Noise statistics of phase-resolved optical coherence tomography (OCT) imaging are complicated and involve noises of OCT, correlation of signals, and speckles. In this paper, the statistical properties of phase shift between two OCT signals that contain additive random noises and speckle noises are presented. Experimental results obtained with a scattering tissue phantom are in good agreement with theoretical predictions. The performances of the dual-beam method and conventional single-beam method are compared. As expected, phase shift noise in the case of the dual-beam-scan method is less than that for the single-beam method when the transversal sampling step is large.
© 2014 Optical Society of America
Phase-resolved optical coherence tomography (OCT) is a powerful extension to several functional imaging. For example, cross-sectional flow images are obtained by using the Doppler phase shift caused by the motion of blood cells [1–7], the cross-sectional biomechanical property can be mapped by detecting local deformation from the phase of OCT [8–10], and the local photothermal effect can be detected [11–14]. Because these methods are based on the OCT technique , they allow three-dimensional high-resolution imaging.
To evaluate the quality of phase-resolved OCT images, a generalized formulation of the phase shift noise would be a powerful technique. Several previous works have addressed the phase shift noise by considering simple additive noise  and/or decorrelation of signals because of scanning of a probing beam [17, 18]. However, they lack the contribution of speckle noise; the fluctuation of a signal in a turbid tissue image results in a varying instantaneous signal-to-noise ratio (SNR). Hence, the simple additive noise model with a constant signal intensity is not valid.
Recently, our and other groups introduced the dual-beam-scan Doppler detection method, where two probing beams are separated along the scanning direction, to increase the sensitivity to motion [19–21]. This dual-beam-scan Doppler method measures phase shift between two OCT signals obtained with two probing beams. Because the detection scheme and signal processing of this technique differ from those of conventional phase-resolved OCT, evaluation of its performance is difficult.
In this paper, statistics of phase-resolved OCT imaging with additive, speckle, and decorre-lation noises are addressed. The statistics of generalized phase-resolved OCT are formulated in Section 2. The essential parameter is correlation coefficient between two OCT signals and described with specifications of OCT (Section 2.2). The performances of Doppler OCT with conventional single-beam and dual-beam methods are presented in Section 3 according to the formulation in Section 2. We evaluate the performances of phase-resolved Doppler OCT. Phase-resolved imaging performances are compared between the dual-beam-scan and conventional single-beam methods in phantom tissue experiments (Section 4).
2. Statistics of phase-resolved OCT
Here, we describe the statistics of phase-resolved OCT; i.e., phase shift and correlation between two complex OCT signals. Standard deviation of the phase shift is formulated with the support of previous studies in the field of synthetic aperture radar [22, 23]. The population correlation coefficient between two OCT signals is an essential parameter of the statistics of the phase shift. This correlation coefficient is described with specifications of OCT. The statistics of the real estimator of the phase shift are then addressed and a parameter, the effective number of independent samples, is described. Notations of symbols are listed in table 1.
2.1. Statistics of phase shift
The statistics of phase-resolved OCT with additive, speckle, and decorrelation noises are described here. A statistical model with speckle, where the OCT signals vary randomly, is assumed. It is then shown that the effect of additive noise in complex OCT signals can be expressed as a part of decorrelation.
For phase-resolved imaging, the phase shift between two complex OCT signals is used. By considering the two measurements as random variables G1, G2, phase shift is calculated as a phase term of the Hermitian product of two measured OCT signals;
An realization of OCT signal s is the sum of interference signals from scatterers in a coherent detection volume:
By considering that the additive noises N1 and N2 are zero-mean complex circular Gaussian variables and independent of each other and the signals S1 and S2, the measured signals G1 and G2 are also zero-mean complex circular Gaussian variables. The statistical properties of the product of two complex zero-mean circular Gaussian variables have been studied in the field of synthetic aperture radar [22, 23] and it is known that the probability density function (PDF) of the sample phase shift Δϕ (Eq. (1)) can be expressed asEquation (5) indicates that the parameters ρ and Δϕ0 are the amplitude and phase of the population complex correlation coefficient for the measured signals, respectively. Δϕ0 represents the population phase shift.
