Abstract

Current clinical intravascular optical coherence tomography (IV-OCT) imaging systems have limited in-vivo flow imaging capability because of non-uniform catheter rotation and inadequate A-line scan density. Thus any flow-localisation method that seeks to identify sites of variation within the OCT image data-sets, whether that is in amplitude or phase, produces non-representative correlation (or variance) maps. In this study, both mean and the variation within a set of cross-correlation maps, for static OCT imaging was used to differentiate flow from nonflow regions. Variation was quantified by use of standard deviation. The advantage of this approach is its ability to image flow, even in the presence of motion artifacts. The ability of this technique to suppress noise and capture flow maps was demonstrated by imaging microflow in an ex-vivo porcine coronary artery model, by nailfold capillary imaging and in-vivo microvessel imaging from within the human coronary sinus.

© 2015 Optical Society of America

Full Article  |  PDF Article
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References

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    [Crossref]
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2014 (7)

H. M. Subhash and M. J. Leahy, “Microcirculation imaging based on full-range high-speed spectral domain correlation mapping optical coherence tomography,” J. Biomed. Opt. 19(2), 021103 (2014).
[Crossref]

W. J. Choi, R. Reif, S. Yousefi, and R. K. Wang, “Improved microcirculation imaging of human skin in vivo using optical microangiography with a correlation mapping mask,” J. Biomed. Opt. 19(3), 036010 (2014).
[Crossref]

S. Joseph, C. Rousseau, H. M. Subhash, M. J. Leahy, and D. Adlam, “Variation in cross-correlation as a discriminator for microvessel imaging using clinical intravascular optical coherence tomography systems,” Proc. SPIE 8934, 89342L (2014).
[Crossref]

A. Doronin, S. Botting, M. Meglinski, K. Jentoft, and I. Meglinski, “Mapping of spatial distribution of superficial blood vessels in human skin by double correlation analysis of optical coherence tomography images,” Proc. SPIE 8580, 858002 (2014).
[Crossref]

H. S. Cho, S-J. Jang, K. Kim, A. V. Dan-Chin-Yu, M. Shishkov, B. E. Bouma, and W-Y. Oh, “High frame-rate intravascular optical frequency-domain imaging in vivo,” Biomed. Opt. Express 5(1), 223–232 (2014).
[Crossref] [PubMed]

B. Vuong, A. M. D. Lee, T. W. H. Luk, C. Sun, S. Lam, P. Lane, and V. X. D. Yang, “High speed, wide velocity dynamic range Doppler optical coherence tomography (Part IV): split spectrum processing in rotary catheter probes,” Opt. Express 22(7), 7399–7415 (2014).
[Crossref] [PubMed]

N. D. Shemonski, S. G. Adie, Y. Z. Liu, F. A. South, P. S. Carney, and S. A. Boppart, “Stability in computed optical interferometric tomography (Part I): Stability requirements,” Opt. Express 22(16), 19183–19197 (2014).
[Crossref] [PubMed]

2013 (4)

2012 (3)

2011 (4)

Z. Y. Shen, M. Wang, Y. H. Ji, Y. H. He, X. S. Dai, P. Li, and H. Ma, “Transverse flow velocity quantification using optical coherence tomography with correlation,” Laser Phys. Lett. 8(4), 318–323 (2011).
[Crossref]

J. Enfield, E. Jonathan, and M. Leahy, “In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography (cmOCT),” Biomed. Opt. Express 2(5), 1184–1193 (2011).
[Crossref] [PubMed]

W. Kang, H. Wang, Z. Wang, M. W. Jenkins, G. A. Isenberg, A. Chak, and A. M. Rollins, “Motion artifacts associated with in vivo endoscopic OCT images of the esophagus,” Opt. Express 19(21), 20722–20735 (2011).
[Crossref] [PubMed]

H. C. Lowe, J. Narula, J. G. Fujimoto, and I. -K. Jang, “Intracoronary optical diagnostics current status, limitations, and potential,” JACC: Cardiovascular Interventions 4(12), 1257–1270 (2011).
[Crossref] [PubMed]

2010 (3)

M. J. Mulligan-Kehoe, “The vasa vasorum in diseased and nondiseased arteries,” Am. J. Physiol.: Heart Circ. Physiol. 298, H295–H305 (2010).

