Abstract

Doppler optical coherence tomography (OCT) is widely used for high-resolution mapping of flow velocities and is based on analysis of temporal changes in the phase of an OCT signal (i.e., how fast the OCT signal rotates in the complex plane). Determination of the rate of phase change or rotation speed critically depends on the center of rotation. Here, we demonstrate the bias in high-pass filtering, the current widely used method to determine the center of rotation, and propose two advanced methods for Doppler OCT clutter rejection. The bias in the high-pass filtering method becomes increasingly significant with lower velocities or larger signal noise. Two novel methods based on variance minimization and offset extrapolation can potentially reduce this bias and thereby improve the accuracy of Doppler OCT measurements of flow velocities, even for low-velocity and/or high-noise signals. The two novel methods and the current standard method (high-pass filtering) have been tested in combination with several currently used velocity measurement algorithms: Kasai, autocorrelation function fitting, and maximum likelihood estimation. The newly proposed methods are shown to improve the accuracy in both the center of rotation and resultant velocity by up to 60 percentage points and reduce the flow conservation error by 30% when applied to in vivo cerebral blood flow imaging of the rodent brain cortex.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2014 (1)

2013 (2)

J. Lee, H. Radhakrishnan, W. Wu, A. Daneshmand, M. Climov, C. Ayata, and D. A. Boas, “Quantitative imaging of cerebral blood flow velocity and intracellular motility using dynamic light scattering-optical coherence tomography,” J. Cereb. Blood Flow Metab. 33(6), 819–825 (2013).
[Crossref] [PubMed]

A. C. Chan, E. Y. Lam, and V. J. Srinivasan, “Comparison of Kasai autocorrelation and maximum likelihood estimators for Doppler optical coherence tomography,” IEEE Trans. Med. Imaging 36, 1033 (2013).

2012 (2)

2011 (1)

V. J. Srinivasan, D. N. Atochin, H. Radhakrishnan, J. Y. Jiang, S. Ruvinskaya, W. Wu, S. Barry, A. E. Cable, C. Ayata, P. L. Huang, and D. A. Boas, “Optical coherence tomography for the quantitative study of cerebrovascular physiology,” J. Cereb. Blood Flow Metab. 31(6), 1339–1345 (2011).
[Crossref] [PubMed]

2010 (1)

2009 (1)

2008 (2)

2006 (2)

2003 (1)

G. Cloutier, D. Chen, and L.-G. Durand, “A new clutter rejection algorithm for Doppler ultrasound,” IEEE Trans. Med. Imaging 22(4), 530–538 (2003).
[Crossref] [PubMed]

2002 (1)

S. Bjaerum, H. Torp, and K. Kristoffersen, “Clutter filter design for ultrasound color flow imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 49(2), 204–216 (2002).
[Crossref] [PubMed]

2000 (3)

1997 (1)

H. Torp, “Clutter rejection filters in color flow imaging: A theoretical approach,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 44(2), 417–424 (1997).
[Crossref] [PubMed]

1996 (2)

A. Herment, G. Demoment, and P. Dumée, “Improved estimation of low velocities in color Doppler imaging by adapting the mean frequency estimator to the clutter rejection filter,” IEEE Trans. Biomed. Eng. 43(9), 919–927 (1996).
[Crossref] [PubMed]

A. F. Fercher, “Optical coherence tomography,” J. Biomed. Opt. 1(2), 157–173 (1996).
[Crossref] [PubMed]

1995 (1)

A. P. Kadi and T. Loupas, “On the performance of regression and step-initialized IIR clutter filters for color Doppler systems in diagnostic medical ultrasound,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 42(5), 927–937 (1995).
[Crossref]

1991 (2)

Y. B. Ahn and S. B. Park, “Estimation of mean frequency and variance of ultrasonic Doppler signal by using second-order autoregressive model,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 38(3), 172–182 (1991).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

1985 (1)

C. Kasai, K. Namekawa, A. Koyano, and R. Omoto, “Real-Time Two-Dimensional Blood Flow Imaging Using an Autocorrelation Technique,” IEEE Trans. Sonics Ultrason. 32(3), 458–464 (1985).
[Crossref]

Ahn, Y. B.

