Optical coherence tomography (OCT) is an emerging biomedical optical imaging technique that can perform high-resolution, cross-sectional imaging in biological tissues.1, 2 OCT was first applied in ophthalmology for in vivo retinal imaging3, 4 and has proven to be useful for the diagnosis of macular and optic disk diseases.5, 6 Ultrahigh resolution OCT has also been demonstrated for cellular imaging using a state-of-the-art femtosecond laser or supercontinium light source. 7, 8, 9, 10 In recent years, the progress of OCT technology utilizing a spectrometer-based OCT or swept-source-based OCT has gained much attention in ophthalmology 11, 12, 13, 14, 15, 16, 17 and other biological tissue imaging.18, 19
Full-field optical coherence tomography (FF-OCT)20, 21, 22 is known as a nonscanning approach to OCT, where the detected horizontal cross-sectional image is of the same form as that obtained in the en face 23 or transverse scanning OCT.24 FF-OCT is based on an interference microscope, where a parallel beam is illuminated to the sample and the backscattered light is detected using a two-dimensional (2-D) sensor array (e.g., CCD or CMOS camera). The sectioning capability (axial resolution) of FF-OCT is determined by the coherence length of the light source, while the transverse resolution is mainly determined by the magnification of the imaging system. Since the light source is employed to illuminate the sample, a spatially incoherent light source such as a halogen lamp or xenon arc lamp is well suited in FF-OCT to achieve both high axial and high transverse resolutions. The use of a spatially incoherent source also offers the advantage of cross-talk suppression.25
An FF-OCT system using a thermal light source has been demonstrated by Vabre for ultrahigh resolution imaging.26 A three-dimensional (3-D) image of a Xenopus laevis tadpole was reconstructed using a stack of FF-OCT images with unprecedented resolutions. However, the reported imaging speed is relatively slow ( per image), due to the practical requirement of image averaging to improve the detection sensitivity. Recently, Grieve have developed a rapid FF-OCT system associated with a high-speed CMOS camera, enabling in vivo anterior segment imaging of small animals.27 More recently, Moneron have developed a stroboscopic FF-OCT system, in which image acquisition time has been dramatically reduced to by using a xenon flash lamp and a pair of CCD cameras.28 FF-OCT has been expected for high-resolution 3-D imaging, in which the images acquired are comparable to those of confocal microscopy.29, 30
So far we have developed a video-rate FF-OCT imaging system using a dual-channel parallel heterodyne detection technique with a pair of CCD cameras.31 Three-dimensional imaging was performed in a single longitudinal scan, making imaging possible. However, the strict requirement of frequency stability in synchronous detection is apparently a drawback for an in vivo imaging application. Sample motion may adversely affect the frequency stability, lowering the signal-to-noise ratio (SNR). Recently, we have built an ultra-high-resolution FF-OCT system using a thermal light source and a CCD camera. Investigations of the ocular tissue structure of a donor cornea and a porcine eye at the subcellular level have been carried out.32, 33 In this setup, however, a phase-shift-based detection technique was employed, and the measurement time was relatively lengthy, making it difficult for real-time imaging. In this paper, we describe a novel dual-channel FF-OCT system for ultrahigh-resolution and high-speed imaging. We demonstrate what is to our best knowledge the first in vivo video-rate cellular-level imaging result of blood cell dynamics of a Xenopus laevis tadpole by FF-OCT.
A schematic of our FF-OCT system is shown in Fig. 1 . The system is based on a white light interference microscope with a Linnik-type configuration. The radiation from a 150-W tungsten halogen lamp is incidented into a flexible fiber bundle (not shown), and it is conducted to a Köhler illumination (KI) system that consists of four aspheric lenses and two variable apertures. The KI system is intended to be used for a uniform illumination to the sample, as in a conventional microscope. The output quasi-parallel beam from the KI system is launched into the Michelson interferometer and then split into the signal and reference beams by a 50:50 beamsplitter. Two identical infinity-corrected water immersion microscope objectives ( , , LUMPLFL40XW/IR2, Olympus, Tokyo) with a working distance of are placed in both arms of the interferometer. An achromatic quarter wave plate (QWP) and a linear polarizer are inserted in the reference arm. The light beam reflected back by a reference mirror is again passed through the linear polarizer and the QWP so that the light beam returning to the BS becomes a circularly polarized light. A drop of water (W3500 cell cultured water, Sigma-Ardorich, St. Louis, Missouri) is placed as an immersion fluid between the tip of the objective and the total reflection mirror. A neutral density (ND) filter whose transmittance is experimentally optimized to 13% is set in the reference arm to increase the visibility of the interferometric image. On the other hand, the sample is illuminated with a randomly polarized light. To compensate for a residual dispersion mismatch between the signal and the reference arms, a BK7 glass plate is placed in the reference arm so that the dispersion is initially matched. The recombined output beam from the interferometer is detected by the microscope setup, where the interference light is divided into two orthogonal parts by a polarization beamsplitter (PBS) before reaching the two identical CCD cameras (MC-512PF, Texas Instruments, Tokyo). The CCD cameras employed have pixels with a 12-bit resolution at an output rate of .
