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High-resolution full-field optical coherence microscopy (FF-OCM) is demonstrated using a single broadband light-emitting diode (LED). The characteristics of the LED-illumination FF-OCM system are measured and compared to those obtained using a halogen lamp, the light source of reference in FF-OCM. Both light sources yield identical performance in terms of spatial resolution and detection sensitivity, using the same setup and camera. In particular, an axial resolution of 0.7 μm (in water) is reached. A Xenopus laevis tadpole and ex-vivo human skin have been imaged using both sources, resulting in similar images, showing for the first time that LEDs could favorably replace halogen lamps in high-resolution FF-OCM for biomedical imaging.
© 2016 Optical Society of America
1. Introduction
Optical coherence tomography (OCT) is a well-established technique for tomographic imaging of biological media with micrometer-scale spatial resolution [1,2]. OCT is based on low-coherence interferometry in order to produce cross-sectional images having an axial resolution determined by the temporal coherence of the light source. As for the transverse (lateral) resolution, it is determined by the size of the beam focused onto the sample. Full-field optical coherence tomography (FF-OCT), also referred to as full-field optical coherence microscopy (FF-OCM), is a parallelized approach of OCT, combining full-field illumination and detection, thus suppressing the need for lateral scanning since en-face images are acquired using an area camera [3–6]. In en-face imaging, there is no depth of field constraint, hence microscope objectives of relatively high numerical apertures (NA) can be used to achieve high transverse resolution. Isotropic resolutions of ∼1 μm are typically reported in FF-OCM [7,8]. FF-OCM has drawn much attention for biomedical imaging since it offers a sufficient penetration depth to exhibit in-depth structures of importance for clinicians and surgeons, combined with a cellular resolution, not reached by traditional OCT. Nevertheless, FF-OCM is generally limited to ex-vivo imaging, since the motion of in-vivo samples generally causes blurring of the interference over the time required for acquiring an en-face image, thus leading to a loss of the signal.
Different types of spontaneously-emitting light sources have been used in FF-OCM. After a review of those light sources and their main characteristics, this paper presents a high-resolution FF-OCM setup using a single light emitting diode (LED). The setup is characterized in terms of axial and transverse resolutions, detection sensitivity and operation speed for both LED and conventional halogen lamp illumination. Application of the LED-illumination FF-OCM setup for biomedical imaging is then investigated by imaging an ex-vivo Xenopus laevis tadpole and a human skin sample. A comparison of the tomographic images is carried out using a halogen lamp instead of the LED.
2. Light source in FF-OCM
2.1 Required characteristics
The light source is a key element in FF-OCM. Various characteristics of the light source, including spectral properties, radiance, and spatial coherence, have a direct impact on the imaging performance of FF-OCM.
The spectral properties of the illumination determine the imaging spatial resolution in both axial and transverse directions. The axial resolution (the sectioning ability) is usually governed by the temporal coherence function, which is the Fourier transform of the spectral power distribution (SPD) of the light source. Therefore, a broad SPD results in a high axial resolution (i.e. narrow temporal coherence function). The transverse resolution is ultimately limited by diffraction, and therefore proportional to the mean wavelength of the SPD. Furthermore, the mean wavelength also determines the imaging penetration capability, depending on the spectral absorption and scattering of the sample to be imaged. In biological tissues, the penetration is maximized in the near infrared, between 650 nm and 1350 nm typically [9].
The radiance of the source determines the highest achievable acquisition speed while maintaining an optimal detection sensitivity. It has been shown that in order to have the best possible sensitivity, the exposure time of the detector (camera) should be set so that its full-well capacity is almost completely filled [4,8,10]. Hence, the higher the radiance of the source is, the lower the exposure time needs to be, and therefore higher acquisition speeds can be achieved, as long as the camera frame rate can be increased accordingly.
In FF-OCM, it is also important that the light source has a low spatial coherence. As full-field imaging is performed, high spatial coherence would result in parasite interference patterns severely degrading the images [11,12]. This phenomenon, referred to as cross-talk, also happens in traditional bright field microscopy, and generally in all full-field imaging techniques using spatially coherent illumination. Even though cross-talk rejection has been investigated in order to be able to use spatially coherent sources in FF-OCM [13–16], low spatially coherent sources are still preferred, since they naturally eliminate cross-talk [17], without having to resort to any technique or apparatus.