Y. B. Ahn and S. B. Park, “Estimation of mean frequency and variance of ultrasonic Doppler signal by using second-order autoregressive model,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 38(3), 172–182 (1991).
[Crossref] [PubMed]

Atochin, D. N.

V. J. Srinivasan, D. N. Atochin, H. Radhakrishnan, J. Y. Jiang, S. Ruvinskaya, W. Wu, S. Barry, A. E. Cable, C. Ayata, P. L. Huang, and D. A. Boas, “Optical coherence tomography for the quantitative study of cerebrovascular physiology,” J. Cereb. Blood Flow Metab. 31(6), 1339–1345 (2011).
[Crossref] [PubMed]

Ayata, C.

J. Lee, H. Radhakrishnan, W. Wu, A. Daneshmand, M. Climov, C. Ayata, and D. A. Boas, “Quantitative imaging of cerebral blood flow velocity and intracellular motility using dynamic light scattering-optical coherence tomography,” J. Cereb. Blood Flow Metab. 33(6), 819–825 (2013).
[Crossref] [PubMed]

V. J. Srinivasan, D. N. Atochin, H. Radhakrishnan, J. Y. Jiang, S. Ruvinskaya, W. Wu, S. Barry, A. E. Cable, C. Ayata, P. L. Huang, and D. A. Boas, “Optical coherence tomography for the quantitative study of cerebrovascular physiology,” J. Cereb. Blood Flow Metab. 31(6), 1339–1345 (2011).
[Crossref] [PubMed]

Bajraszewski, T.

Barry, S.

V. J. Srinivasan, D. N. Atochin, H. Radhakrishnan, J. Y. Jiang, S. Ruvinskaya, W. Wu, S. Barry, A. E. Cable, C. Ayata, P. L. Huang, and D. A. Boas, “Optical coherence tomography for the quantitative study of cerebrovascular physiology,” J. Cereb. Blood Flow Metab. 31(6), 1339–1345 (2011).
[Crossref] [PubMed]

Bjaerum, S.

S. Bjaerum, H. Torp, and K. Kristoffersen, “Clutter filter design for ultrasound color flow imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 49(2), 204–216 (2002).
[Crossref] [PubMed]

Boas, D. A.

J. Lee, H. Radhakrishnan, W. Wu, A. Daneshmand, M. Climov, C. Ayata, and D. A. Boas, “Quantitative imaging of cerebral blood flow velocity and intracellular motility using dynamic light scattering-optical coherence tomography,” J. Cereb. Blood Flow Metab. 33(6), 819–825 (2013).
[Crossref] [PubMed]

J. Lee, W. Wu, J. Y. Jiang, B. Zhu, and D. A. Boas, “Dynamic light scattering optical coherence tomography,” Opt. Express 20(20), 22262–22277 (2012).
[Crossref] [PubMed]

V. J. Srinivasan, D. N. Atochin, H. Radhakrishnan, J. Y. Jiang, S. Ruvinskaya, W. Wu, S. Barry, A. E. Cable, C. Ayata, P. L. Huang, and D. A. Boas, “Optical coherence tomography for the quantitative study of cerebrovascular physiology,” J. Cereb. Blood Flow Metab. 31(6), 1339–1345 (2011).
[Crossref] [PubMed]

V. J. Srinivasan, S. Sakadzić, I. Gorczynska, S. Ruvinskaya, W. Wu, J. G. Fujimoto, and D. A. Boas, “Quantitative cerebral blood flow with optical coherence tomography,” Opt. Express 18(3), 2477–2494 (2010).
[Crossref] [PubMed]

Cable, A. E.

V. J. Srinivasan, D. N. Atochin, H. Radhakrishnan, J. Y. Jiang, S. Ruvinskaya, W. Wu, S. Barry, A. E. Cable, C. Ayata, P. L. Huang, and D. A. Boas, “Optical coherence tomography for the quantitative study of cerebrovascular physiology,” J. Cereb. Blood Flow Metab. 31(6), 1339–1345 (2011).
[Crossref] [PubMed]

Chan, A. C.