M. Vorpahl, M. Nakano, and R. Virmani, “Small black holes in optical frequency domain imaging matches intravascular neoangiogenesis formation in histology,” Eur. Heart J. 31, 1889 (2010).
[Crossref] [PubMed]

J. Y. Ha, M. Shishkov, M. Colice, W. Y. Oh, H. Yoo, L. Liu, G. J. Tearney, and B. E. Bouma, “Compensation of motion artifacts in catheter-based optical frequency domain imaging,” Opt. Express 18(11), 11418–11427 (2010).
[Crossref] [PubMed]

2009 (1)

H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: a comprehensive review clinical and research applications,” JACC Cardiovascular Interventions 2, 1035–1046 (2009).
[Crossref] [PubMed]

2008 (1)

G. van Soest, J. G. Bosch, and A. F. van der Steen, “Azimuthal registration of image sequences affected by nonuniform rotation distortion,” IEEE Trans. Inf. Technol. Biomed. 12(3), 348–355 (2008).
[Crossref] [PubMed]

Adie, S. G.

Adlam, D.

S. Joseph, C. Rousseau, H. M. Subhash, M. J. Leahy, and D. Adlam, “Variation in cross-correlation as a discriminator for microvessel imaging using clinical intravascular optical coherence tomography systems,” Proc. SPIE 8934, 89342L (2014).
[Crossref]

Beurskens, R.

Bezerra, H. G.

H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: a comprehensive review clinical and research applications,” JACC Cardiovascular Interventions 2, 1035–1046 (2009).
[Crossref] [PubMed]

Boppart, S. A.

Bosch, J. G.

G. van Soest, J. G. Bosch, and A. F. van der Steen, “Azimuthal registration of image sequences affected by nonuniform rotation distortion,” IEEE Trans. Inf. Technol. Biomed. 12(3), 348–355 (2008).
[Crossref] [PubMed]

Botting, S.

A. Doronin, S. Botting, M. Meglinski, K. Jentoft, and I. Meglinski, “Mapping of spatial distribution of superficial blood vessels in human skin by double correlation analysis of optical coherence tomography images,” Proc. SPIE 8580, 858002 (2014).
[Crossref]

Bouma, B. E.

Cable, A. E.

Carney, P. S.

Chak, A.

Cheng, K. H.

Cho, H. S.

Choi, W. J.

W. J. Choi, R. Reif, S. Yousefi, and R. K. Wang, “Improved microcirculation imaging of human skin in vivo using optical microangiography with a correlation mapping mask,” J. Biomed. Opt. 19(3), 036010 (2014).
[Crossref]

Colice, M.

Costa, M. A.

H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: a comprehensive review clinical and research applications,” JACC Cardiovascular Interventions 2, 1035–1046 (2009).
[Crossref] [PubMed]

Courtney, B.

Dai, X. S.

Z. Y. Shen, M. Wang, Y. H. Ji, Y. H. He, X. S. Dai, P. Li, and H. Ma, “Transverse flow velocity quantification using optical coherence tomography with correlation,” Laser Phys. Lett. 8(4), 318–323 (2011).
[Crossref]

Dan-Chin-Yu, A. V.

Daniels, J. M. A.

de Boer, J. F.

de Groot, M.

Doronin, A.

A. Doronin, S. Botting, M. Meglinski, K. Jentoft, and I. Meglinski, “Mapping of spatial distribution of superficial blood vessels in human skin by double correlation analysis of optical coherence tomography images,” Proc. SPIE 8580, 858002 (2014).
[Crossref]

Enfield, J.

Fujimoto, J. G.

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Pearson Prentice Hall, Upper Saddle River, N.J., 2008), pp. 742–747.

Grünberg, K.

Guagliumi, G.

H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: a comprehensive review clinical and research applications,” JACC Cardiovascular Interventions 2, 1035–1046 (2009).
[Crossref] [PubMed]

Ha, J. Y.

He, Y. H.

Z. Y. Shen, M. Wang, Y. H. Ji, Y. H. He, X. S. Dai, P. Li, and H. Ma, “Transverse flow velocity quantification using optical coherence tomography with correlation,” Laser Phys. Lett. 8(4), 318–323 (2011).
[Crossref]

Heim, P. J. S.