The expectation and standard deviation of the sample phase shift Δϕ are described as
2.2. Correlation coefficient of OCT signals
The correlation coefficient ρ is an essential parameter for defining the statistics of the phase shift. Hence, it determines the performance of phase-resolved OCT. Here, the generalized formulation of correlation coefficient ρ of OCT is described and can be applied to both conventional and dual-beam-scan OCT. The estimations of correlation coefficients are then presented.
The parameter ρ can be described as the following according to the definition of measured signals G1, G2 (Eq. (2));Equation (11) gives the correlation coefficient between two OCT signals S1 and S2 and SNRi = E[|Si|2]/E[|Ni|2] (i=1,2) are the expected signal-to-noise ratios of each measurement. As mentioned in the following section (Section 2.2), ρs is decreased by means of the displacement of the sampling location on tissue, tissue deformation, scattering, and also, in the case of dual-beam-scan OCT, differences in the system properties between two detections. It can be understood that the denominator of Eq. (10) represents the degree of decorrelation caused by additive random noise. For simplicity, here we define a representative of the SNR as
Note that the previously presented formula for decorrelation noise of Doppler OCT (Eq. (10) in ) is identical to Eq. (4) when ρ = α2: i.e., the correlation coefficient depends only on transversal sampling displacement, and Δϕ0 = 0. However, the current model presented here includes the effects of both additive noise and speckle noise. The measured OCT signal G is assumed to be the sum of the varying signal S and noise N. The effect of speckle on phase shift might be accounted for by the varying instantaneous signal-to-noise ratio of each realization |s|2/|n|2.
The correlation coefficient between two OCT signals ρs is defined by referring to previous studies [24–26]. For generalization, two OCT signals are assumed to be detected in two independent channels. Here, the OCT signals (Eq. (3)) can be redescribed as a time series of two channels;
The population correlation coefficient of OCT signals in Eq. (11) and population phase shift Δϕ0 can be described from the signal cross-correlation coefficient between realizations s1, s2:
Considering Gaussian beam profiles and a Gaussian coherence function of the light source, PSFs are expressed asEq. (15) and (13) into Eq. (14):
In the case of solid tissues (no diffusion and no deformation), the correlation coefficient can be defined as:
The estimation of the parameter ρ is the sample correlation between realizations g1 and g2:
According to Eq. (10), the correlation coefficient of the OCT signal ρ̂s can be estimated asEq. (12)):
2.3. Maximum likelihood estimation of phase shift
The mean of phase shift Δϕ (Eq. (8)) is a biased estimator for Δϕ0. According to the expectation (Eq. (6)), the mean estimator results in large offset of the estimation from the population parameter Δϕ0 when it is close to the boundaries of phase measurement range . The maximum likelihood estimation (MLE) of parameter Δϕ0 will be used for better estimation. The MLE of population phase shift Δϕ0 with ν independent realizations and (κ = 1, ...,ν) is 22, 23]
The phase shift noise of the MLE σΔϕ̂0 is characterized by first- and second-order moments E[Δϕ̂0], E[Δϕ̂02] asEq. (24). However, the calculation cost is high. To reduce the computation time, approximations of the moments have been found. The moments of Δϕ̂0 can be expressed by the summation of an infinite series as shown in Appendix A. The decrement of the higher-order term from the previous term in the series is from about 10 to more than 90 %. Hence, asymptotic expressions of expectation, variance, and other statistics of Δϕ̂0 can be obtained by taking the first several ten terms of the series. Summing up to the ∼ 30-th order provides a good approximation. The only exception is the case when ν → ∞ or ρ → 1, where the summation does not asymptotically converge to the real value. However, it is a rare case in real experiments and can thus be ignored.
2.4. Practical estimators
The MLE of phase shift Δϕ̂0 has been shown as Eq. (23). However, in the real case, it is almost impossible to acquire several independent samples for a single location.
To estimate the moments of the estimated phase shift and the sample correlation coefficient, the effective number of independent samples (ENIS) within an averaging window should be known. Taking the analogy of synthetic aperture radar , the ENIS can be defined using the cross-correlation coefficient between Hermitian products as
3. Performance of flow imaging with phase-resolved OCT
Here, the statistical properties of phase-resolved OCT investigated in the previous sections are used to analyze phase-resolved Doppler OCTs. The theoretical performances of conventional phase-resolved Doppler OCT and dual-beam-scan Doppler OCT [19, 20, 31] are investigated. Experimental data are acquired to validate the statistical analysis. The comparison of phase-resolved OCTs is then discussed.