A. C. Chan, E. Y. Lam, and V. J. Srinivasan, “Comparison of Kasai autocorrelation and maximum likelihood estimators for Doppler optical coherence tomography,” IEEE Trans. Med. Imaging 36, 1033 (2013).

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Chen, D.

G. Cloutier, D. Chen, and L.-G. Durand, “A new clutter rejection algorithm for Doppler ultrasound,” IEEE Trans. Med. Imaging 22(4), 530–538 (2003).
[Crossref] [PubMed]

Chen, Z.

Climov, M.

J. Lee, H. Radhakrishnan, W. Wu, A. Daneshmand, M. Climov, C. Ayata, and D. A. Boas, “Quantitative imaging of cerebral blood flow velocity and intracellular motility using dynamic light scattering-optical coherence tomography,” J. Cereb. Blood Flow Metab. 33(6), 819–825 (2013).
[Crossref] [PubMed]

Cloutier, G.

G. Cloutier, D. Chen, and L.-G. Durand, “A new clutter rejection algorithm for Doppler ultrasound,” IEEE Trans. Med. Imaging 22(4), 530–538 (2003).
[Crossref] [PubMed]

Cobb, M. J.

Corti, L.

G. Guidi, L. Corti, and P. Tortoli, “Application of autoregressive methods to multigate spectral analysis,” Ultrasound Med. Biol. 26(4), 585–592 (2000).
[Crossref] [PubMed]

Daneshmand, A.

J. Lee, H. Radhakrishnan, W. Wu, A. Daneshmand, M. Climov, C. Ayata, and D. A. Boas, “Quantitative imaging of cerebral blood flow velocity and intracellular motility using dynamic light scattering-optical coherence tomography,” J. Cereb. Blood Flow Metab. 33(6), 819–825 (2013).
[Crossref] [PubMed]

Davis, A. M.

de Boer, J. F.

Demoment, G.

A. Herment, G. Demoment, and P. Dumée, “Improved estimation of low velocities in color Doppler imaging by adapting the mean frequency estimator to the clutter rejection filter,” IEEE Trans. Biomed. Eng. 43(9), 919–927 (1996).
[Crossref] [PubMed]

Du, C.

Dumée, P.

A. Herment, G. Demoment, and P. Dumée, “Improved estimation of low velocities in color Doppler imaging by adapting the mean frequency estimator to the clutter rejection filter,” IEEE Trans. Biomed. Eng. 43(9), 919–927 (1996).
[Crossref] [PubMed]

Durand, L.-G.

G. Cloutier, D. Chen, and L.-G. Durand, “A new clutter rejection algorithm for Doppler ultrasound,” IEEE Trans. Med. Imaging 22(4), 530–538 (2003).
[Crossref] [PubMed]

Fercher, A. F.

A. F. Fercher, “Optical coherence tomography,” J. Biomed. Opt. 1(2), 157–173 (1996).
[Crossref] [PubMed]

Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Fujimoto, J. G.

V. J. Srinivasan, S. Sakadzić, I. Gorczynska, S. Ruvinskaya, W. Wu, J. G. Fujimoto, and D. A. Boas, “Quantitative cerebral blood flow with optical coherence tomography,” Opt. Express 18(3), 2477–2494 (2010).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Gorczynska, I.

Gregory, K.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Guidi, G.

G. Guidi, L. Corti, and P. Tortoli, “Application of autoregressive methods to multigate spectral analysis,” Ultrasound Med. Biol. 26(4), 585–592 (2000).
[Crossref] [PubMed]

Hee, M. R.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Herment, A.

A. Herment, G. Demoment, and P. Dumée, “Improved estimation of low velocities in color Doppler imaging by adapting the mean frequency estimator to the clutter rejection filter,” IEEE Trans. Biomed. Eng. 43(9), 919–927 (1996).
[Crossref] [PubMed]

Huang, D.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Huang, P. L.