2.2 Appropriate sources
Three main types of spontaneously-emitting light sources can satisfy the different properties stated above for efficient FF-OCM imaging: thermal sources (halogen lamps and arc lamps), fluorescence-based sources, and light-emitting diodes (LEDs).
Halogen lamps have been the most widely used sources up to now, and have proven to yield excellent images, with the highest axial resolution ever measured in FF-OCM [18]. They produce light simply by heating a tungsten filament (blackbody radiator), and are very cheap. Nevertheless, they do not have a long lifetime and have very low energy efficiencies, since they dissipate a lot of heat. Furthermore, their spectral emission is so wide that only a small fraction of it is actually “seen” by the detector and contributes to the actual axial resolution. Moreover, they emit light quasi-isotropically, so that only a fraction of it can be collected for illumination. Hence, halogen lamps consume a lot of energy to produce light from which only a fraction (spatial and spectral) is effectively used for imaging. Let us eventually note that pulse illumination, which can be useful in high-speed FF-OCM [19,20], is not straightforward using halogen lamps since they respond very slowly to a change in the supplied electrical power.
Arc lamps produce light by an electric arc induced between two electrodes through an ionized gas. Their optical spectrum is a combination of a thermal spectrum and spectral lines. The most appropriate type of arc lamp for FF-OCM uses Xenon as the ionized gas. It has a broad optical spectrum expanding towards near infrared, and therefore including the spectral region of interest for maximum depth of penetration in tissue. Nevertheless, the spectrum exhibits many spikes (spectral lines) in that region [10,21], which result in side lobes in the coherence function, and therefore may create artefacts in the images. Furthermore, the spectrum is so wide that a large region of it is useless for biomedical imaging, especially the part extending in the near ultraviolet. Besides, like halogen lamps, arc lamps have a short lifetime, dissipate heat, and emit light quasi-isotropically, resulting in low energy efficiencies. Nevertheless, as the cloud of plasma generating light is very small (pinpoint-sized), arc lamps can have a higher radiance than halogen lamps, resulting in a potentially faster acquisition compared to a halogen lamp. Furthermore, the small size of the plasma makes it easy to inject light from an arc lamp into a multimode optical fiber [10,21]. Arc lamps are also particularly well suited for short pulse emission [19,20], but their unstability should be taken into account for proper image acquisition.
Fluorescence-based sources rely on the spontaneous emission of light by a material that has absorbed light at a shorter wavelength. Depending on the nature of the material, light emitted by fluorescence may have a low temporal coherence. Titanium-doped Sapphire (Ti:Al2O3) has the broadest fluorescence emission spectrum of any known material, from 660 nm to 1100 nm. The peak of its absorption band being at 490 nm, it can be excited with a variety of laser sources emitting green light. A broadband fluorescence-based light source, based on a Ti:Al2O3 crystal and a frequency-doubled neodymium vanadate (Nd:YVO4) excitation laser was specifically developed for FF-OCM [22]. This type of source has the advantage of having a very smooth optical spectrum, and is particularly appropriate for high-speed, pulsed-illumination imaging. Nevertheless, only a small fraction of the excitation laser power is actually converted into fluorescence power, resulting in a need for powerful excitation laser sources. Another disadvantage of such fluorescence-based sources is their substantial cost.