Phase-resolved imaging is a common method for cross-sectional flow imaging by OCT. The phase shift between OCT signals at different time points is caused by axial movements of samples, and expressed as
The sensitivity of flow imaging is defined by the minimum detectable flow in images. This minimum detectable flow can be defined as the velocity corresponding to the random variation of the phase shift for surrounding solid tissue.Equation (32) clearly shows that longer time delay and smaller phase shift noise increase the sensitivity of flow imaging. To compare the phase-resolved flow imaging performances of conventional Doppler OCT and dual-beam-scan OCT, phase noise in each method is defined in the following sections.
3.1. Conventional phase-resolved Doppler OCT
Conventional Doppler OCT uses a single probe beam and single detection channel, and applies auto-correlation processing to obtain the phase shift. In this case, h1 = h2 and η1 = η2. Under this condition, the signal correlation coefficient with a static tissue is obtained from Eqs. (18) and (19) as2], , which is the transversal sampling step between adjacent axial lines.Eq. (29).
3.2. Dual-beam-scan Doppler OCT
The polarization-multiplexing dual-beam-scan Doppler method detects two OCT signals using different polarization states at the same location of the static tissue . Hence, the signal correlation coefficient with a static tissue can be obtained from Eqs. (18) and (19) as
4. Evaluation of phase shift noise
To validate and demonstrate the phase-resolved OCT analysis, an experiment using static tissue was conducted. The behaviors of the phase shift noise in phase-resolved OCT and dual-beam-scan OCT are compared.
4.1. Experimental setup and method
A dual-beam-scan OCT (DB-OCT) system was used for experiments of both single-beam and dual-beam Doppler OCT. The comparison between two different methods is eased by using single system because the conditions and system parameters are identical except the probe beam power. The details of the system were described in a previous work . The ophthalmic lens was removed for tissue phantom imaging. A superluminescent diode with central wavelength of 840 nm and spectral bandwidth of 50 nm was used. Polarization optics (i.e., a Faraday rotator and a quarter waveplate) are introduced to avoid phase retardation due to birefringence of samples. The beam spot radius on tissue was estimated to be 16.5 μm using optical simulation software (ZEMAX, Radiant Zemax, LLC, Redmond, WA). The axial resolution was measured to be about 9.5 μm (full-width at half maximum, 6 dB width) in air. Theoretically, this corresponds to . In this system, two probing beams are divided from the same light source and pass through common optics in the sample arm. It is thus assumed that .
The scattering phantom was made by fixing 1 % soybean oil lipid emulsion (Intralipos®20%, Otsuka Pharmaceutical Factory Inc., Japan) with 10 % porcine gelatin (G2500, Sigma-Aldrich Corp., St. Louis, MO).
Phase-resolved OCT imaging was performed with 256 axial lines/frame and different fractional sampling steps δx from 0.1 to 2.
The conventional single-beam Doppler OCT system can use power of two beams into single probe beam. To emulate the single-beam Doppler method using this DB-OCT system, the two detected OCT signals are summed after a bulk motion correction as33]. Hence, the ESNR of the single-beam method is theoretically twice that of the dual-beam method; ESNR(SB) = 2ESNR(SB). Two signals g1 and g2 are assigned as g1 ≜ gH(xi, zl), g2 ≜ gV(xi, zl) in the case of the dual-beam method and g1 ≜ g(xi, zl), g2 ≜ g(xi+1, zl) in the case of the single-beam method using Eq. (37). The sample phase differences of the dual-beam and single-beam methods are calculated and analyzed.
Figure 1 is a cross-sectional OCT image of the scattering phantom. The phase-resolved phantom images with several fractional sampling steps are shown in Fig. 2. A part of an image with a constant image depth was assigned as a region of interest (ROI) for analysis as indicated by a yellow box in Fig. 1 (256 lines × 10 pixels). A set of 100 B-scans are acquired and each statistics are measured every B-scan. The final measurements of statistics are averages of 100 realizations.