Helderman, F.

Herman, P. R.

Hornegger, J.

Huang, Y.

Huber, R.

Isenberg, G. A.

Jang, I. -K.

H. C. Lowe, J. Narula, J. G. Fujimoto, and I. -K. Jang, “Intracoronary optical diagnostics current status, limitations, and potential,” JACC: Cardiovascular Interventions 4(12), 1257–1270 (2011).
[Crossref] [PubMed]

Jang, S-J.

Jayaraman, V.

Jenkins, M. W.

Jentoft, K.

A. Doronin, S. Botting, M. Meglinski, K. Jentoft, and I. Meglinski, “Mapping of spatial distribution of superficial blood vessels in human skin by double correlation analysis of optical coherence tomography images,” Proc. SPIE 8580, 858002 (2014).
[Crossref]

Ji, Y. H.

Z. Y. Shen, M. Wang, Y. H. Ji, Y. H. He, X. S. Dai, P. Li, and H. Ma, “Transverse flow velocity quantification using optical coherence tomography with correlation,” Laser Phys. Lett. 8(4), 318–323 (2011).
[Crossref]

Jiang, J.

Jonathan, E.

Joseph, S.

S. Joseph, C. Rousseau, H. M. Subhash, M. J. Leahy, and D. Adlam, “Variation in cross-correlation as a discriminator for microvessel imaging using clinical intravascular optical coherence tomography systems,” Proc. SPIE 8934, 89342L (2014).
[Crossref]

Kang, J. U.

Kang, W.

Kiehl, T.-R.

Kim, K.

Kraus, M. F.

Lam, S.

B. Vuong, A. M. D. Lee, T. W. H. Luk, C. Sun, S. Lam, P. Lane, and V. X. D. Yang, “High speed, wide velocity dynamic range Doppler optical coherence tomography (Part IV): split spectrum processing in rotary catheter probes,” Opt. Express 22(7), 7399–7415 (2014).
[Crossref] [PubMed]

A. M. Lee, K. Ohtani, C. MacAulay, A. McWilliams, T. Shaipanich, V. X. Yang, S. Lam, and P. Lane, “In vivo lung microvasculature visualized in three dimensions using fiber-optic color Doppler optical coherence tomography”, J. Biomed. Opt. 18(5), 050501 (2013).
[Crossref]

Lancee, C. T.

Lane, P.

B. Vuong, A. M. D. Lee, T. W. H. Luk, C. Sun, S. Lam, P. Lane, and V. X. D. Yang, “High speed, wide velocity dynamic range Doppler optical coherence tomography (Part IV): split spectrum processing in rotary catheter probes,” Opt. Express 22(7), 7399–7415 (2014).
[Crossref] [PubMed]

A. M. Lee, K. Ohtani, C. MacAulay, A. McWilliams, T. Shaipanich, V. X. Yang, S. Lam, and P. Lane, “In vivo lung microvasculature visualized in three dimensions using fiber-optic color Doppler optical coherence tomography”, J. Biomed. Opt. 18(5), 050501 (2013).
[Crossref]

Leahy, M.

Leahy, M. J.

H. M. Subhash and M. J. Leahy, “Microcirculation imaging based on full-range high-speed spectral domain correlation mapping optical coherence tomography,” J. Biomed. Opt. 19(2), 021103 (2014).
[Crossref]

S. Joseph, C. Rousseau, H. M. Subhash, M. J. Leahy, and D. Adlam, “Variation in cross-correlation as a discriminator for microvessel imaging using clinical intravascular optical coherence tomography systems,” Proc. SPIE 8934, 89342L (2014).
[Crossref]

Lee, A. M.

A. M. Lee, K. Ohtani, C. MacAulay, A. McWilliams, T. Shaipanich, V. X. Yang, S. Lam, and P. Lane, “In vivo lung microvasculature visualized in three dimensions using fiber-optic color Doppler optical coherence tomography”, J. Biomed. Opt. 18(5), 050501 (2013).
[Crossref]

Lee, A. M. D.

Lee, K. K.

Li, J.

Li, P.