Provided that the sample exhibits no birefringence, the dual CCD outputs exhibit a phase difference of and can be expressed as follows:and are the signal and reference intensities, respectively, is a backscattering component that does not contribute to the interference signal, is an initial phase difference between the signal and the reference lights, and is a numerical factor. The dual CCD outputs contain a common background of , while their respective interferometric terms and correspond to the sine and cosine components.
To extract the sine and cosine components from the CCD outputs, a subtraction of the background component is needed. In the present setup, we measure an additional pair of images and with a phase shift of by using a piezoelectric transducer (PZT) that is glued to the reference mirror. An FF-OCT image is then derived by summing the squares of the sine and cosine components of the interference signal as follows:is a numerical factor determined by the ratio of the amount of light intensity incidented on the CCD cameras. The value of is measured in advance before the experiment. In this manner, two consecutive frames are recorded to derive a single FF-OCT image. The FF-OCT image is displayed using a logarithmic lookup table with a 256 gray scale, where a high reflectance is displayed as white against a black background. It is noteworthy that the frame subtraction in the present setup offers the advantage that the fixed pattern noise in the CCD camera can be removed when two consecutive frames with a phase difference of are subtracted. Functionally, the detected four frames in the present system are equivalent to those acquired in the previously reported four-frame phase shift detection scheme.32
To synchronize the dual-detection channels, common pulse signals are applied to the CCD cameras through a programmable camera control line in the camera link format. The alternative voltage changes applied to the PZT, which correspond to phase shifts of 0 and , are triggered by the frame-sync signal from one CCD camera so that every two consecutive CCD frames exhibit a relative phase difference of in the interference image.
The validity of Eq. 3 requires that the two CCD cameras capture exactly the same field. In other words, a pixel-to-pixel correspondence between the CCD cameras is needed. Mechanically, one camera is stationary, while the other camera that is mounted on a xyz translation stage is manually aligned to its counterpart with a subpixel accuracy. The alignment employs either home-written template matching or image-correlation software. This calibration is done only once in the initial setting of the system.
Image Acquisition Software
We wrote a software program that performs three main functions: monitor mode for real-time observation, time-lapse mode for 2-D video-rate imaging, and z-stack mode for 3-D imaging. In monitor mode, a software sequence including image acquisition, processing, and display is repeated at a 5-Hz rate. Second, in time-lapse mode, FF-OCT images are continuously detected at a fixed depth as a function of time to achieve a time-sequence image. Assuming that the consecutive frames with a phase difference of are , , , and , the frame subtractions of , may effectively remove the background component to derive the sequential FF-OCT images using Eq. 3. Using such a rolling manner, video-rate imaging is made possible. To avoid the possible fringe averaging during in vivo imaging, the exposure time of the cameras is set to , while the frame interval is fixed at so that a single FF-OCT image can be acquired as short as . Third, in z-stack mode, the sample is placed in a motorized z-translation stage (ALV-600-H0M, Chuo Precision Industrial, Tokyo) and is translated along the optical axis to yield a stack of FF-OCT images. In this mode, the exposure time is set to . Using such a 3-D data set, a longitudinal cross section (xz- or yz-), which is of the common form of the conventional OCT tomogram, can be sectioned out by software. Illumination flux is also adjusted so that the camera pixels are close to the saturation level in both imaging modes.
Although a half-micron axial resolution can be achieved by fully exploiting the extremely broad bandwidth of the halogen lamp, only a partial band in the near infrared ranging from to has been chosen. Optical filters are inserted at the exit of the KI system to cut off the visible and long wavelength components. To avoid any thermal injuries of the sample, a short pass filter (passband of ) is inserted as well. This wavelength region has been chosen by considering the weaker absorption and better penetration in most of the biological tissues, and also the wavelength dependency of the polarization optics employed. To reduce unwanted reflections, all optical components are antireflection coated for the wavelength range employed. The resultant spectrum is shown in Fig. 2a . The spectrum was measured at the exit of the interferometer by blocking the sample arm so that it contains a transmittance dependency of the overall optics. In the present study, a spectrally filtered source having a full-width at half maximum (FWHM) of centered around was employed, yielding a theoretical axial resolution of in water. It should be noted that the effective spectrum for imaging is a product of the source spectrum and the spectral response of the CCD chip. Figure 2b shows the axial response profile of one pixel measured by scanning a total reflection mirror along the optical axis. As seen in Fig. 2b, the axial point spread function detected by FF-OCT exhibits a symmetrical form, with negligible sidelobes. The measured value of (FWHM) is slightly better than the theoretical value, mainly because the spectrum is slightly different from the Gaussian distribution.