As expected from Eq. (7), the phase shift noise can be characterized by the correlation co-efficient of measured signals ρ. In Fig. 3, sample standard deviations of sample phase shift SΔϕ of dual-beam and emulated single-beam methods are plotted against the sample correlation r obtained using Eqs. (20) and (9). The solid curve is the line calculated with Eq. (7) at Δϕ0 = 0. Experimental and theoretical results are in good agreement.
To compare the dual-beam and single-beam methods, phase shift noise SΔϕ is plotted against the fractional sampling step δx in Fig. 4. Each curve represents expected phase shift noise (standard deviation of the phase shift, Eq. (7)) for the dual-beam and single-beam methods. The correlation coefficient ρPol. in the dual-beam method was estimated to be 0.91 by averaging the estimation of each n-th measurement, where we assume and use Eq. (21). is the sample representative SNR of the dual-beam method calculated using Eq. (22) as approx. 11 dB. The population representative SNR ESNR is set to 11 dB for the dual-beam method and 14 dB for the single-beam method. As expected, phase shift noise is almost constant for all fractional sampling steps in the dual-beam method, because two signals are obtained at the same position on the sample no matter the magnitude of the fractional sampling step. The phase shift noise is significantly small compared with that for the single-beam method at large δx. The transitional point of the fractional sampling step where the magnitudes of phase shift noise become identical between single-beam and dual-beam methods is
This is plotted as Fig. 5 for ρPol. = 0.91. When the fractional sampling step is larger than δxc, the dual-beam method exhibits less phase shift noise. When δx < δxc, the single-beam method is better. And δxc is larger as ESNR decreases. These characteristics can be easily understood as follows. In the case of smaller δxc and lower ESNR, phase shift noise caused by additive random noise is dominant. Since the single-beam method exhibits a larger SNR by a factor of 2, the phase shift noise of the single-beam method is less than that of the dual-beam method.
In Fig. 4, the predicted phase shift noise is greater than the experimental results at large δx in the case of the single-beam method. This would be explained by elongation of the beam profile . A broadened beam profile increases the signal correlation coefficient ρs and decreases the phase shift noise.
The phase shift noise against the ESNR is shown in Fig. 6. To virtually change the ESNR, complex circular Gaussian noise is numerically generated and added to complex OCT data. The phase shift noise decreases as the ESNR increases. However, the phase shift noise approaches an asymptotic value.
In the high-ESNR regime, decorrelation phase shift noise is dominant. The equivalent representative SNR of a signal correlation coefficient ESNRρs can be described by equating as
4.2.1. Averaged phase shift noise
In practical applications, spatial complex averaging (Eq. (26)) is used to enhance the contrast of phase-resolved images. The performances with averaging in conventional and dual-beam-scan phase-resolved flow imaging are compared in this section.
First, estimations of the correlation coefficient of the Hermitian product ρg2 are evaluated because ρg2 is essential to the estimation of the effective number of independent samples ENIS (Eq. (27)). The Hermitian product was calculated for the single B-scan image and the spatial autocorrelation was obtained in the ROI according to the spatial displacement steps Δx and Δz, which correspond to the spatial lengths according to the single pixel. Figure 7 shows the profiles of estimated ρg2,SS. The horizontal axis of each plot is normalized by the beam spot radius, w = 16.5 μm, and the axial resolution defined as half width at e−2 of axial PSF, . The solid curves in each plot show the expected profiles from Eqs. (29) by substituting and . Here, ρPol. and the ESNR were calculated from data obtained in the experiment. They show that the experimental data and estimation using Eq. (29) are in good agreement.
The suppression of phase shift noise by the moving average is shown in Fig. 8. The effective number of independent samples within the window ENIS is calculated using Eq. (27). Solid curves show the approximate phase shift noise numerically simulated using Eqs. (25), (43), and (44) by summing series up to the 50-th order. The experimental results and numerical estimations are in good agreement for large δx.
When the fractional sampling step is very small, (i.e., δx < 0.2), measured results with lateral averaging deviate from predicted values. Perhaps under this condition, the OCT signals do not significantly differ between the two axial lines. The phase shift estimation σΔϕ̂0 does not obey Eq. (24). When correlation coefficient ρs is close to 1, phase shift Δϕ0 is constant. In addition, if δx is small, the Hermitian products extracted along the lateral direction can be considered as a sum of a constant phasor and a random phasor. If this assumption is valid, the phase shift noise will decrease by the square root of the number of averaged realizations. In fact, the noise suppression ratio under this condition is close to , where N is the number of sampling points in the lateral averaging window.
