Abstract

In a recent paper by Bosschaart et al. [Biomed. Opt. Express 4, 2570 (2013)] various algorithms of time-frequency signal analysis have been tested for their performance in blood analysis with spectroscopic optical coherence tomography (sOCT). The measurement of hemoglobin concentration and oxygen saturation based on blood absorption spectra have been considered. Short time Fourier transform (STFT) was found as the best method for the measurement of blood absorption spectra. STFT was superior to other methods, such as dual window Fourier transform. However, the algorithm proposed by Bosschaart et al. significantly underestimates values of blood oxygen saturation. In this comment we show that this problem can be solved by thorough design of STFT algorithm. It requires the usage of a non-gaussian shape of STFT window that may lead to an excellent reconstruction of blood absorption spectra from OCT interferograms. Our study shows that sOCT can be potentially used for estimating oxygen saturation of blood with the accuracy below 1% and the spatial resolution of OCT image better than 20 μm.

© 2014 Optical Society of America

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References

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2013 (1)

2012 (1)

2011 (1)

F. Robles, C. Wilson, G. Grant, and A. Wax, “Molecular imaging true-colour spectroscopic optical coherence tomography,” Nature Photon. 5, 744–747 (2011).
[Crossref]

2010 (2)

C. Kasseck, V. Jaedicke, N. C. Nils, H. Welp, and M. R. Hofmann, “Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography,” Opt. Commun. 283, 4816–4822 (2010).
[Crossref]

A. Karakoullis, E. Bousi, and C. Pitris, “Scatterer size-based analysis of optical coherence tomography images using spectral estimation techniques,” Opt. Express 18, 9181–9191 (2010).
[Crossref]

2009 (2)

2005 (2)

2004 (2)

2003 (1)

2000 (2)

1978 (1)

F. J. Harris, “On the use of windows for harmonic analysis with the discrete Fourier transform,” Proceedings of the IEEE 66, 51–83 (1978).
[Crossref]

1946 (1)

C. L. Dolph, “A current distribution for broadside arrays which optimizes the relationship between beam width and side-lobe level,” Proceedings of the IRE 34, 335–348 (1946).
[Crossref]

Aalders, M. C. G.

Bizheva, K.

Boppart, S.

Boppart, S. A.

Bosschaart, N.

Bousi, E.

Do, M.

Dolph, C. L.

C. L. Dolph, “A current distribution for broadside arrays which optimizes the relationship between beam width and side-lobe level,” Proceedings of the IRE 34, 335–348 (1946).
[Crossref]

Drexler, W.

Faber, D.

Faber, D. J.

Fercher, A. F.

Fujimoto, J. G.

Graf, R. N.

Grant, G.

F. Robles, C. Wilson, G. Grant, and A. Wax, “Molecular imaging true-colour spectroscopic optical coherence tomography,” Nature Photon. 5, 744–747 (2011).
[Crossref]

Harris, F. J.

F. J. Harris, “On the use of windows for harmonic analysis with the discrete Fourier transform,” Proceedings of the IEEE 66, 51–83 (1978).
[Crossref]

Hermann, B.

Hitzenberger, C. K.

Hofmann, M. R.

C. Kasseck, V. Jaedicke, N. C. Nils, H. Welp, and M. R. Hofmann, “Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography,” Opt. Commun. 283, 4816–4822 (2010).
[Crossref]

Ippen, E. P.

Jaedicke, V.

C. Kasseck, V. Jaedicke, N. C. Nils, H. Welp, and M. R. Hofmann, “Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography,” Opt. Commun. 283, 4816–4822 (2010).
[Crossref]

Kamalabadi, F.

Karakoullis, A.

Kartner, F. X.

Kasseck, C.

C. Kasseck, V. Jaedicke, N. C. Nils, H. Welp, and M. R. Hofmann, “Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography,” Opt. Commun. 283, 4816–4822 (2010).
[Crossref]

Kowalczyk, A.

Leitgeb, R.

Li, D.

Li, Y. L.

Marks, D.

Mik, E. G.

Morgner, U.

Nils, N. C.

C. Kasseck, V. Jaedicke, N. C. Nils, H. Welp, and M. R. Hofmann, “Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography,” Opt. Commun. 283, 4816–4822 (2010).
[Crossref]

Pitris, C.

Povazay, B.

Robles, F.

Robles, F. E.

Sattmann, H.

Schmetterer, L.

Seekell, K.

Smith, S. W.

S. W. Smith, “Digital filters,” in The Scientist and Engineer’s Guide to Digital Signal Processing2nd ed., (California Technical Publishing, 1997), pp. 261–350.

Sticker, M.