Z. Y. Shen, M. Wang, Y. H. Ji, Y. H. He, X. S. Dai, P. Li, and H. Ma, “Transverse flow velocity quantification using optical coherence tomography with correlation,” Laser Phys. Lett. 8(4), 318–323 (2011).
[Crossref]

Liu, L.

Liu, X.

Liu, Y. Z.

Lowe, H. C.

H. C. Lowe, J. Narula, J. G. Fujimoto, and I. -K. Jang, “Intracoronary optical diagnostics current status, limitations, and potential,” JACC: Cardiovascular Interventions 4(12), 1257–1270 (2011).
[Crossref] [PubMed]

Luk, T. W. H.

Ma, H.

Z. Y. Shen, M. Wang, Y. H. Ji, Y. H. He, X. S. Dai, P. Li, and H. Ma, “Transverse flow velocity quantification using optical coherence tomography with correlation,” Laser Phys. Lett. 8(4), 318–323 (2011).
[Crossref]

MacAulay, C.

A. M. Lee, K. Ohtani, C. MacAulay, A. McWilliams, T. Shaipanich, V. X. Yang, S. Lam, and P. Lane, “In vivo lung microvasculature visualized in three dimensions using fiber-optic color Doppler optical coherence tomography”, J. Biomed. Opt. 18(5), 050501 (2013).
[Crossref]

Mariampillai, A.

Marotta, T. R.

Mashimo, H.

Mathews, S. A.

McWilliams, A.

A. M. Lee, K. Ohtani, C. MacAulay, A. McWilliams, T. Shaipanich, V. X. Yang, S. Lam, and P. Lane, “In vivo lung microvasculature visualized in three dimensions using fiber-optic color Doppler optical coherence tomography”, J. Biomed. Opt. 18(5), 050501 (2013).
[Crossref]

Meglinski, I.

A. Doronin, S. Botting, M. Meglinski, K. Jentoft, and I. Meglinski, “Mapping of spatial distribution of superficial blood vessels in human skin by double correlation analysis of optical coherence tomography images,” Proc. SPIE 8580, 858002 (2014).
[Crossref]

Meglinski, M.

A. Doronin, S. Botting, M. Meglinski, K. Jentoft, and I. Meglinski, “Mapping of spatial distribution of superficial blood vessels in human skin by double correlation analysis of optical coherence tomography images,” Proc. SPIE 8580, 858002 (2014).
[Crossref]

Mo, J.

Montanera, W. J.

Mulligan-Kehoe, M. J.

M. J. Mulligan-Kehoe, “The vasa vasorum in diseased and nondiseased arteries,” Am. J. Physiol.: Heart Circ. Physiol. 298, H295–H305 (2010).

Nakano, M.

M. Vorpahl, M. Nakano, and R. Virmani, “Small black holes in optical frequency domain imaging matches intravascular neoangiogenesis formation in histology,” Eur. Heart J. 31, 1889 (2010).
[Crossref] [PubMed]

Narula, J.

H. C. Lowe, J. Narula, J. G. Fujimoto, and I. -K. Jang, “Intracoronary optical diagnostics current status, limitations, and potential,” JACC: Cardiovascular Interventions 4(12), 1257–1270 (2011).
[Crossref] [PubMed]

Nolte, F.

Oh, W. Y.

Oh, W-Y.

Ohtani, K.

A. M. Lee, K. Ohtani, C. MacAulay, A. McWilliams, T. Shaipanich, V. X. Yang, S. Lam, and P. Lane, “In vivo lung microvasculature visualized in three dimensions using fiber-optic color Doppler optical coherence tomography”, J. Biomed. Opt. 18(5), 050501 (2013).
[Crossref]

Pfeiffer, T.

Potsaid, B.

Ramella-Roman, J. C.

Reif, R.

W. J. Choi, R. Reif, S. Yousefi, and R. K. Wang, “Improved microcirculation imaging of human skin in vivo using optical microangiography with a correlation mapping mask,” J. Biomed. Opt. 19(3), 036010 (2014).
[Crossref]

Rollins, A. M.