In order to compare dual-beam and single-beam methods, phase shift noise with lateral moving average was calculated where the window size is up to the optical resolution. The window sizes for each method are as follows.Eqs. (27), (40), and (41) are plotted in Fig. 9. Since the window size must be an integers (Eqs. (40) and (41)), the population standard deviation of the MLE of phase shift σΔϕ̂0 and estimated effective number ENIS exhibit discontinuous values along δx as shown by solid curves in Fig. 9. The transitional point of the fractional sampling step is nearly the same as that without averaging. However, the phase shift noise of the dual-beam method at small fractional sampling step is reduced and approaches that of the single-beam method.
The essential factor that explains the phase shift noise in phase-resolved OCT is the correlation coefficient of measured OCT signals. The phase shift noise relying on the SNR can be treated as the decorrelation of measured signals caused by additive noise. Hence, the phase shift noises derived from additive noise and structural decorrelation are unified. The presented statistical model accounts for the spatial variation of the instantaneous SNR; i.e., speckle. The introduced statistics well describe the imaging performance of phase-resolved OCT.
In the formulation of the correlation coefficient of OCT signals (Eq. (14)), we did not consider an effect of displacement of objects during integration of photons at a detector . This effect will result in changes of population parameters in the presented statistical model; i.e., Δϕ0 and ρs. The further alterations for the presented study according to the previous work will provide a statistical analysis tool of phase-resolved OCT that is more accurate.
In this study, we compared the dual-beam Doppler method and the inter-line single-beam Doppler method. When the transversal sampling is coarse, the phase shift noise of the dual-beam method is less than that of the conventional Doppler method as expected. This indicates that there is a great advantage in the case of systems with high spatial resolution. The imaging speed and/or imaging range can be increased by increasing the transversal sampling step as a level of phase shift noise is low.
Recently, Doppler methods with dedicated scanning protocols have been employed to increase the time delay and increase the flow sensitivity [35–37]. With high-dense transversal sampling, it is predicted that the single-beam method will surpass the dual-beam method. However, repeatability of a beam scanning mechanism and/or sample fluctuation perhaps limit the advantage . As shown in Fig. 3, a small reduction of the correlation coefficient will result in a rapid increase of the phase shift noise. On the other hand, the dual-beam-scan method can be used with a simple raster scanning protocol.
For vasculature imaging in optical coherence angiography, squared Doppler phase shifts are calculated to contrast vessels . The response to flow can be defined by the second moment of the phase shift estimation E [Δϕ̂2]. Since the lateral motion of samples reduces the correlation coefficient between OCT signals at different time points and hence increases E[Δϕ̂2], the squared Doppler phase shift imaging is expected to be sensitive to not only axial motion but also lateral movement.
The statistical properties of phase-resolved OCT imaging were described. The investigated statistics of phase-resolved OCT were validated by evaluating phase shift noise measured with a static tissue phantom. Flow imaging performances of dual-beam-scan phase-resolved Doppler OCT and the conventional single-beam method were compared and discussed using the presented statistics. The dual-beam method exhibited lower phase shift noise for coarse transversal sampling than the single-beam method. The presented statistics of phase-resolved OCT are useful in investigating, comparing, and designing phase-resolved OCT systems.