V. J. Srinivasan, D. N. Atochin, H. Radhakrishnan, J. Y. Jiang, S. Ruvinskaya, W. Wu, S. Barry, A. E. Cable, C. Ayata, P. L. Huang, and D. A. Boas, “Optical coherence tomography for the quantitative study of cerebrovascular physiology,” J. Cereb. Blood Flow Metab. 31(6), 1339–1345 (2011).
[Crossref] [PubMed]

Izatt, J. A.

Jiang, J. Y.

J. Lee, W. Wu, J. Y. Jiang, B. Zhu, and D. A. Boas, “Dynamic light scattering optical coherence tomography,” Opt. Express 20(20), 22262–22277 (2012).
[Crossref] [PubMed]

V. J. Srinivasan, D. N. Atochin, H. Radhakrishnan, J. Y. Jiang, S. Ruvinskaya, W. Wu, S. Barry, A. E. Cable, C. Ayata, P. L. Huang, and D. A. Boas, “Optical coherence tomography for the quantitative study of cerebrovascular physiology,” J. Cereb. Blood Flow Metab. 31(6), 1339–1345 (2011).
[Crossref] [PubMed]

Kadi, A. P.

A. P. Kadi and T. Loupas, “On the performance of regression and step-initialized IIR clutter filters for color Doppler systems in diagnostic medical ultrasound,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 42(5), 927–937 (1995).
[Crossref]

Kasai, C.

C. Kasai, K. Namekawa, A. Koyano, and R. Omoto, “Real-Time Two-Dimensional Blood Flow Imaging Using an Autocorrelation Technique,” IEEE Trans. Sonics Ultrason. 32(3), 458–464 (1985).
[Crossref]

Kolbitsch, C.

Kowalczyk, A.

Koyano, A.

C. Kasai, K. Namekawa, A. Koyano, and R. Omoto, “Real-Time Two-Dimensional Blood Flow Imaging Using an Autocorrelation Technique,” IEEE Trans. Sonics Ultrason. 32(3), 458–464 (1985).
[Crossref]

Kristoffersen, K.

S. Bjaerum, H. Torp, and K. Kristoffersen, “Clutter filter design for ultrasound color flow imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 49(2), 204–216 (2002).
[Crossref] [PubMed]

Lam, E. Y.

A. C. Chan, E. Y. Lam, and V. J. Srinivasan, “Comparison of Kasai autocorrelation and maximum likelihood estimators for Doppler optical coherence tomography,” IEEE Trans. Med. Imaging 36, 1033 (2013).

Lee, J.

J. Lee, H. Radhakrishnan, W. Wu, A. Daneshmand, M. Climov, C. Ayata, and D. A. Boas, “Quantitative imaging of cerebral blood flow velocity and intracellular motility using dynamic light scattering-optical coherence tomography,” J. Cereb. Blood Flow Metab. 33(6), 819–825 (2013).
[Crossref] [PubMed]

J. Lee, W. Wu, J. Y. Jiang, B. Zhu, and D. A. Boas, “Dynamic light scattering optical coherence tomography,” Opt. Express 20(20), 22262–22277 (2012).
[Crossref] [PubMed]

Leitgeb, R. A.

Li, X.

Lin, C. P.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Loupas, T.

A. P. Kadi and T. Loupas, “On the performance of regression and step-initialized IIR clutter filters for color Doppler systems in diagnostic medical ultrasound,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 42(5), 927–937 (1995).
[Crossref]

MacDonald, D. J.

Namekawa, K.

C. Kasai, K. Namekawa, A. Koyano, and R. Omoto, “Real-Time Two-Dimensional Blood Flow Imaging Using an Autocorrelation Technique,” IEEE Trans. Sonics Ultrason. 32(3), 458–464 (1985).
[Crossref]

Nelson, J. S.

Omoto, R.