The theoretical transverse resolution ( , ) is determined by the well-known formula . An 0.8-NA objective employed yields at the wavelength of , while we experimentally confirmed a transverse resolution of using the edge response (10 to 90%) of a silicon wafer as a sample. In tissue imaging, however, aberration may degrade the transverse resolution when imaging at a deeper position due to multiple scattering.
The theoretical detection sensitivity for FF-OCT is described in the literature.20, 34 We followed the equation by considering a four-frame phase shift detection technique. A full-well capacity of the camera is an important factor to achieve a high sensitivity measurement. The CCD camera employed has a full-well capacity of 400,000 electrons, and the theoretical detection sensitivity is calculated to be . The measured detection sensitivity was approximately . Unwanted back-reflection from the optical components in the FF-OCT setup and an uneven ploarization splitting ratio by the PBS may account for a sensitivity loss.
A typical FF-OCT image acquired by our system consists of pixels covering an area of . The illumination flux to the biological tissue is approximately . In the measerement, where the exposure time of the CCDs was shortened to , the incident power was adequetely increased.
A Xenopus laevis (African frog) tadpole of stage 40 to 45 was chosen for the present study. Xenopus laevis is a widely used and well-characterized developmental biology animal model and is commonly used in OCT measurement to demonstrate the visualization of the morphological structure 35, 36, 37, 38 as well as the cardiovascular activity.39, 40
Xenopus laevis tadpoles were bred in an aquarium tank at 23 to until measurements were performed. The first group of specimens were anesthetized by immersion in diluted ketamine solution for until they no longer responded to touch. The second group of specimens were fixed in 4% buffered solution of formaldehyde for an hour until no cardiac activity was observed. The tadpoles were transferred to a Petri dish and then submerged in water at room temperature. Specimens were oriented for imaging with the optical beam incident from either the dorsal or ventral sides. No staining and contrast agents were used. All animal handling was performed according to protocols approved by the Committee on Animal Care, Yamagata Promotional Organization for Industrial Technology.
To demonstrate the video-rate imaging capability of our FF-OCT system, Fig. 3 shows examples of the FF-OCT images of an anesthetized tadpole at approximately 0.3-s time interval. Figures 3a and 3d are detected where the speed of the blood flow is minimum. It can be seen from these images that individual blood cells are visible as well as the cellular structure of the tadpole. Time-lapse imaging results can be seen in 1Video 1 . The movie shows a total of 300 FF-OCT images recorded during a period of . Outputs from the CCD cameras were continuously captured and stored in the computer main memory for post-processing. FF-OCT imaging was performed with the tadpole ventral side up, and the time-lapsed images were taken near the exit of the aorta. In the movie, the pulsatile blood cells are clearly visualized. It is noteworthy that both the cell nuclei and the blood flow can be observed in a single image, where the expansion and contraction of a blood vessel can be seen in accordance with a pulsation of the blood flow. It is estimated from the movie clip that the pulsation occurs at a rate of . The imaging speed and the ultrahigh spatial resolution of our system make it possible to identify most of the individual blood cells during diastole of the heart. During systole, however, the blood cells move beyond the temporal resolution of the present system, so that the images of blood cells appear to be blurred [Fig. 3b] and, in the severe case, averaged out [Fig. 3c].10.1117/1.2822159.1
In the feasibility study of FF-OCT for ultrahigh-resolution 3-D morphological imaging, a series of FF-OCT images was acquired from the dorsal side to the ventral side of a fixed tadpole with a depth interval of . Four FF-OCT images were averaged at each depth position to increase the image contrast, and the measurement of 3-D volume involving FF-OCT imaging at 200 depths took approximately . The lower parts of Figs. 4a and 4b show the representative FF-OCT images at different depths of and , respectively, while the upper parts of (a) and (b) depict the longitudinal cross section reconstructed from the 3-D OCT volume. The cyan line indicates the depth where the FF-OCT image shown in the lower part of the figure was measured. Z-stack imaging results can be seen in 2Video 2 .10.1117/1.2822159.2
Features of the internal architectural morphology, such as cell membranes, boundaries of the cell cytoplasm, and cell nuclei, are distinctly visible from the FF-OCT images. It is interesting to note that inside the cell nuclei, a bright spot is always observed. Such bright spots at the individual cells are indicated by arrows in Fig. 4a. These bright spots might correspond to the nucleolus, and the fine fibers connected to the cell membrane might correspond to the cytoskeleton. A similar structure has also been reported in Ref. 38. Identification of the details of the other complicated internal morphology are under investigation. It can be seen from the movie clip that FF-OCT provides enriched information about cellular structure along the horizontal plane.
