Abstract

Spectroscopic Optical Coherence Tomography (S-OCT) extracts depth resolved spectra that are inherently available from OCT signals. The back scattered spectra contain useful functional information regarding the sample, since the light is altered by wavelength dependent absorption and scattering caused by chromophores and structures of the sample. Two aspects dominate the performance of S-OCT: (1) the spectral analysis processing method used to obtain the spatially-resolved spectroscopic information and (2) the metrics used to visualize and interpret relevant sample features. In this work, we focus on the second aspect, where we will compare established and novel metrics for S-OCT. These concepts include the adaptation of methods known from multispectral imaging and modern signal processing approaches such as pattern recognition. To compare the performance of the metrics in a quantitative manner, we use phantoms with microsphere scatterers of different sizes that are below the system’s resolution and therefore cannot be differentiated using intensity based OCT images. We show that the analysis of the spectral features can clearly separate areas with different scattering properties in multi-layer phantoms. Finally, we demonstrate the performance of our approach for contrast enhancement in bovine articular cartilage.

© 2013 Optical Society of America

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2013

2012

D. K. Kasaragod, Z. Lu, J. Jacobs, and S. J. Matcher, “Experimental validation of an extended Jones matrix calculus model to study the 3D structural orientation of the collagen fibers in articular cartilage using polarization-sensitive optical coherence tomography,” Biomed. Opt. Express3(3), 378–387 (2012).
[CrossRef] [PubMed]

F. Fereidouni, A. N. Bader, and H. C. Gerritsen, “Spectral phasor analysis allows rapid and reliable unmixing of fluorescence microscopy spectral images,” Opt. Express20(12), 12729–12741 (2012).
[CrossRef] [PubMed]

F. E. Robles, J. W. Wilson, M. C. Fischer, and W. S. Warren, “Phasor analysis for nonlinear pump-probe microscopy,” Opt. Express20(15), 17082–17092 (2012).
[CrossRef]

J. Yi and V. Backman, “Imaging a full set of optical scattering properties of biological tissue by inverse spectroscopic optical coherence tomography,” Opt. Lett.37(21), 4443–4445 (2012).
[CrossRef] [PubMed]

P. Cernohorsky, D. M. de Bruin, M. van Herk, J. Bras, D. J. Faber, S. D. Strackee, and T. G. van Leeuwen, “In-situ imaging of articular cartilage of the first carpometacarpal joint using co-registered optical coherence tomography and computed tomography,” J. Biomed. Opt.17(6), 060501 (2012).
[CrossRef] [PubMed]

M. Hoffmann, M. Lange, F. Meuche, T. Reuter, H. Plettenberg, G. Spahn, and I. Ponomarev, “Comparison of Optical and Biomechanical Properties of Native and Artificial Equine Joint Cartilage under Load using NIR Spectroscopy,” Biomed. Tech. (Berl.)57, 1059–1061 (2012).
[PubMed]

B. C. Tay, T. H. Chow, B. K. Ng, and T. K. Loh, “Dual-window dual-bandwidth spectroscopic optical coherence tomography metric for qualitative scatterer size differentiation in tissues,” IEEE Trans. Biomed. Eng.59(9), 2439–2448 (2012).
[CrossRef] [PubMed]

2011

F. E. Robles, C. Wilson, G. Grant, and A. Wax, “Molecular imaging true-colour spectroscopic optical coherence tomography,” Nat. Photonics5(12), 744–747 (2011).
[CrossRef] [PubMed]

2010

2009

R. N. Graf, F. E. Robles, X. Chen, and A. Wax, “Detecting precancerous lesions in the hamster cheek pouch using spectroscopic white-light optical coherence tomography to assess nuclear morphology via spectral oscillations,” J. Biomed. Opt.14(6), 064030 (2009).
[CrossRef] [PubMed]

A. L. Oldenburg, M. N. Hansen, T. S. Ralston, A. Wei, and S. A. Boppart, “Imaging gold nanorods in excised human breast carcinoma by spectroscopic optical coherence tomography,” J. Mater. Chem.19(35), 6407–6411 (2009).
[CrossRef] [PubMed]

L. Zhang, J. Hu, and K. A. Athanasiou, “The role of tissue engineering in articular cartilage repair and regeneration,” Crit. Rev. Biomed. Eng.37(1-2), 1–57 (2009).
[CrossRef] [PubMed]