W. Kang, H. Wang, Z. Wang, M. W. Jenkins, G. A. Isenberg, A. Chak, and A. M. Rollins, “Motion artifacts associated with in vivo endoscopic OCT images of the esophagus,” Opt. Express 19(21), 20722–20735 (2011).
[Crossref] [PubMed]

H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: a comprehensive review clinical and research applications,” JACC Cardiovascular Interventions 2, 1035–1046 (2009).
[Crossref] [PubMed]

Rousseau, C.

S. Joseph, C. Rousseau, H. M. Subhash, M. J. Leahy, and D. Adlam, “Variation in cross-correlation as a discriminator for microvessel imaging using clinical intravascular optical coherence tomography systems,” Proc. SPIE 8934, 89342L (2014).
[Crossref]

Shaipanich, T.

A. M. Lee, K. Ohtani, C. MacAulay, A. McWilliams, T. Shaipanich, V. X. Yang, S. Lam, and P. Lane, “In vivo lung microvasculature visualized in three dimensions using fiber-optic color Doppler optical coherence tomography”, J. Biomed. Opt. 18(5), 050501 (2013).
[Crossref]

Shemonski, N. D.

Shen, Z. Y.

Z. Y. Shen, M. Wang, Y. H. Ji, Y. H. He, X. S. Dai, P. Li, and H. Ma, “Transverse flow velocity quantification using optical coherence tomography with correlation,” Laser Phys. Lett. 8(4), 318–323 (2011).
[Crossref]

Shishkov, M.

Simon, D. I.

H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: a comprehensive review clinical and research applications,” JACC Cardiovascular Interventions 2, 1035–1046 (2009).
[Crossref] [PubMed]

South, F. A.

Spears, J.

Springeling, G.

Standish, B. A.

Subhash, H. M.

H. M. Subhash and M. J. Leahy, “Microcirculation imaging based on full-range high-speed spectral domain correlation mapping optical coherence tomography,” J. Biomed. Opt. 19(2), 021103 (2014).
[Crossref]

S. Joseph, C. Rousseau, H. M. Subhash, M. J. Leahy, and D. Adlam, “Variation in cross-correlation as a discriminator for microvessel imaging using clinical intravascular optical coherence tomography systems,” Proc. SPIE 8934, 89342L (2014).
[Crossref]

Sun, C.

Sutedja, T. G.

Tao, Y. K.

Tearney, G. J.

Tsai, T-H.

van der Steen, A. F.

G. van Soest, J. G. Bosch, and A. F. van der Steen, “Azimuthal registration of image sequences affected by nonuniform rotation distortion,” IEEE Trans. Inf. Technol. Biomed. 12(3), 348–355 (2008).
[Crossref] [PubMed]

van der Steen, A. F. W.

van Soest, G.

T. Wang, W. Wieser, G. Springeling, R. Beurskens, C. T. Lancee, T. Pfeiffer, A. F. W. van der Steen, R. Huber, and G. van Soest, “Intravascular optical coherence tomography imaging at 3200 frames per second”, Opt. Lett. 38(10), 1715–1717 (2013).
[Crossref] [PubMed]

G. van Soest, J. G. Bosch, and A. F. van der Steen, “Azimuthal registration of image sequences affected by nonuniform rotation distortion,” IEEE Trans. Inf. Technol. Biomed. 12(3), 348–355 (2008).
[Crossref] [PubMed]

Virmani, R.

M. Vorpahl, M. Nakano, and R. Virmani, “Small black holes in optical frequency domain imaging matches intravascular neoangiogenesis formation in histology,” Eur. Heart J. 31, 1889 (2010).
[Crossref] [PubMed]

Vorpahl, M.

M. Vorpahl, M. Nakano, and R. Virmani, “Small black holes in optical frequency domain imaging matches intravascular neoangiogenesis formation in histology,” Eur. Heart J. 31, 1889 (2010).
[Crossref] [PubMed]

Vuong, B.

Wang, H.

Wang, M.

Z. Y. Shen, M. Wang, Y. H. Ji, Y. H. He, X. S. Dai, P. Li, and H. Ma, “Transverse flow velocity quantification using optical coherence tomography with correlation,” Laser Phys. Lett. 8(4), 318–323 (2011).
[Crossref]

Wang, R. K.