C. Kasai, K. Namekawa, A. Koyano, and R. Omoto, “Real-Time Two-Dimensional Blood Flow Imaging Using an Autocorrelation Technique,” IEEE Trans. Sonics Ultrason. 32(3), 458–464 (1985).
[Crossref]

Pan, Y.

Park, S. B.

Y. B. Ahn and S. B. Park, “Estimation of mean frequency and variance of ultrasonic Doppler signal by using second-order autoregressive model,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 38(3), 172–182 (1991).
[Crossref] [PubMed]

Podoleanu, A. G.

A. G. Podoleanu, “Optical coherence tomography,” J. Microsc. 247(3), 209–219 (2012).
[Crossref] [PubMed]

Puliafito, C. A.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Radhakrishnan, H.

J. Lee, H. Radhakrishnan, W. Wu, A. Daneshmand, M. Climov, C. Ayata, and D. A. Boas, “Quantitative imaging of cerebral blood flow velocity and intracellular motility using dynamic light scattering-optical coherence tomography,” J. Cereb. Blood Flow Metab. 33(6), 819–825 (2013).
[Crossref] [PubMed]

V. J. Srinivasan, D. N. Atochin, H. Radhakrishnan, J. Y. Jiang, S. Ruvinskaya, W. Wu, S. Barry, A. E. Cable, C. Ayata, P. L. Huang, and D. A. Boas, “Optical coherence tomography for the quantitative study of cerebrovascular physiology,” J. Cereb. Blood Flow Metab. 31(6), 1339–1345 (2011).
[Crossref] [PubMed]

Ren, H.

Ruvinskaya, S.

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Science (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
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Figures (11)

Fig. 1
Fig. 1 Examples of non-origin CORs in real Doppler OCT signals. Multiple rotations in the top row correspond to higher velocities, while partial rotations in the bottom row represent lower velocities. All signals are shown on the same scale (a.u.). Re, real axis; Im, imaginary axis.
Fig. 2
Fig. 2 Illustration of the bias in the high-pass filtering method to determine CORs. Re, real axis; Im, imaginary axis.
Fig. 3
Fig. 3 Examples of CORs determined by the high-pass filtering and variance minimization methods. Re, real axis; Im, imaginary axis.
Fig. 4
Fig. 4 (a) Definition of the offset between the high-pass filtering-determined COR (green), variance minimization-determined COR (blue), and the true COR (black). Re, real axis; Im, imaginary axis. (b) The characterized offset as a function of the degree of rotation and noise level. The rotation represents flow velocity when the OCT scan speed and data point number are fixed. The noise level is presented in the standard deviation (SD) of added random noise divided by the radius of rotation (ROR).
Fig. 5
Fig. 5 Diagram of the iterative process used to determine the offset extrapolation method. HP, high-pass filtering; VM, variance minimization.
Fig. 6
Fig. 6 Examples of four CORs for different rotations and noise levels: true, high-pass filtering (HP)-determined, variance minimization-determined, and offset extrapolation-determined CORs. Re, real axis; Im, imaginary axis.
Fig. 7
Fig. 7 Accuracy in COR determination. The example velocity values correspond to the rotation values when 147,000 Ascan/s and 8 Ascan/position are used. SD, standard deviation of simulated random noise; ROR, radius of rotation.
Fig. 8
Fig. 8 Accuracy in velocity measurement. The example velocity values correspond to the rotation values when 147,000 Ascan/s and 8 Ascan/position are used. SD, the standard deviation of simulated random noise; ROR, the radius of rotation.
Fig. 9
Fig. 9 Examples of en face OCT angiogram and Doppler OCT velocity maps. Every en face map was obtained by either maximum intensity projection (angiogram) or mean intensity projection (Doppler) over ± 35 μm depth. Scale bar, 100 μm.
Fig. 10
Fig. 10 Examples for en face Doppler OCT velocity maps and blood flow values, for different clutter rejection methods. Scale bar, 100 μm.
Fig. 11
Fig. 11 Statistics of the flow conservation error (a) and the absolute flow (b). *p<0.05, ***p<0.001 (paired t-test). Data are presented as mean ± SE. NS, Not Significant.

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