A LED is made of a combination of semiconducting materials that are doped with impurities to allow conversion of electrical energy into light through a reversible process called injection electroluminescence. The wavelength of generated light is determined by the energy bandgap at the p-n junction in the semiconductor. LEDs are very attractive sources as they are relatively cheap, very compact, consume little power, have a long lifetime, are comfortable in use and maintenance, and are suitable for high-speed modulation. Furthermore, they have high energy efficiencies, since they do not dissipate heat, do not have very broad optical spectra compared to thermal sources, and emit light over a limited solid angle, so that it is possible to collect most of the light they emit for illumination. LEDs have been used in FF-OCM for various applications [23–26], giving axial resolutions (measured in air) ranging from 10 μm [23] down to 1.5 μm [26], the latter being however obtained using a LED whose optical spectrum was centered outside the near infrared window, which is not optimal for in-depth imaging in biological tissues. Let us note that efforts have been made to optimize the temporal coherence function of LED sources for OCT by coupling several LEDs [27,28], reaching a resolution of 2.2 μm (in water) in a FF-OCM setup combining 5 different LEDs [29]. Let us also note that LEDs have proven to be well appropriate for full color FF-OCM [30,31]. Up to now, LEDs were unsuitable for ultrahigh-resolution FF-OCM in biological tissues because broadband LEDs having an optical spectrum centered in the near infrared window were not available. Blue LEDs have now made it possible to commercially produce broadband sources based on optical excitation of phosphor by a LED, with a smooth optical spectrum spanning from 470 nm to 850 nm, which is a spectral range suitable concerning penetration within tissue. This type of source combines the advantages of LEDs with the broad spectra offered by fluorescence-based sources (since phosphor scintillation is analogous to fluorescence). This type of broadband LED source has been investigated for spectroscopic OCT [32], but, to the author’s knowledge, not for OCT imaging up to now.
3. LED-illumination high-resolution FF-OCM
The current paper reports on a high-resolution FF-OCM system using a single broadband LED. The performance of the system is compared with that of a conventional halogen-based FF-OCM system, since halogen lamps are in most cases the simplest and cheapest source producing high quality images, what made them the reference source for FF-OCM.
3.1 Experimental setup
The experimental setup is based on a Linnik interference microscope, the configuration that has been the most widely used in FF-OCM [4–8,10]. It consists of a Michelson interferometer with a microscope objective in each arm, providing high transverse resolution and magnification. Since very high transverse resolution and magnification generally result in small imaged fields, a compromise between a large enough field and a high enough transverse resolution is usually required. Here, water immersion objectives having a 10x magnification and 0.3 NA were used, combined with a tube lens of 200 mm focal length and a Photonfocus MV1-D1024E-160-CL camera, having a 10.9 mm × 10.9 mm charge complementary oxide semiconductor (CMOS) sensor (1024 × 1024 pixels), resulting in a 912 μm × 912 μm imaging field on the sample.
The light source is a broadband LED (Thorlabs MBB1L3, output power 70 mW, spectral width 280 nm, center wavelength 650 nm). A 100 W halogen lamp is used as the FF-OCM reference source. Both sources are used in a Köhler configuration. Figure 1 represents the SPD of the LED given by the manufacturer along with the spectrum of a 3000 K blackbody representing the halogen lamp. The two SPDs are modulated by the spectral response of the camera, provided by the manufacturer, resulting in spectral widths (full-width at half-maximum) Δλ of 225 nm for the LED and 330 nm for the halogen lamp. The center wavelength λ0 is 630 nm for the LED, whereas it is 700 nm for the halogen lamp.
Fig. 1 Spectral power distribution (SPD) of the LED and the halogen lamp, modulated by the spectral response of the camera. LED: λ0 = 630 nm, Δλ = 225 nm. Halogen lamp: λ0 = 700 nm, Δλ = 330 nm.
3.2 System characterization
In order to measure the axial resolution of the system, it was first necessary to obtain its “raw” interferometric axial response. This response is the interferogram obtained by axially scanning a perfect planar reflector (identical to the reference planar reflector) in the object arm of the Linnik interferometer. Figure 2 shows the axial interferometric responses of the system measured for LED and halogen illumination. The parameter z refers to the axial position of the scanned reflector, z = 0 corresponding to a zero optical path difference in the interferometer.
For the system to acquire reflectivity-based tomographic images, the raw interferometric data needs to be demodulated. In order to do so, several interferometric images are recorded with a given phase-shift between consecutive images, and an algebric combination of them is applied [8]. The axial response of the FF-OCM system corresponds to the squared envelope of the interferograms presented in Fig. 2, since reflectivity is proportional to the squared amplitude of the interferometric data [4]. The axial resolution is then given by the full-width at half maximum (FWHM) of the axial response. Theoretically, considering the spectral widths and center wavelengths of the SPDs given in Fig. 1, an identical axial resolution of ∼0.4 μm is obtained for the two sources when considering water-immersion.