W. J. Choi, R. Reif, S. Yousefi, and R. K. Wang, “Improved microcirculation imaging of human skin in vivo using optical microangiography with a correlation mapping mask,” J. Biomed. Opt. 19(3), 036010 (2014).
[Crossref]

Wang, T.

Wang, Z.

Wieser, W.

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Pearson Prentice Hall, Upper Saddle River, N.J., 2008), pp. 742–747.

Yang, V. X.

A. M. Lee, K. Ohtani, C. MacAulay, A. McWilliams, T. Shaipanich, V. X. Yang, S. Lam, and P. Lane, “In vivo lung microvasculature visualized in three dimensions using fiber-optic color Doppler optical coherence tomography”, J. Biomed. Opt. 18(5), 050501 (2013).
[Crossref]

C. Sun, F. Nolte, K. H. Cheng, B. Vuong, K. K. Lee, B. A. Standish, B. Courtney, T. R. Marotta, A. Mariampillai, and V. X. Yang, “In vivo feasibility of endovascular Doppler optical coherence tomography”, Biomed. Opt. Express 3(10), 2600–2610 (2012).
[Crossref] [PubMed]

Yang, V. X. D.

Yoo, H.

Yousefi, S.

W. J. Choi, R. Reif, S. Yousefi, and R. K. Wang, “Improved microcirculation imaging of human skin in vivo using optical microangiography with a correlation mapping mask,” J. Biomed. Opt. 19(3), 036010 (2014).
[Crossref]

Zhou, C.

Am. J. Physiol.: Heart Circ. Physiol. (1)

M. J. Mulligan-Kehoe, “The vasa vasorum in diseased and nondiseased arteries,” Am. J. Physiol.: Heart Circ. Physiol. 298, H295–H305 (2010).

Biomed. Opt. Express (5)

Eur. Heart J. (1)

M. Vorpahl, M. Nakano, and R. Virmani, “Small black holes in optical frequency domain imaging matches intravascular neoangiogenesis formation in histology,” Eur. Heart J. 31, 1889 (2010).
[Crossref] [PubMed]

IEEE Trans. Inf. Technol. Biomed. (1)

G. van Soest, J. G. Bosch, and A. F. van der Steen, “Azimuthal registration of image sequences affected by nonuniform rotation distortion,” IEEE Trans. Inf. Technol. Biomed. 12(3), 348–355 (2008).
[Crossref] [PubMed]

J. Biomed. Opt. (3)

A. M. Lee, K. Ohtani, C. MacAulay, A. McWilliams, T. Shaipanich, V. X. Yang, S. Lam, and P. Lane, “In vivo lung microvasculature visualized in three dimensions using fiber-optic color Doppler optical coherence tomography”, J. Biomed. Opt. 18(5), 050501 (2013).
[Crossref]

H. M. Subhash and M. J. Leahy, “Microcirculation imaging based on full-range high-speed spectral domain correlation mapping optical coherence tomography,” J. Biomed. Opt. 19(2), 021103 (2014).
[Crossref]

W. J. Choi, R. Reif, S. Yousefi, and R. K. Wang, “Improved microcirculation imaging of human skin in vivo using optical microangiography with a correlation mapping mask,” J. Biomed. Opt. 19(3), 036010 (2014).
[Crossref]

JACC Cardiovascular Interventions (1)

H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: a comprehensive review clinical and research applications,” JACC Cardiovascular Interventions 2, 1035–1046 (2009).
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JACC: Cardiovascular Interventions (1)

H. C. Lowe, J. Narula, J. G. Fujimoto, and I. -K. Jang, “Intracoronary optical diagnostics current status, limitations, and potential,” JACC: Cardiovascular Interventions 4(12), 1257–1270 (2011).
[Crossref] [PubMed]

Laser Phys. Lett. (1)

Z. Y. Shen, M. Wang, Y. H. Ji, Y. H. He, X. S. Dai, P. Li, and H. Ma, “Transverse flow velocity quantification using optical coherence tomography with correlation,” Laser Phys. Lett. 8(4), 318–323 (2011).
[Crossref]

Opt. Express (5)

Opt. Lett. (2)

Proc. SPIE (2)

S. Joseph, C. Rousseau, H. M. Subhash, M. J. Leahy, and D. Adlam, “Variation in cross-correlation as a discriminator for microvessel imaging using clinical intravascular optical coherence tomography systems,” Proc. SPIE 8934, 89342L (2014).
[Crossref]

A. Doronin, S. Botting, M. Meglinski, K. Jentoft, and I. Meglinski, “Mapping of spatial distribution of superficial blood vessels in human skin by double correlation analysis of optical coherence tomography images,” Proc. SPIE 8580, 858002 (2014).
[Crossref]

Other (1)

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Pearson Prentice Hall, Upper Saddle River, N.J., 2008), pp. 742–747.