Taylor, F. J.

F. J. Taylor, “Window design method,” in Digital Filters: Principles and Applications with MATLAB, (California Technical Publishing, 2012), pp. 71–82.

F. J. Taylor, “Adaptive Filtering and Signal Analysis,” in Adaptive Digital Filters2nd ed., (Marcel Dekker Inc., 2001), pp. 10–23.

Unterhuber, A.

van Leeuwen, T. G.

Wax, A.

Welp, H.

C. Kasseck, V. Jaedicke, N. C. Nils, H. Welp, and M. R. Hofmann, “Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography,” Opt. Commun. 283, 4816–4822 (2010).
[Crossref]

Wilson, C.

F. Robles, C. Wilson, G. Grant, and A. Wax, “Molecular imaging true-colour spectroscopic optical coherence tomography,” Nature Photon. 5, 744–747 (2011).
[Crossref]

Wojtkowski, M.

Xu, C.

Yuan, H.

Appl. Opt. (1)

Biomed. Opt. Express (2)

Nature Photon. (1)

F. Robles, C. Wilson, G. Grant, and A. Wax, “Molecular imaging true-colour spectroscopic optical coherence tomography,” Nature Photon. 5, 744–747 (2011).
[Crossref]

Opt. Commun. (1)

C. Kasseck, V. Jaedicke, N. C. Nils, H. Welp, and M. R. Hofmann, “Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography,” Opt. Commun. 283, 4816–4822 (2010).
[Crossref]

Opt. Express (4)

Opt. Lett. (5)

Proceedings of the IEEE (1)

F. J. Harris, “On the use of windows for harmonic analysis with the discrete Fourier transform,” Proceedings of the IEEE 66, 51–83 (1978).
[Crossref]

Proceedings of the IRE (1)

C. L. Dolph, “A current distribution for broadside arrays which optimizes the relationship between beam width and side-lobe level,” Proceedings of the IRE 34, 335–348 (1946).
[Crossref]

Other (4)

F. J. Taylor, “Adaptive Filtering and Signal Analysis,” in Adaptive Digital Filters2nd ed., (Marcel Dekker Inc., 2001), pp. 10–23.

S. W. Smith, “Digital filters,” in The Scientist and Engineer’s Guide to Digital Signal Processing2nd ed., (California Technical Publishing, 1997), pp. 261–350.

F. J. Taylor, “Window design method,” in Digital Filters: Principles and Applications with MATLAB, (California Technical Publishing, 2012), pp. 71–82.

S. Prahl, “Optical Absorption of Hemoglobin,” http://omlc.ogi.edu/spectra/hemoglobin/index.html .

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Figures (7)

Fig. 1
Fig. 1 Left: an exact light spectrum (solid) and its version recovered from Fourier transform multiplied by gaussian window (dotted). Right: Fourier transform of light spectrum (solid) and applied window (dotted).
Fig. 2
Fig. 2 Simulated OCT scan with various STFT windows. The peak at 120 μm corresponds to the first boundary of the blood layer and the peak at 165 μm corresponds to the second boundary of the blood layer.
Fig. 3
Fig. 3 The shape of examined windows in z- (top) and k- domain (bottom).
Fig. 4
Fig. 4 The blood absorption spectra recovered by sOCT with various shapes of the windows and spectra fitted by a non-linear optimization.
Fig. 5
Fig. 5 The error of SO2 estimation for different values of peak separation in OCT scans and different STFT windows.
Fig. 6
Fig. 6 The estimation of oxygen saturation by sOCT with different types of STFT windows. The bars in right figure represent the standard deviation of the results from 30 simulations.
Fig. 7
Fig. 7 The estimation error for different windows and different values of oxygen saturation

Tables (3)

Tables Icon

Table 1 The results of blood parameter estimation by sOCT with different windows. Exact values tHb=150 g/l and SO2 = 85% were used.

Tables Icon

Table 2 The results of blood parameter estimation by sOCT with DW method. Exact values tHb=150 g/l and SO2 = 85% were used.

Tables Icon

Table 3 The results of blood parameter estimation by sOCT with STFT. Exact values tHb=150 g/l and SO2 = 85% were used.

Equations (5)

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μ a ( k ) = 0.01 [ t Hb ] { SO 2 μ a , HbO 2 ( k ) + ( 1 SO 2 ) μ a , Hb ( k ) } ,
w ( z ) = exp ( 4 ln 4 z 2 / Δ z 2 ) .
w ( z ) = { 1 if | z | < Δ z , 0 otherwise
Δ z = z max F c / 2 ,
W ( k ) = sin ( k Δ z / 2 ) k z max / 2 W s ( k ) ,

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