Figure 3 shows the measured linear and logarithmic axial responses of the system for LED and halogen illumination. An identical axial resolution of 0.7 μm was measured for both sources. The discrepancy between theory and experiment is most likely due to residual dispersion mismatch between the two arms of the interferometer, which is critical when working at very high axial resolution, and also possibly to SPD changes induced by the non-flat spectral transmission of all the optical elements of the system, including the microscope objectives. It should be noted that the two responses do not have the same general shape, the one of the system illuminated by the halogen lamp dropping to zero quicker than the response for LED illumination. This can degrade the axial resolution for LED illumination, especially if dynamic range compression is applied [8]. Nevertheless, the presence of side lobes also degrades resolution, in a similar fashion for the two types of illumination, eventually making the difference in shape of the central peak negligible, and resulting in two similar logarithmically compressed axial responses, as shown in Fig. 3.
Fig. 3 Comparison of the measured axial responses of the system with LED and halogen illumination, on linear scale (left) and logarithmic scale (right).
The transverse resolution of the system was measured by imaging a high-contrast edge, and recording an intensity profile across it. This yields the edge response of the system, which is the convolution of a perfect edge by the transverse point spread function (PSF) of the system. The 20-80% width of the edge response directly gives the transverse resolution of the system [29]. It was measured to be 1.8 μm for the LED and 2 μm for the halogen lamp. Therefore the transverse resolution is slightly better for the LED, as expected considering the 20% difference between the mean wavelengths.
Another parameter of importance is the detection sensitivity, which corresponds to the minimum detectable reflectivity, usually expressed on a logarithmic scale. It is determined by the well-charge storage capability of the camera, the ability of the source to saturate it, and also the quality of the illumination (i.e. quantity of parasite (incoherent) light seen by the camera) [4,8]. The sensitivity can also be improved by summing several interferometric images at the same position before computing the en-face image, at the cost of an acquisition time multiplied by the number of images accumulated. For N images accumulated, the sensitivity is S + 10log10(N), S being the sensitivity with no accumulation (N = 1) [8]. In order to compromise between acquisition speed and sensitivity, ten images were accumulated. Above that number, considering the shape of the logarithm function, the increase in acquisition time becomes very significative for only small gain in sensitivity [8]. The sensitivity with ten accumulated images was measured to be 70 dB for both sources, which corresponds to the absolute value of the background noise level of the logarithmically compressed axial responses shown in Fig. 3. It is consistent with the value of 68 dB obtained theoretically when carrying out a noise analysis analogous to the one presented in [4].
Furthermore, it should be noted that the camera could not be used at its highest frame rate (150 frames per second), because the LED could not saturate it at that speed. At full power, the maximum frame rate accessible with LED illumination was 60 frames per second, with a 10 ms exposure time. Therefore, it was not possible to perform a faster acquisition while maintaining a 70 dB sensitivity, when using this broadband LED. It should be noted that the power of the halogen lamp was sufficient for using the camera at maximum frame rate. Nevertheless, the power of the halogen lamp was reduced in order to use the camera in the same conditions when using the LED or the halogen lamp, for a relevant comparison.
It is worth noting that LEDs with higher output powers are commercially available and it is very likely that the broadband LED we used will soon be available with a higher output power, enabling faster image acquisition.
4. Demonstration of cellular-level imaging
The performance of the setup for biomedical applications has been investigated by imaging an ex-vivo Xenopus laevis tadpole and healthy human skin excised in the region of the neck. The samples were placed under a cover glass, immersed in a liquid matching the refractive index of the cover glass in order to attenuate the reflection of light upon them. An identical cover glass was placed in the reference arm of the Linnik interferometer for dispersion mismatch compensation. A first acquisition was done using LED illumination, immediately followed by a second one with halogen illumination, in order to obtain stacks of en-face images at the exact same locations within the samples. A logarithmic dynamic range compression was then applied, along with a one pixel Gaussian blur for noise reduction.
4.1 Xenopus laevis
Xenopus laevis tadpoles have been widely studied in OCT [8,10,33] and can be considered as standard samples. They are transparent (i.e. weakly scattering and absorbing) and mainly constituted of water, thus introducing little dispersion if water-immersed objectives are used. Therefore, it is possible to image deeply in those samples. Futhermore, they exhibit relatively large cells with thin membranes that can illustrate the imaging resolution capability of OCT.