It is noteworthy that the present z-stack imaging started from a depth approximately below the sample surface. A longer-range yet more time consuming z-scan [Fig. 4c] reveals that the OCT images shown in Figs. 4a and 4b reflect the details of a second layer under the sample surface. In Fig. 4c, the broken line indicates the initial measurement depth of the z-scans in Figs. 4a and 4b. The results in Figs. 3 and 4 show some of the advantages of the present FF-OCT scheme over conventional OCT. FF-OCT is a nonscanning method capable of capturing an ultrahigh-resolution horizontal cross-sectional image that is constantly matched to the focal plane. Video-rate FF-OCT imaging offers the opportunity of continuous (time-lapsed) observation of cellular dynamics at a fixed depth. While speckles are prominent in conventional OCT and adversely affect the image contrast, they are minimal in Figs. 3 and 4, demonstrating another advantage of FF-OCT using a spatially incoherent thermal source.
Discussion and Conclusions
An FF-OCT system using a newly developed dual-channel detection scheme has been applied to in vivo imaging at a cellular level. Although we have demonstrated the visualization of individual flowing blood cells in the vessel, further improvement in the imaging speed that permits the visualization of faster flowing individuals may yield practical applications in biomedical diagnosis. The imaging speed of the present system is primary limited by the CCD frame rate. To avoid motion artifact and the resultant fringe averaging, the exposure time of the camera has been set to , despite of a frame interval of . Obviously, the “on-off” duty ratio of the CCD camera is merely 1:5. Therefore, the use of a CCD camera with five times higher frame rate may yield up to five times higher imaging speed without compromising the exposure time. The development of a higher-speed FF-OCT system using high-speed CCD cameras is underway in our laboratory.
In our detection method, the sine and cosine components of the interference signal are extracted to form a FF-OCT image. Although a more straightforward method to obtain the signal intensity by differentiating two phase-opposed interference images has been reported by Moneron, 28 the uncertainty factor of , where is an initial phase between signal and reference lights, could be multiplied to the final OCT image, making the FF-OCT image low contrast and fluctuating point by point. As such, the present detection scheme offers a practical advantage of imaging stability in biological tissue imaging.
The depth of focus (DOF), which is also recognized as a confocal parameter, is inversely proportional to the NA of the objective, . Using an objective with and , it is calculated that , which is nearly equal to the OCT axial resolution given by the coherence length. In FF-OCT imaging, an optimal sensitivity is achieved when the coherence-gated detection plane is matched to the focal plane of the microscope. Therefore, care must be taken to maintain a matching between the two planes when imaging at a deeper position. In tissue imaging, however, the mismatch of refractive indexes of the tissue sample and water may cause the focal plane to move away from the coherence-gated plane, so the sensitivity could be significantly degraded. As a solution, dynamic focusing techniques have been proposed.41, 42 In our measurement, the average refractive index of the Xenopus specimen, which is assumed to be 1.35 according to Tearney, 43 is close to that of water (1.33). Therefore, dynamic focusing has not been applied to our water immersion FF-OCT system.
In the presence of birefrigence in the tissue sample, the intensities of the two orthogonal parts of the backscattering light will become uneven, yielding an undesired image artifact in the present detection scheme. Meanwhile, the scattering of light is a dominant factor that limits the imaging depth of OCT. The lower sensitivity of FF-OCT as compared to the conventional (longitudinal) OCT may limit its application to a lesser depth. However, it has been demonstrated in the present work that ultrahigh-resolution FF-OCT is capable of providing enriched information about the morphological structure at a cellular level. Since scattering is less severe at a longer wavelength, FF-OCT system operated at the longer wavelength may have the advantage of deeper penetration. For this purpose, CCD cameras sensitive at and beyond can be employed for FF-OCT imaging.34, 37, 42
In conclusion, we have developed an ultrahigh-resolution FF-OCT system using a low-cost thermal light source incorporated with a pair of CCD cameras. The system has been applied to in vivo imaging of living specimens and demonstrated to be capable of visualizing the individual cells, including blood cell dynamics. With a spatial resolution of , the present system may be further developed for the use as an “optical histology,” and it may potentially contribute to investigations of developmental biology and cell interaction as well. The use of a contrast-enhanced material such as microspheres to change the scattering characteristics and to label a single cell may also allow the identification of cells and lead to functional cellular imaging in vivo.
The authors thank Yasufumi Fukuma, Topcon Corporation, Japan, for providing helpful advice. Helpful discussions with Dr. Yoshiaki Yasuno, University of Tsukuba, Japan, are gratefully acknowledged. This research is partially supported by the New Energy and Industrial Technology Development Organization (NEDO), Japan.