Appendix A. Moments of the maximum likelihood estimate of phase shift
From the moment generating function, the n-th order moment of the sample phase shift is obtained asEq. (42):
References and links
1. Y. Zhao, Z. Chen, C. Saxer, S. Xiang, J. F. d. Boer, and J. S. Nelson, “Phase-resolved optical coherence tomography and optical Doppler tomography for imaging blood flow in human skin with fast scanning speed and high velocity sensitivity,” Opt. Lett. 25, 114–116 (2000). [CrossRef]
2. B. R. White, M. C. Pierce, N. Nassif, B. Cense, B. H. Park, G. J. Tearney, B. E. Bouma, T. C. Chen, and J. F. d. Boer, “In vivo dynamic human retinal blood flow imaging using ultra-high-speed spectral domain optical Doppler tomography,” Opt. Express 11, 3490–3497 (2003). [CrossRef] [PubMed]
3. R. Leitgeb, L. Schmetterer, W. Drexler, A. Fercher, R. Zawadzki, and T. Bajraszewski, “Real-time assessment of retinal blood flow with ultrafast acquisition by color Doppler Fourier domain optical coherence tomography,” Opt. Express 11, 3116–3121 (2003). [CrossRef] [PubMed]
5. 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, 1219–1223 (2009). [CrossRef] [PubMed]
6. D. Y. Kim, J. Fingler, J. S. Werner, D. M. Schwartz, S. E. Fraser, and R. J. Zawadzki, “In vivo volumetric imaging of human retinal circulation with phase-variance optical coherence tomography,” Biomed. Opt. Express 2, 1504–1513 (2011). [CrossRef] [PubMed]
7. B. Braaf, K. A. Vermeer, V. A. D. Sicam, E. van Zeeburg, J. C. van Meurs, and J. F. de Boer, “Phase-stabilized optical frequency domain imaging at 1-m for the measurement of blood flow in the human choroid,” Opt. Express 19, 20886–20903 (2011). [CrossRef] [PubMed]
8. R. K. Wang, S. Kirkpatrick, and M. Hinds, “Phase-sensitive optical coherence elastography for mapping tissue microstrains in real time,” Appl. Phys. Lett. 90, 164105, 2007). [CrossRef]
10. B. F. Kennedy, S. H. Koh, R. A. McLaughlin, K. M. Kennedy, P. R. T. Munro, and D. D. Sampson, “Strain estimation in phase-sensitive optical coherence elastography,” Biomed. Opt. Express 3, 1865–1879 (2012). [CrossRef] [PubMed]
11. T. Akkin, D. P. Dav, J.-I. Youn, S. A. Telenkov, H. G. R. III, and T. E. Milner, “Imaging tissue response to electrical and photothermal stimulation with nanometer sensitivity,” Lasers Surg. Med. 33, 219–225 (2003). [CrossRef] [PubMed]
12. S. A. Telenkov, D. P. Dave, S. Sethuraman, T. Akkin, and T. E. Milner, “Differential phase optical coherence probe for depth-resolved detection of photothermal response in tissue,” Phys. Med. Biol. 49, 111–119 (2004). [CrossRef] [PubMed]
13. D. C. Adler, S.-W. Huang, R. Huber, and J. G. Fujimoto, “Photothermal detection of gold nanoparticles using phase-sensitive optical coherence tomography,” Opt. Express 16, 4376–4393 (2008). [CrossRef] [PubMed]
14. H. H. Mller, L. Ptaszynski, K. Schlott, C. Debbeler, M. Bever, S. Koinzer, R. Birngruber, R. Brinkmann, and G. Httmann, “Imaging thermal expansion and retinal tissue changes during photocoagulation by high speed OCT,” Biomed. Opt. Express 3, 1025–1046 (2012). [CrossRef]
15. W. Drexler and J. G. Fujimoto, Optical Coherence Tomography: Technology and Applications (Springer, 2008). [CrossRef]
16. S. Yazdanfar, C. Yang, M. Sarunic, and J. Izatt, “Frequency estimation precision in Doppler optical coherence tomography using the Cramer-Rao lower bound,” Opt. Express 13, 410–416 (2005). [CrossRef] [PubMed]
17. B. H. Park, M. C. Pierce, B. Cense, S.-H. Yun, M. Mujat, G. J. Tearney, B. E. Bouma, and J. F. d. Boer, “Real-time fiber-based multi-functional spectral-domain optical coherence tomography at 1.3 μm,” Opt. Express 13, 3931–3944 (2005). [CrossRef] [PubMed]