An ex-vivo Xenopus laevis tadpole was imaged with our FF-OCM system. A stack of 1000 en-face (x × y) tomographic images (each of 1024 × 1024 pixels) was acquired with each light source, resulting in two volumes of 912 × 912 × 300μm3 (x × y × z). Figure 4 shows en-face FF-OCM images of the tadpole at a 40 μm depth. Mesenchymal stem cells are clearly identifiable, along with their nuclei. The strong backscattering structures around the zone including the mesenchymal cells are neural crest melanocytes. The images obtained with both sources are very similar.
Fig. 4 En-face FF-OCM images of a Xenopus laevis tadpole (ex-vivo) obtained with broadband LED illumination (left) and halogen illumination (right).
Figure 5 shows cross-sectional images of the tadpole, to illustrate the axial resolution and penetration depth reachable with both sources. Mesenchymal stem cells are well resolved, and a strong backscattering melanin layer is visible right above the cell layer. There is no observable difference in resolution with LED or halogen illumination. The penetration depth is also similar, due to the fact that the tadpole is transparent, and the small difference of center wavelength between the SPDs of the two sources does not affect penetration.
Fig. 5 Cross-sectional FF-OCM images of a Xenopus laevis tadpole (ex-vivo) obtained with broadband LED illumination (top) and halogen illumination (bottom).
4.2 Human skin
For the human neck-skin sample, the images were acquired according to the same procedure as for the images of the tadpole, except that the stacks were smaller in the z direction in order to image only up to 200 μm in depth, since no more interesting features could be seen deeper than 200 μm. Figures 6 shows en-face images of the epidermis at 30 μm in depth, obtained with the two sources. Cell nuclei are visible, along with thin features of the stratum corneum and a hair bulb. The images are of the same quality using the broadband LED or the halogen lamp. The exact same structures can be identified with both sources.
Fig. 6 En-face FF-OCM image of human skin (ex-vivo) obtained with broadband LED illumination (left) and halogen illumination (right).
Figure 7 shows cross-sectional (y × z) images of the sample. The image processing was the same as before, except that eight adjacent images were projected, in order to exhibit weakly reflecting in-depth structures. Note that due to this projection, the cell nuclei become blurred, nevertheless epidermis and a significant part of the dermis are then clearly identifiable. The in-depth architecture of a hair bulb is visible. It can be noticed that features within the dermis are better resolved using the halogen lamp than the LED. This is most likely due to light scattering being more significant in the case of LED illumination, since the mean wavelength of the SPD of the LED is lower than the mean wavelength of the SPD of the halogen lamp. Nevertheless the difference in penetration is not critical. Note that in Fig. 7, the reflection of light on the cover glass is very weak due to the index matching liquid. Nevertheless, in several small zones, the liquid was not properly immersing the sample, resulting in a shadow artifact in the images due to the strong reflection.
Fig. 7 Cross-sectional FF-OCM images of human skin (ex-vivo) obtained with broadband LED illumination (top) and halogen illumination (bottom).
5. Conclusion
For the first time, a FF-OCM setup with LED illumination having the same performance in terms of spatial resolution and sensitivity as FF-OCM with halogen illumination, was demonstrated. Using a broadband LED, an axial resolution of 0.7 μm (measured in water) was reached, identical to the one obtained with a halogen lamp using the same camera. Images of a Xenopus laevis tadpole and ex-vivo human skin validated that LED illumination could replace halogen illumination in the context of biological imaging. The only drawback to this LED-based FF-OCM setup is acquisition speed. The power of the LED was insufficient to use the camera at its maximum frame rate while maintaining a high sensitivity, what rules out in-vivo imaging, at least using this camera. Nevertheless, very high brightness LEDs are starting to be commercially available and broadband LEDs such as the one used here but with higher outputs are most likely to be produced soon. Those could definitely replace halogen lamps in FF-OCM, considering the numerous advantages of LEDs, including cheapness, compactness, lifetime and energy efficiency, that already ruled out halogen lamps in most of today’s applications requiring white light.
Acknowledgment
The authors are grateful to Odile Bronchain and Albert Chesneau (Institut des Neurosciences Paris-Saclay) for providing the Xenopus laevis tadpole and to the company DAMAE medical for providing the human skin sample.