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Figures (12)

Fig. 1
Fig. 1

Comparison of application of the CC mapping method to a zero pullback dataset of SS-OCT (top row) and IV-OCT systems (bottom row). SS-OCT results are a template for IV-OCT data. (a) and (d–e) Structural OCT images from the respective systems. (b) and (f) CC map for (a) and (e), respectively. The colormap in both cases indicates coefficient values. (c) and (g) variation in the CC values across the 30 CC stack for SS-OCT and IVOCT systems. OCT images obtained from IV-OCT systems are in polar form (d), while raw-format image (e) was used for CC mapping. The markings boe-6-3-668-i001 and boe-6-3-668-i002 in (a and e) are respectively the non-flow and flow regions, selected for CC variation analysis shown in (c and g). The dotted green circle in (a and d–e) highlights the flow regions. While flow and non-flow regions are distinguishable in (b), a broad low CC map is obtained for (f). This is due to the rotational artefact of the IV-OCT system. The distortion also introduces correlation variation which primarily affects the non-flow region CC values as shown in (g). Scale bar in (a) and (d) represents 1 mm.

Fig. 2
Fig. 2

Histogram plot showing the distribution of the SD values at selected flow and nonflow regions (marked in Fig. 1(a) and 1(e)) in the SD map, obtained from the 30 CC map stack. (a–b) flow regions and (c–d) non-flow regions in the SS-OCT and IV-OCT dataset. While (a) and (b) have similar profile, the non-flow region histogram profile in (d) resembles more a flow SD profile than to (c). From (b) and (d) a optimum threshold value for SD (=0.22) can be obtained. Due to the overlapping of the SD histogram plots in (b and d), at this threshold value, some of the non-flow regions can be mislabelled as flow regions.

Fig. 3
Fig. 3

Histogram plot showing the distribution of the mean values at selected flow and non-flow regions (marked in Fig. 1(a) and 1(e)) in the mean map, obtained from the 30 CC map stack. (a–b) flow regions and (c–d) non-flow regions in the SS-OCT and IV-OCT dataset. Though (a) and (b) have similar profile, non-flow regions shown in (d) have lower mean value distribution compared to (c). This drop in mean value is a result of the random fluctuation of the CC values in the non-flow regions of the IV-OCT phantom dataset. Comparing (b and d) with (a and c) shows that, for IV-OCT dataset a threshold value of mean = 0.12 can be used to discriminate non-flow regions.

Fig. 4
Fig. 4

Application of Mean and SD Mask to CC map at Fig. 1(f). (a) after applying the Mean Mask. (b) after SD Mask is applied on to the resultant from (a). (c) after binarization and image filtering, where pixel areas smaller than the (8 × 4) kernel size were removed. (d) after masking out regions beyond 2.5 mm depth. Green arrow indicates background regions that were falsely identified as flow regions. Images have been enlarged with respect to Fig. 1(f), to highlight the noise effects.

Fig. 5
Fig. 5

Flow map region, obtained with Mean+SD Masking method is superimposed on to the OCT image (see, Fig. 1(b))

Fig. 6
Fig. 6

Comparing the effectiveness of the Mean and SD Masks using the CC map in Fig. 1(f). (a and b) Binary version of Fig. 4(a) and 4(b), respectively. Regions below 2.5 mm were masked out, as there was no quantitative information available. (c) shows the differences between (a and b). (d) CC fluctuation for certain non-flow regions. Most non-flow regions are suppressed with Mean Mask, however some pixels still appear as flow regions. From (d) these pixels (represented by boe-6-3-668-i004 Non-Flow2) have low mean value and therefore escape the Mean Mask. Roughly, 13% improvement was obtained when SD Mask was used along with Mean Mask.

