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Image quality optimization of line-focused spectral domain optical coherence tomography with subsection dispersion compensation
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摘要:
本研究中搭建了一套用于生物样品成像的线聚焦谱域光学相干层析(LF-SD-OCT)系统，针对系统误差引起的轴向展宽和灵敏度衰减问题，提出了一种分段色散补偿的方法，对成像深度内的二阶和三阶色散相位进行了补偿，并通过平面镜和透明胶带样品成像实验来验证该方法的有效性和可靠性。通过本文提出的分段色散补偿方法和其他成熟方法的联合应用，系统在获得57.2 kHz的等效A-scan速率的同时，轴向分辨率提升至6.76 μm，对胶带样品2 mm深度范围内及水果样品0.3 mm深度范围内进行了较清晰成像。实验结果表明，该方法可以在兼顾处理效率的同时提高全深度轴向分辨率和灵敏度，未来有望广泛实现线聚焦谱域光学相干层析的生物医学成像应用。
Abstract:In this study, a line-focused spectral domain optical coherence tomography (LF-SD-OCT) system for imaging biological samples was built, and a data processing algorithm to improve the imaging quality was proposed to solve most of the problems of axial broadening and sensitivity attenuation caused by systematic errors. In particular, a segmented dispersion compensation method is proposed to compensate the second- and third-order dispersion phases in the imaging depth. The effectiveness and reliability of this method are verified by the imaging experiments of plane mirrors and scotch tape samples. Finally, it is proved that this method can improve the full-depth axial resolution and sensitivity without affecting the image processing speed. The final system can achieve the axial resolution of 6.76 μm and an equivalent A-scan rate of 57.2 kHz, and clearly image the tape sample within 2 mm depth and the apple sample within 0.3 mm depth. In the future, it is expected to widely realize the biological imaging applications of line-focused spectral domain optical coherence tomography.
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Overview: LF-SD-OCT was first proposed by Zuluaga and Kortum et al in 1999 and has since made significant advances in imaging speed and resolution. Compared with mainstream SD-OCT, LF-SD-OCT has advantages in system cost, imaging speed, and biosecurity, so it has a very good application prospect in areas with high imaging speed and security requirements, such as in vivo imaging. However, as the depth of LF-SD-OCT increases, the image quality deteriorates rapidly. At present, LF-SD-OCT has not been successfully applied in the biomedical field in China, and there are only a few successful cases in the international community.
In this study, we independently designed and built a line-focused spectral domain optical coherence tomography (LF-SD-OCT) system for imaging biological samples, and proposed data processing algorithms to improve imaging quality. We found that the dispersion parameters of different depth positions are quite different, and the unified dispersion compensation coefficient will lead to undercompensation or overcompensation in some regions. First, the curve of the dispersion compensation coefficient with depth is obtained by system calibration. Then, the original data is divided into four segments in the depth direction, and the dispersion compensation coefficient corresponding to the center position of each segment is used to compensate the second- and third-order dispersion phase in the segment. Subsequently, the segments are combined. The effectiveness and reliability of the proposed method are verified by using a flat mirror and scotch tape sample imaging. Finally, it is proved that the proposed method can improve the full-depth axial resolution and sensitivity while taking into account the image processing speed. After iterative wavelength distribution calibration, piecewise dispersion compensation, and deconvolution denoising, most of the axial broadening and sensitivity attenuation problems caused by systematic errors have been solved. The final system can achieve the axial resolution of 6.76 μm (theoretical value is 6.2 μm) and an equivalent A-scan rate of 57.2 kHz, and can image the tape sample within 2 mm depth and the apple sample within 0.3 mm depth.
This study has proved that line-focused spectral OCT has great advantages in imaging speed and development cost. In the future, further increase of light source power and appropriate improvement of the optical path can image human samples with weaker backscattered light, and it is expected that line-focused spectral OCT can be successfully applied to domestic biomedical imaging fields.
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图 4 重采样和色散补偿前后对比图。 (a)轴向分辨率对比图； (b)系统滚降对比图
Figure 4. Comparison before and after resampling and dispersion compensation. (a) Comparison of axial resolution; (b) Comparison of system roll down
图 6 色散补偿系数随深度变化图。 (a) 二次项系数； (b) 三次项系数
Figure 6. Diagram of dispersion compensation coefficient with depth. (a) Quadratic coefficient; (b) Cubic coefficient
图 7 针对性色散补偿前 (蓝)后 (红)系统滚降对比图
Figure 7. Comparison of system roll-down before (blue) and after (red) targeted dispersion compensation
图 8 分段色散补偿前 (蓝)后 (红)系统滚降对比图
Figure 8. Comparison of system roll-down before (blue) and after (red) subsection dispersion compensation
图 9 分段色散补偿前后成像结果对比图。 (a) Δz=1.25 mm； (b) Δz=1.65 mm； (c) Δz=2.05 mm； (d) Δz=2.45 mm
Figure 9. Comparison of imaging results before and after segmented dispersion compensation. (a) Δz=1.25 mm; (b) Δz=1.65 mm; (c) Δz=2.05 mm; (d) Δz=2.45 mm
图 10 解卷积前 (蓝红)后 (绿)结果对比
Figure 10. Comparison of results before and after deconvolution
图 11 光程差约0.375 mm平面镜样品成像结果。 (a) 平面镜样品成像结果； (b) 线照明中心位置轴向分辨率
Figure 11. Flat mirror sample imaging results (Δz=0.375 mm). (a) Flat mirror sample imaging results; (b) Axial resolution of focus line center position
图 12 透明胶带成像结果。 (a) 单一系数色散补偿后图像； (b) 分段色散补偿后图像
Figure 12. Scotch tape imaging results. (a) Image after single coefficient dispersion compensation; (b) Image after subsection dispersion compensation
图 14 水果样品成像结果。 (a) 苹果表皮； (b) 梨表皮； (c) 橙子囊瓣
Figure 14. Fruits sample imaging results. (a) Apple skin; (b) Pear skin; (c) Orange saccule
表 1 大深度位置灵敏度和轴向分辨对比
Table 1. Comparison of sensitivity and axial resolution
OPD Δz/mm Axial resolution/μm Sensitivity/dB 1.05 44.95→12.9 79.4→84.6 1.25 56.60→10.98 78.6→84.7 1.45 68.50→17.40 77.1→82.7 1.65 84.05→36.05 76.3→79.0 1.85 89.10→12.15 75.8→82.6 2.05 98.40→8.67 75.3→83.9 2.25 97.66→18.53 74.5→80.5 2.45 125.89→35.41 73.3→78.0 2.65 123.45→37.15 72.8→76.3 2.85 130.18→11.25 72.4→81.7 3.05 96.11→21.12 73.1→79.3 
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