellow) surrounded by a homogeneous sample. The parameter μs for blood is fixed to 650 cm−1, μa, to 5 cm−1, g, to 0.9888, and n, to 1.37. The parameter μs for the sample is set to 10 cm−1, μa, to 1 cm−1, g, to 0.7, and n, to 1.37. Scale bars correspond to 100 μm.
Fig. 4
Fig. 4 Flow chart of the MC algorithm.
Fig. 5
Fig. 5 Cross-sectional OCT-based images of two blood vessels simulated using different optical properties. Top row shows structural images while bottom row presents angiography images. Optical properties of the skin and blood were extracted from literature. For normal skin, values were set according to [11] for a wavelength of 1,200 nm. The scattering coefficient, μs, was fixed to 16 cm−1, the absorption coefficient, μa, to 5 cm−1, and the anisotropy factor, g, to 0.715. Values for blood were chosen according to prior studies [12, 13] where optical properties were measured for whole blood with physiological hematocrit percentages of 42% and 46%, respectively. (a, e) Simulated vessels obtained with μs for blood fixed to 650 cm−1, μa, to 5 cm−1, and g, to 0.97 [13]. (b, f) Simulated vessels generated with μs for blood set to 650 cm−1, μa, to 5 cm−1, and g, to 0.9888. (c, g) Simulated vessels obtained with μs for blood equal to 650 cm−1, μa, to 5 cm−1, and g, to 0.995 [12]. (d, h) Experimental images of skin vessels acquired with an existing OCT-based system on a murine model. Scale bars correspond to 100 μm.
Fig. 6
Fig. 6 Comparison of angiography images showing two blood vessels. Histograms were generated for ROIs surrounded by a red box to quantitatively compare (a, c) simulated and (b, d) in vivo angiography datasets. Scale bars correspond to 100 μm.

Equations (4)

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S ( k ) = G ( k ) n = 1 N R n e i ( 2 k l n )
G ( k ) exp ( k k 0 d k ) 2
R ( k ) = α G ( k )
I D ( k ) = | S ( k ) + R ( k ) | 2 | S ( k ) R ( k ) | 2

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