Optical coherence tomography (OCT) is a widely used imaging technique in retina research, with the spectrometer being a crucial component that determines the performance of spectral domain OCT (SD-OCT). While there are commercial spectrometers and systems available with a variety of options, they are often expensive and not customizable for specific light sources and applications. Thus, independently developing spectrometers and OCT systems could provide a better alternative. The calibration of a spectrometer is typically complex because it requires a standard light source, such as a mercury lamp, that must meet the specific requirements for calibration, including accurate spectral characteristics. Additionally, use of such a light source demands certain technical and operational expertise. Therefore, this paper proposes a practical calibration method for an OCT spectrometer based on a common OCT algorithm. As a result, the need for a standard light source is eliminated, and hence OCT spectrometer calibration is simpler and easier.

In this study, an SD-OCT system was built, incorporating a supercontinuum laser as the laser source. The corresponding wavelength range is 800?950 nm using filters. The low coherent light emitted by the laser is split into two beams through a fiber coupler. Each beam enters the sample arm and the reference arm, respectively. In the sample arm, the light passes through a two-dimensional galvanometer, generating a scanning beam on the mouse retina in this work. The power of the beam at the mouse pupil was approximately 600 μW, with the beam diameter of 0.93 mm. To minimize chromatic aberrations, the lenses used in both the sample arm and reference arm were paired appropriately. The reflected beams from the sample arm and the reference arm combine and interfere on a custom-built spectrometer. The spectrometer includes a transmission grating, a line CCD camera, and other optical devices. For spectrometer calibration, a mirror is placed at the retina plane, reflecting light back to the spectrometer similar to the reference arm setup. To achieve accurate calibration and performance analysis, the optical power from both arms was adjusted using irises to achieve similar intensity. The interference fringes at different imaging depths are then captured by the camera, facilitating subsequent calibration procedures. The calibration process involves synchronously optimizing the peak value and full width at half maximum of specular reflections collected at these depth positions via manual tuning of difference parameters. Finally, OCT imaging experiments on ten mice were conducted to validate the performance of the spectrometer.

The quantitative analysis results of the spectrometer are presented in Fig. 5. Figure 5(a) shows the spectral curve of the light source directly measured by the spectrometer. In Fig. 5(b), the k-value linearization curve is displayed. The spectral data obtained after Fourier transform is shown in Fig. 5(c), with the peak range of 127 dB?104 dB. To determine the corresponding relationship between CCD camera pixels and spatial distance in A-scan, the position of the peak in Fig.5(c) was extracted and correlated with the actual moving distance of the displacement platform. The calculated relationship between pixels and actual spatial distance was determined as 2.65 μm/pixel in the air, as depicted in Fig.5(d). The spectral data in Fig.5(c) was further Gaussian fitted and multiplied by the above obtained relationship to determine the maximum and minimum axial resolutions in the air of the system, which are 4.14 and 2.72 μm, respectively. The axial resolution change curve remains relatively stable within the imaging range, as demonstrated in Fig.5(e). Additionally, the sensitivity change curve [Fig.5(f)] was realized by connecting the data peaks in Fig.5(c) with a polygonal line. To evaluate the practical application of the spectrometer in mouse retina imaging, 1000 B-scan images were collected at the same position using the OCT system. Each B-scan comprised 1083 A-scans, with the A-scan rate of 100 kHz. The acquired image data were then aligned, averaged, and contrast-enhanced using ImageJ. The mouse retina OCT images are presented in Figs.6(b) and 6(c). To analyze the retina’s structure, the profile of each retinal layer was obtained by averaging the image in the horizontal direction, as depicted in Fig.6(d). Based on this profile, the thickness of each layer of tissue was measured. The comparison results for the thickness of each layer of the mouse retina are detailed in Table 2, demonstrating the successful implementation and performance of the spectrometer in mouse retina imaging. As a result, valuable insights are provided regarding the retinal structure, with potential application in further research investigations.

To address the demand for high-resolution imaging of the mouse retina in basic science research, a specific SD-OCT system was designed and constructed. The system is based on a customized broadband spectrometer. Herein, the design process of the spectrometer is introduced comprehensively, and an alternate optimization approach to its calibration is proposed based on a few key performance metrics. A notable advantage of this calibration approach is that accurate calibration of a spectrometer is achieved without relying on a standard light source. This streamlined process significantly simplifies the calibration procedure, making it more efficient and cost-effective. Overall, the method offers a practical and convenient solution for optimizing OCT systems. In conclusion, the SD-OCT system presented in this paper, with the custom broadband spectrometer and novel calibration approach, is a practical and convenient tool for achieving high-resolution imaging of the mouse retina in basic science research.