In this study we demonstrate ultrahigh-resolution spectral domain optical coherence tomography (UHR SD-OCT) with a linear-wavenumber (k) spectrometer, to accelerate signal processing and to display two-dimensional (2-D) images in real time. First, we performed a numerical simulation to find the optimal parameters for the linear-k spectrometer to achieve ultrahigh axial resolution, such as the number of grooves in a grating, the material for a dispersive prism, and the rotational angle between the grating and the dispersive prism. We found that a grating with 1200 grooves and an F2 equilateral prism at a rotational angle of 26.07°, in combination with a lens of focal length 85.1 mm, are suitable for UHR SD-OCT with the imaging depth range (limited by spectrometer resolution) set at 2.0 mm. As guided by the simulation results, we constructed the linear-k spectrometer needed to implement a UHR SD-OCT. The actual imaging depth range was measured to be approximately 2.1 mm, and axial resolution of 3.8 μm in air was achieved, corresponding to 2.8 μm in tissue (n = 1.35). The sensitivity was -91 dB with -10 dB roll-off at 1.5 mm depth. We demonstrated a 128.2 fps acquisition rate for OCT images with 800 lines/frame, by taking advantage of NVIDIA’s compute unified device architecture (CUDA) technology, which allowed for real-time signal processing compatible with the speed of the spectrometer’s data acquisition.
Optical coherence tomography (OCT) is a noninvasive
Ultrahigh axial resolution OCT (UHR-OCT), approaching 3 μm or less in tissue, makes it is possible to obtain images close to the level afforded by histology [6-8]. The axial resolution of OCT depends on the light source’s center wavelength, full width at half maximum (FWHM), spectrum shape, and so on. A broadband light source is often desirable for ultrahigh axial resolution in OCT because this resolution is inversely proportional to the FWHM of the light source [6-11]. Drexler
In SD-OCT, a spectral interferogram with a response function linear in the wavenumber (
In this paper, we demonstrate UHR SD-OCT based on a linear-
2.1. Linear Wavenumber Spectrometer
We used used an SLD (BLM2-D-840-B-I-10; Superlum, Ireland) with a FWHM (Δ
where
where
where
where
Figure 1(b) shows a schematic diagram of our UHR SD-OCT system. Light from the SLD was incident on a broadband 2×2 optical coupler with 50:50 ratio (Gould Fiber Optics, Millersville, MD, USA) and was split into the reference and sample arms. In the sample arm, the collimated light was incident upon samples via an OCT scan lens (LSM04-BB; Thorlabs Inc., Newton, NJ, USA) with an optical power of approximately 3.5 mW, forming a focal spot with 1/
Spectral data from the camera were digitized by a frame grabber (PCIe-1433, National Instruments Corp., Austin, TX, USA) with a resolution of 12 bits. Acquired spectral data were processed by a personal computer with a quad-core CPU (Intel Core i7-3370; Intel, Santa Clara, CA, USA) and a graphics card (WinFast GTX 680; Leadteck Research Inc., New Taipei City, Taiwan) based on a graphics processing unit (GPU) of the GeForce series (NVIDIA Corp., Santa Clara, CA, USA), which had 1536 stream processors at both a base clock of 1006 MHz and a boost clock of 1058 MHz, and memory of 4 Gbytes at a memory clock of 6008 MHz [15, 17]. To accelerate numerical calculations and display real-time 2-D images, we developed software using Microsoft Visual C++ and NVIDIA’s compute unified device architecture (CUDA) technology.
Preferentially, we should choose the imaging depth range of UHR SD-OCT to be 2.0 mm and 2.5 mm, to obtain ultrahigh axial resolution and slow decay of sensitivity (roll-off). The total pixel number of the camera was set to be 2048 pixels, and the physical pixel size in the sensor of the camera was 10 µm (width) × 10 μm (height). Then we calculated
Finally, from Eq. (3) we could estimate the focal length of the lens to be approximately 85.1 mm. We repeated the calculation while changing the imaging depth range, grating groove count, and prism angle. Table 1 shows the results of the calculations, with optimal material and rotation angle listed in red letters. As shown in Table 1, for a grating with 600 grooves the equilateral and isosceles Brewster prisms of all materials were suitable, because the standard deviation of the derivative (δdeg/δk) for every prism was low (< 0.02). In other words, the linearity of the linear-
[TABLE 1.] Calculated results to determine parameters for a linear-k spectrometer
Calculated results to determine parameters for a linear-k spectrometer
3.2. Evaluation of the Linear-k Spectrometer
To achieve an imaging depth range of 2.0 mm necessitates a lens with focal length of 85.1 mm, as shown in Table 1. In this study we used achromatic doublet lenses with focal lengths of 750 and 100 mm; the combination of these lenses could yield a focal length of 88.2 mm. Therefore, we could measure the imaging depth range as approximately 2.1 mm. We evaluated the linear-
[TABLE 2.] Wavelength and wavenumber of a fiber Bragg grating (FBG) array
Wavelength and wavenumber of a fiber Bragg grating (FBG) array
3.3. Performance of UHR SD-OCT
We used a -34 dB partially reflecting mirror as a sample to evaluate the system’s performance, including its sensitivity, sensitivity roll-off, and axial resolution. The sensitivity of our system, determined by adding the sample attenuation constant, was approximately 91 dB at near-zero depth, and a decrease of 10 dB was observed at approximately 1.5 mm, as shown in Fig. 4(a). The attenuated light with -30 dB in the reference arm was also used. Using a 10:90 optical coupler and an optical circulator, the sensitivity could reach up to approximately 94 dB. Figure 4(b) shows the point-spread function of the axial resolution when the mirror sample was positioned at a depth of approximately 0.9 mm. The measured axial resolution was 3.8 μm in air, corresponding to 2.8 μm in tissue (
[FIG 4.] (a) Sensitivity roll-off and (b) point-spread function for measurement of axial resolution.
To obtain a real-time cross-sectional OCT image, we used the multithreading technique of the CPU and parallel calculation technique of the GPU. First, we acquired and saved spectral interference signals in the memory by using the first thread of the CPU. Acquired interference signals were transferred to the memory of GPU using the second thread of the CPU, and the second thread also asked the GPU to calculate averaging, FFT, and log scaling. When the calculation was completed, the second thread transferred the calculated data from the memory of the GPU to the RAM. The third thread of the CPU was used to display OCT images, and to save BMP files and raw data. In the GPU, spectral interference signals were averaged in the lateral direction, to evaluate the level of the DC signal by averaging the whole spectrum. We subtracted the DC level from each spectral interference signal and performed FFT processing with each spectral interference signal without the DC component. Finally, a logarithmic scaling process was carried out to obtain an OCT image. Numerical
Figure 5 shows OCT images of a cherry tomato and human finger skin with 1024 × 800 pixels, corresponding to a physical size of 2.1 mm (deep) × 9.4 mm (lateral). In Fig. 5(a), membrane layers of small cells in the cherry tomato are clearly visible. In addition, we can see fine structures in the deep region of the finger skin in Fig. 5(b).
In this study we demonstrate UHR SD-OCT based on a linear-