L. Zhang, J. Hu, and K. A. Athanasiou, “The role of Tissue Engineering in Articular Cartilage Repair and Regeneration,” Crit. Rev. Biomed. Eng.37(1-2), 1–57 (2009).
[CrossRef] [PubMed]

F. E. Robles, R. N. Graf, and A. Wax, “Dual window method for processing spectroscopic optical coherence tomography signals with simultaneously high spectral and temporal resolution,” Opt. Express17(8), 6799–6812 (2009).
[CrossRef] [PubMed]

D. J. Faber and T. G. van Leeuwen, “Are quantitative attenuation measurements of blood by optical coherence tomography feasible?” Opt. Lett.34(9), 1435–1437 (2009).
[CrossRef] [PubMed]

J. Yi, J. Gong, and X. Li, “Analyzing absorption and scattering spectra of micro-scale structures with spectroscopic optical coherence tomography,” Opt. Express17(15), 13157–13167 (2009).
[CrossRef] [PubMed]

P. Cimalla, J. Walther, M. Mehner, M. Cuevas, and E. Koch, “Simultaneous dual-band optical coherence tomography in the spectral domain for high resolution in vivo imaging,” Opt. Express17(22), 19486–19500 (2009).
[CrossRef] [PubMed]

B. Hermann, B. Hofer, C. Meier, and W. Drexler, “Spectroscopic measurements with dispersion encoded full range frequency domain optical coherence tomography in single- and multilayered non-scattering phantoms,” Opt. Express17(26), 24162–24174 (2009).
[CrossRef] [PubMed]

2008

M. A. Digman, V. R. Caiolfa, M. Zamai, and E. Gratton, “The phasor approach to fluorescence lifetime imaging analysis,” Biophys. J.94(2), L14–L16 (2008).
[CrossRef] [PubMed]

D. Sacchet, J. Moreau, P. Georges, and A. Dubois, “Simultaneous dual-band ultra-high resolution full-field optical coherence tomography,” Opt. Express16(24), 19434–19446 (2008).
[CrossRef] [PubMed]

2007

2006

T. Storen, A. Royset, L. O. Svaasand, and T. Lindmo, “Measurement of dye diffusion in scattering tissue phantoms using dual-wavelength low-coherence interferometry,” J. Biomed. Opt.11(1), 014017 (2006).
[CrossRef] [PubMed]

C. Xu, C. Vinegoni, T. S. Ralston, W. Luo, W. Tan, and S. A. Boppart, “Spectroscopic spectral-domain optical coherence microscopy,” Opt. Lett.31(8), 1079–1081 (2006).
[CrossRef] [PubMed]

2005

C. Xu, F. Kamalabadi, and S. A. Boppart, “Comparative performance analysis of time-frequency distributions for spectroscopic optical coherence tomography,” Appl. Opt.44(10), 1813–1822 (2005).
[CrossRef] [PubMed]

R. A. Leitgeb, W. Drexler, B. Povazay, B. Hermann, H. Sattmann, and A. F. Fercher, “Spectroscopic Fourier domain optical coherence tomography: principle, limitations, and applications,” Proc. SPIE5690, 151–158 (2005).
[CrossRef]

N. Jacobson and M. Gupta, “Design goals and solutions for display of hyperspectral images,” IEEE Trans. Geosci. Rem. Sens.43(11), 2684–2692 (2005).
[CrossRef]

X. Li, S. Martin, C. Pitris, R. Ghanta, D. L. Stamper, M. Harman, J. G. Fujimoto, and M. E. Brezinski, “High-resolution optical coherence tomographic imaging of osteoarthritic cartilage during open knee surgery,” Arthritis Res. Ther.7(2), R318–R323 (2005).
[CrossRef] [PubMed]

2004

2003

X. Xu, R. Wang, and J. B. Elder, “Optical clearing effect on gastric tissues immersed with biocompatible chemical agents investigated by near infrared reflectance spectroscopy,” J. Phys. D Appl. Phys.36(14), 1707–1713 (2003).
[CrossRef]