18. B. J. Vakoc, G. J. Tearney, and B. E. Bouma, “Statistical properties of phase-decorrelation in phase-resolved Doppler optical coherence tomography,” IEEE Trans. Med. Imaging 28, 814–821 (2009). [CrossRef] [PubMed]
19. S. Makita, F. Jaillon, M. Yamanari, M. Miura, and Y. Yasuno, “Comprehensive in vivo micro-vascular imaging of the human eye by dual-beam-scan Doppler optical coherence angiography,” Opt. Express 19, 1271–1283 (2011). [CrossRef] [PubMed]
20. S. Zotter, M. Pircher, T. Torzicky, M. Bonesi, E. Gtzinger, R. A. Leitgeb, and C. K. Hitzenberger, “Visualization of microvasculature by dual-beam phase-resolved Doppler optical coherence tomography,” Opt. Express 19, 1217–1227 (2011). [CrossRef] [PubMed]
21. F. Jaillon, S. Makita, E.-J. Min, B. H. Lee, and Y. Yasuno, “Enhanced imaging of choroidal vasculature by high-penetration and dual-velocity optical coherence angiography,” Biomed. Opt. Express 2, 1147–1158 (2011). [CrossRef] [PubMed]
22. L. Jong-Sen, K. Hoppel, S. Mango, and A. Miller, “Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery,” IEEE Trans. Geosci. Remote Sens. 32, 1017–1028 (1994). [CrossRef]
23. R. J. A. Tough, D. Blacknell, and S. Quegan, “A statistical description of polarimetric and interferometric synthetic aperture radar data,” Proc. R. Soc. Lond. A 449, 567–589 (1995). [CrossRef]
24. J. Walther and E. Koch, “Transverse motion as a source of noise and reduced correlation of the Doppler phase shift in spectral domain OCT,” Opt. Express 17, 19698–19713 (2009). [CrossRef] [PubMed]
25. V. J. Srinivasan, S. Sakadi, I. Gorczynska, S. Ruvinskaya, W. Wu, J. G. Fujimoto, and D. A. Boas, “Quantitative cerebral blood flow with optical coherence tomography,” Opt. Express 18, 2477–2494 (2010). [CrossRef] [PubMed]
27. A. Szkulmowska, M. Szkulmowski, A. Kowalczyk, and M. Wojtkowski, “Phase-resolved Doppler optical coherence tomographylimitations and improvements,” Opt. Lett. 33, 1425–1427 (2008). [CrossRef] [PubMed]
28. V. X. Yang, M. L. Gordon, A. Mok, Y. Zhao, Z. Chen, R. S. Cobbold, B. C. Wilson, and I. A. Vitkin, “Improved phase-resolved optical Doppler tomography using Kasai velocity estimator and histogram segmentation,” Opt. Commun. 208, 209–214 (2002). [CrossRef]
29. A. Moreira, “Improved multilook techniques applied to SAR and SCANSAR imagery,” IEEE Trans. Geosci. Remote Sens. 29, 529–534, 1991). [CrossRef]
30. J. S. Bendat and A. G. Piersol, Random Data: Analysis and Measurement Procedures (John Wiley and Sons, 2010). [CrossRef]
32. S. Makita, F. Jaillon, M. Yamanari, and Y. Yasuno, “Dual-beam-scan Doppler optical coherence angiography for birefringence-artifact-free vasculature imaging,” Opt. Express 20, 2681–2692 (2012). [CrossRef] [PubMed]
33. K. Kurokawa, K. Sasaki, S. Makita, Y.-J. Hong, and Y. Yasuno, “Three-dimensional retinal and choroidal capillary imaging by power Doppler optical coherence angiography with adaptive optics,” Opt. Express 20, 22796–22812 (2012). [CrossRef] [PubMed]
35. I. Grulkowski, I. Gorczynska, M. Szkulmowski, D. Szlag, A. Szkulmowska, R. A. Leitgeb, A. Kowalczyk, and M. Wojtkowski, “Scanning protocols dedicated to smart velocity ranging in spectral OCT,” Opt. Express 17, 23736–23754 (2009). [CrossRef]
36. L. An, J. Qin, and R. K. Wang, “Ultrahigh sensitive optical microangiography for in vivo imaging of microcirculations within human skin tissue beds,” Opt. Express 18, 8220–8228 (2010). [CrossRef] [PubMed]
37. B. Braaf, K. A. Vermeer, K. V. Vienola, and J. F. de Boer, “Angiography of the retina and the choroid with phase-resolved OCT using interval-optimized backstitched b-scans,” Opt. Express 20, 20516–20534 (2012). [CrossRef] [PubMed]