Fig. 7
Fig. 7

Effect of frame number on generating authentic mean and SD maps to reduce flow/non-flow misidentification. (a) Percentage number of pixels in each selected region lost due to the Mean Mask. As the fidelity of the Mean Mask improves with the number of CC maps, the loss is reduced. (b). Likewise, the effect of fidelity of the SD Mask on the flow map. The data presented here are averaged over 20 phantom experiments and the errorbar shows the variability across these experiments.

Fig. 8
Fig. 8

Analysis of every-frame CC mapping approach and determination of threshold value for corresponding mean and SD maps. (a–b) respectively shows histogram plot of mean and SD values of the selected flow and non-flow regions, obtained from CC maps for �� = 31 images. There is minimal overlap between flow and non-flow distribution in (a). A mean threshold value 0.06 – 0.08 can be determined. Likewise, from (b) an SD threshold value of 0.19 was obtained. Though there is an overlap between flow and non-flow histogram profiles it is considerably less than in Fig. 2(b and d). (c–d) exhibits the effect of frame numbers on respective mean and SD maps and improvement in reduction of flow/non-flow identification ambiguity. The data presented here are averaged over 5 phantom experiments and the errorbar shows the variability across these experiments.

Fig. 9
Fig. 9

Dependence of flow intensity on the flow mapping method. (a) Cross-sectional OCT image of the flow phantom, with ( boe-6-3-668-i006) indicates the 900 μm capillary tube. The flow area is filled with 0.05% intralipid solution, with brownian flow. (b) Polar form raw-format image of (a) and boe-6-3-668-i005 highlights an A-line. (c) Intensity profile along the highlighted A-line for 0.05% and 1% intralipid concentration. (inset) shows the intensity profile at the flow region, which is marked by boe-6-3-668-i007. The profile shows how the intralipid concentration affects the flow intensity. The intensity for 1% conc. is clearly above the background intensity range, whereas for 0.05% conc. the flow intensity is very close to the background intensity. Intensity threshold values of (d) 0.18, (e) 0.2 and (f) 0.22 were used to generate respective Intensity Masks and applied to CC maps. From (c), at 0.18 intensity threshold limit most background regions are visible in the flow map as shown in (d). As the threshold limit is increased to 0.2 and 0.22, the presence of background is also reduced, which is respectively shown in (e) and (f). Increase in the threshold value also masks out lower intensity flow regions, as in (f).

Fig. 10
Fig. 10

(a) Cross-sectional image of porcine LAD coronary artery. ( boe-6-3-668-i008) indicates the 100 μm capillary tube. (b) Superimposing flow map on to (a).

Fig. 11
Fig. 11

(a) Cross-sectional OCT image of the nail-fold capillary. EP: epidermis, D: dermis, NM: nail matrix, and NR: nail root. (b) Microvascular flow map is overlaid onto (a). The flow regions are marked red.

Fig. 12
Fig. 12

In-vivo microvessel imaging through the Human Coronary Sinus using the every-frame CC mapping method. (a) CC coefficient variation across 21 CC maps obtained from 7 IV-OCT images. boe-6-3-668-i003Non-Flow, boe-6-3-668-i009Microvessel flow, boe-6-3-668-i010Venous blood flow. (b – c) Respectively, are the histogram of mean and SD values for flow and non-flow regions. (d and f) Cross-sectional OCT images obtained with zero pullback. Bold red arrows indicate the vessels. (e and g) Flow maps corresponding to (d) and (f) superimposed onto the respective OCT images. Flow regions are marked red.

Equations (2)

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X = m n ( I m n ( 1 ) I m n ( 1 ) ¯ ) ( I m n ( 2 ) I m n ( 2 ) ¯ ) ( m n ( I m n ( 1 ) I m n ( 1 ) ¯ ) 2 ) ( m n ( I m n ( 2 ) I m n ( 2 ) ¯ ) 2 )
s ( x , y ) = [ 1 ( 𝒩 1 ) i = 1 𝒩 1 ( X ( x , y , i ) X ( x , y ) ¯ ) 2 ] 1 / 2

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