A. Wax, C. Yang, and J. A. Izatt, “Fourier-domain low-coherence interferometry for light-scattering spectroscopy,” Opt. Lett.28(14), 1230–1232 (2003).
[CrossRef] [PubMed]

T. M. Lee, A. L. Oldenburg, S. Sitafalwalla, D. L. Marks, W. Luo, F. J.-J. Toublan, K. S. Suslick, and S. A. Boppart, “Engineered microsphere contrast agents for optical coherence tomography,” Opt. Lett.28(17), 1546–1548 (2003).
[CrossRef] [PubMed]

J. S. Tyo, A. Konsolakis, D. I. Diersen, and R. C. Olsen, “Principal-components-based display strategy for spectral imagery,” IEEE Trans. Geosci. Rem. Sens.41(3), 708–718 (2003).
[CrossRef]

2000

V. Backman, M. B. Wallace, L. T. Perelman, J. T. Arendt, R. Gurjar, M. G. Müller, Q. Zhang, G. Zonios, E. Kline, J. A. McGilligan, S. Shapshay, T. Valdez, K. Badizadegan, J. M. Crawford, M. Fitzmaurice, S. Kabani, H. S. Levin, M. Seiler, R. R. Dasari, I. Itzkan, J. Van Dam, and M. S. Feld, “Detection of preinvasive cancer cells,” Nature406(6791), 35–36 (2000).
[CrossRef] [PubMed]

U. Morgner, W. Drexler, F. X. Kärtner, X. D. Li, C. Pitris, E. P. Ippen, and J. G. Fujimoto, “Spectroscopic optical coherence tomography,” Opt. Lett.25(2), 111–113 (2000).
[CrossRef] [PubMed]

R. Leitgeb, M. Wojtkowski, A. Kowalczyk, C. K. Hitzenberger, M. Sticker, and A. F. Fercher, “Spectral measurement of absorption by spectroscopic frequency-domain optical coherence tomography,” Opt. Lett.25(11), 820–822 (2000).
[CrossRef] [PubMed]

1995

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn.20(3), 273–297 (1995).
[CrossRef]

1993

M. Groß and F. Seibert, “Visualization of multidimensional image data sets using a neural network,” Vis. Comput.10(3), 145–159 (1993).
[CrossRef]

1991

A. K. Jeffery, G. W. Blunn, C. W. Archer, and G. Bentley, “Three-dimensional collagen architecture in bovine articular cartilage,” J. Bone Joint Surg. Br.73(5), 795–801 (1991).
[PubMed]

1990

T. Kohonen, “The self organizing map,” Proc. IEEE78(9), 1464–1480 (1990).
[CrossRef]

1969

J. Sammon, “A nonlinear mapping for data structure analysis,” IEEE Trans. Comput.C-18(5), 401–409 (1969).
[CrossRef]

Adler, D. C.

Archer, C. W.

A. K. Jeffery, G. W. Blunn, C. W. Archer, and G. Bentley, “Three-dimensional collagen architecture in bovine articular cartilage,” J. Bone Joint Surg. Br.73(5), 795–801 (1991).
[PubMed]

Arendt, J. T.

V. Backman, M. B. Wallace, L. T. Perelman, J. T. Arendt, R. Gurjar, M. G. Müller, Q. Zhang, G. Zonios, E. Kline, J. A. McGilligan, S. Shapshay, T. Valdez, K. Badizadegan, J. M. Crawford, M. Fitzmaurice, S. Kabani, H. S. Levin, M. Seiler, R. R. Dasari, I. Itzkan, J. Van Dam, and M. S. Feld, “Detection of preinvasive cancer cells,” Nature406(6791), 35–36 (2000).
[CrossRef] [PubMed]

Athanasiou, K. A.

L. Zhang, J. Hu, and K. A. Athanasiou, “The role of Tissue Engineering in Articular Cartilage Repair and Regeneration,” Crit. Rev. Biomed. Eng.37(1-2), 1–57 (2009).
[CrossRef] [PubMed]

L. Zhang, J. Hu, and K. A. Athanasiou, “The role of tissue engineering in articular cartilage repair and regeneration,” Crit. Rev. Biomed. Eng.37(1-2), 1–57 (2009).
[CrossRef] [PubMed]

Backman, V.