References and links
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). [CrossRef] [PubMed]
2. A. M. Zysk, F. T. Nguyen, A. L. Oldenburg, D. L. Marks, and S. A. Boppart, “Optical coherence tomography: a review of clinical development from bench to bedside,” J. Biomed. Opt. 12(5), 051403 (2007). [CrossRef] [PubMed]
3. E. Beaurepaire, A. C. Boccara, M. Lebec, L. Blanchot, and H. Saint-Jalmes, “Full-field optical coherence microscopy,” Opt. Lett. 23(4), 244–246 (1998). [CrossRef] [PubMed]
4. A. Dubois, L. Vabre, A. C. Boccara, and E. Beaurepaire, “High-resolution full-field optical coherence tomography with a Linnik microscope,” Appl. Opt. 41(4), 805–812 (2002). [CrossRef] [PubMed]
5. L. Vabre, A. Dubois, and A. C. Boccara, “Thermal-light full-field optical coherence tomography,” Opt. Lett. 27(7), 530–532 (2002). [CrossRef] [PubMed]
6. B. Laude, A. De Martino, B. Drévillon, L. Benattar, and L. Schwartz, “Full-field optical coherence tomography with thermal light,” Appl. Opt. 41(31), 6637–6645 (2002). [CrossRef] [PubMed]
7. A. Dubois, G. Moneron, K. Grieve, and A. C. Boccara, “Three-dimensional cellular-level imaging using full-field optical coherence tomography,” Phys. Med. Biol. 49(7), 1227–1234 (2004). [CrossRef] [PubMed]
8. A. Dubois, K. Grieve, G. Moneron, R. Lecaque, L. Vabre, and C. Boccara, “Ultrahigh-resolution full-field optical coherence tomography,” Appl. Opt. 43(14), 2874–2883 (2004). [CrossRef] [PubMed]
9. J. M. Schmitt, A. Knüttel, M. Yadlowsky, and M. A. Eckhaus, “Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering,” Phys. Med. Biol. 39(10), 1705–1720 (1994). [CrossRef] [PubMed]
10. W. Y. Oh, B. E. Bouma, N. Iftimia, S. H. Yun, R. Yelin, and G. J. Tearney, “Ultrahigh-resolution full-field optical coherence microscopy using InGaAs camera,” Opt. Express 14(2), 726–735 (2006). [CrossRef] [PubMed]
11. B. Karamata, P. Lambelet, M. Laubscher, M. Leutenegger, S. Bourquin, and T. Lasser, “Multiple scattering in optical coherence tomography. I. Investigation and modeling,” J. Opt. Soc. Am. A 22(7), 1369–1379 (2005). [CrossRef] [PubMed]
12. areB. Karamata, P. Lambelet, M. Leutenegger, M. Laubscher, S. Bourquin, and T. Lasser, “Multiple scattering in optical coherence tomography. II. Experimental and theoretical investigation of cross talk in wide-field optical coherence tomography,” J. Opt. Soc. Am. A 22(7), 1380–1388 (2005). [CrossRef] [PubMed]
13. B. Považay, A. Unterhuber, B. Hermann, H. Sattmann, H. Arthaber, and W. Drexler, “Full-field time-encoded frequency-domain optical coherence tomography,” Opt. Express 14(17), 7661–7669 (2006). [CrossRef] [PubMed]
14. A.-H. Dhalla, J. V. Migacz, and J. A. Izatt, “Crosstalk rejection in parallel optical coherence tomography using spatially incoherent illumination with partially coherent sources,” Opt. Lett. 35(13), 2305–2307 (2010). [CrossRef] [PubMed]
15. B. Heise, S. E. Schausberger, S. Häuser, B. Plank, D. Salaberger, E. Leiss-Holzinger, and D. Stifter, “Full-field optical coherence microscopy with a sub-nanosecond supercontinuum light source for material research,” Opt. Fiber Technol. 18(5), 403–410 (2012). [CrossRef]
16. A. Grebenyuk, A. Federici, V. Ryabukho, and A. Dubois, “Numerically focused full-field swept-source optical coherence microscopy with low spatial coherence illumination,” Appl. Opt. 53(8), 1697–1708 (2014). [CrossRef] [PubMed]