J. Yi, A. J. Radosevich, J. D. Rogers, S. C. P. Norris, İ. R. Çapoğlu, A. Taflove, and V. Backman, “Can OCT be sensitive to nanoscale structural alterations in biological tissue?” Opt. Express21(7), 9043–9059 (2013).
[CrossRef] [PubMed]

J. Yi and V. Backman, “Imaging a full set of optical scattering properties of biological tissue by inverse spectroscopic optical coherence tomography,” Opt. Lett.37(21), 4443–4445 (2012).
[CrossRef] [PubMed]

V. Backman, M. B. Wallace, L. T. Perelman, J. T. Arendt, R. Gurjar, M. G. Müller, Q. Zhang, G. Zonios, E. Kline, J. A. McGilligan, S. Shapshay, T. Valdez, K. Badizadegan, J. M. Crawford, M. Fitzmaurice, S. Kabani, H. S. Levin, M. Seiler, R. R. Dasari, I. Itzkan, J. Van Dam, and M. S. Feld, “Detection of preinvasive cancer cells,” Nature406(6791), 35–36 (2000).
[CrossRef] [PubMed]

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T. Storen, A. Royset, L. O. Svaasand, and T. Lindmo, “Measurement of dye diffusion in scattering tissue phantoms using dual-wavelength low-coherence interferometry,” J. Biomed. Opt.11(1), 014017 (2006).
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Figures (9)

Fig. 1
Fig. 1

Signal processing for S-OCT. The signal processing chain for S-OCT splits up into four separate blocks: OCT data processing, spectral analysis, the calculation of a spectroscopic metric and the color map (‘staining’).

Fig. 2
Fig. 2

Signal processing steps to calculate the spectroscopic metric and display the data split into four blocks: (1) preprocessing that includes normalization and averaging of the data. (2) Feature reduction, which contains one of the following methods: Phasor Analysis (PHA), Center of Mass (COM), Autocorrelation Function (ACF), Principal Component Analysis (PCA) or Sub Band (SUB). (3) Pattern recognition, an optional step, which consists of one of the following methods K-Means clustering, Self Organizing Map (SOM) or Support Vector Machine (SVM). (4) Displaying the results from the different methods in a color map using the RGB or HSV color model. Alternatively the output of the feature reduction method can be displayed directly, without applying pattern recognition, using an appropriate color map.

Fig. 3
Fig. 3

Standard intensity based OCT images of the microsphere phantoms. The insets show the structure of the particular phantom, blue indicates 3µm microspheres, while red indicates 1µm microspheres. Bar specifies 200µm.

Fig. 4
Fig. 4

Continuous mapping of spectroscopic metrics for phantom sample 3. Bar indicates 200µm.

Fig. 5
Fig. 5

S-OCT analysis by PCA and K-Means clustering of phantoms 1-4. Blue staining indicates 1µm microspheres, red staining 3µm microspheres. Rectangles indicate areas which were chosen for accuracy calculation. Bar indicates 200 µm.

Fig. 6
Fig. 6

OCT images of cartilage/bone sample after application of a static load for of 50N after 0minutes, 15minutes and 45minutes (from left to right).

Fig. 7
Fig. 7

Histological image of articular bovine cartilage tissue. Scale bar is 150µm.

Fig. 8
Fig. 8

Cartilage sample under mechanical load after 0 minutes, 5 minutes, 10 minutes, 20 minutes, 30 minutes and 45 minutes analyzed by S-OCT using the PCA extended K-Means metric. Bar indicates 200µm.

Fig. 9
Fig. 9

Shrinking algorithm to automatically reduce the number of cluster in K-Means. In an iterative manner adjacent cluster are merged and the data is clustered again until no cluster distances below the threshold are existing.

Tables (4)

Tables Icon

Table 1 Cluster accuracies for microsphere phantoms for the different metrics combined with a K-Means algorithm and a Self Organizing Map for two clusters.

Tables Icon

Table 2 Average cluster performance overall phantom samples

Tables Icon

Table 3 Accuracies for the Support Vector Machine supervised pattern recognition algorithm and microsphere phantoms. Each phantom is classified separately.

Tables Icon

Table 4 Accuracies for the Support Vector Machine supervised pattern recognition algorithm and microsphere phantoms. Overall classification performance for microspheres phantom samples.

Metrics