17. B. Karamata, P. Lambelet, M. Laubscher, R. P. Salathé, and T. Lasser, “Spatially incoherent illumination as a mechanism for cross-talk suppression in wide-field optical coherence tomography,” Opt. Lett. 29(7), 736–738 (2004). [CrossRef] [PubMed]
18. A. Federici and A. Dubois, “Full-field optical coherence microscopy with optimized ultrahigh spatial resolution,” Opt. Lett. 40(22), 5347–5350 (2015). [CrossRef] [PubMed]
19. K. Grieve, G. Moneron, A. Dubois, J.-F. Le Gargasson, and A. C. Boccara, “Ultrahigh-resolution ex-vivo ocular imaging using ultrashort acquisition time en-face optical coherence tomography,” J. Opt. A, Pure Appl. Opt. 7(8), 368–373 (2005). [CrossRef]
20. G. Moneron, A. C. Boccara, and A. Dubois, “Stroboscopic ultrahigh-resolution full-field optical coherence tomography,” Opt. Lett. 30(11), 1351–1353 (2005). [CrossRef] [PubMed]
21. K. Grieve, A. Dubois, M. Simonutti, M. Paques, J. Sahel, J. F. Le Gargasson, and C. Boccara, “In-vivo anterior segment imaging in the rat eye with high speed white light full-field optical coherence tomography,” Opt. Express 13(16), 6286–6295 (2005). [CrossRef] [PubMed]
22. D. Sacchet, M. Brzezinski, J. Moreau, P. Georges, and A. Dubois, “Motion artifact suppression in full-field optical coherence tomography,” Appl. Opt. 49(9), 1480–1488 (2010). [CrossRef] [PubMed]
23. E. Beaurepaire, A. C. Boccara, M. Lebec, L. Blanchot, and H. Saint-Jalmes, “Full-field optical coherence microscopy,” Opt. Lett. 23(4), 244–246 (1998). [CrossRef] [PubMed]
24. C. Li, J. A. Zeitler, Y. Dong, and Y. C. Shen, “Non-destructive evaluation of polymer coating structures on pharmaceutical pellets using full-field optical coherence tomography,” J. Pharm. Sci. 103(1), 161–166 (2014). [CrossRef] [PubMed]
25. R. Kimura, T. Iwai, and T. Tsugita, “Optical characterization of facial foundation applied to skin replicas by using visible FF OCT,” Proc. SPIE 9232, 92320X (2014).
26. E. Auksorius and A. C. Boccara, “Dark-field full-field optical coherence tomography,” Opt. Lett. 40(14), 3272–3275 (2015). [CrossRef] [PubMed]
27. J. M. Schmitt, S. L. Lee, and K. M. Yung, “An optical coherence microscope with enhanced resolving power in thick tissue,” Opt. Commun. 142(4-6), 203–207 (1997). [CrossRef]
28. Y. Zhang, M. Sato, and N. Tanno, “Resolution improvement in optical coherence tomography by optimal synthesis of light-emitting diodes,” Opt. Lett. 26(4), 205–207 (2001). [CrossRef] [PubMed]
29. D. Sacchet, J. Moreau, P. Georges, and A. Dubois, “Multi-band ultrahigh resolution full-field optical coherence tomography,” Proc. SPIE 7372, 73721F (2009). [CrossRef]
30. L. Yu and M. Kim, “Full-color three-dimensional microscopy by wide-field optical coherence tomography,” Opt. Express 12(26), 6632–6641 (2004). [CrossRef] [PubMed]
31. B.-W. Yang, Y.-Y. Wang, Y.-M. Lin, Y.-S. Juan, H.-T. Chen, and S.-P. Ying, “Applying RGB LED in full-field optical coherence tomography for real-time full-color tissue imaging,” Appl. Opt. 53(22), E56–E60 (2014). [CrossRef] [PubMed]
32. H. C. Cheng and C. K. Sun, “WLED-Based Low Coherence Interferometry in the Visible Wavelength Range,” J. Med. Biol. Eng. 27, 173–176 (2007).
33. W. Drexler, U. Morgner, F. X. Kärtner, C. Pitris, S. A. Boppart, X. D. Li, E. P. Ippen, and J. G. Fujimoto, “In-vivo ultrahigh-resolution optical coherence tomography,” Opt. Lett. 24(17), 1221–1223 (1999). [CrossRef] [PubMed]
