首页> 外文期刊>Optics Communications: A Journal Devoted to the Rapid Publication of Short Contributions in the Field of Optics and Interaction of Light with Matter >Adaptive optimization of reference intensity for optical coherence imaging using galvanometric mirror tilting method
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Adaptive optimization of reference intensity for optical coherence imaging using galvanometric mirror tilting method

机译:电流镜反射法光学相干成像参考强度的自适应优化

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摘要

Integration time and reference intensity are important factors for achieving high signal-to-noise ratio (SNR) and sensitivity in optical coherence tomography (OCT). In this context, we present an adaptive optimization method of reference intensity for OCT setup. The reference intensity is automatically controlled by tilting a beam position using a Galvanometric scanning mirror system. Before sample scanning, the OCT system acquires two dimensional intensity map with normalized intensity and variables in color spaces using false-color mapping. Then, the system increases or decreases reference intensity following the map data for optimization with a given algorithm. In our experiments, the proposed method successfully corrected the reference intensity with maintaining spectral shape, enabled to change integration time without manual calibration of the reference intensity, and prevented image degradation due to over-saturation and insufficient reference intensity. Also, SNR and sensitivity could be improved by increasing integration time with automatic adjustment of the reference intensity. We believe that our findings can significantly aid in the optimization of SNR and sensitivity for optical coherence tomography systems. (C) 2015 Elsevier B.V. All rights reserved.
机译:积分时间和参考强度是在光学相干断层扫描(OCT)中获得高信噪比(SNR)和灵敏度的重要因素。在这种情况下,我们提出了一种针对OCT设置的参考强度的自适应优化方法。使用检流扫描镜系统倾斜光束位置可自动控制参考强度。在样本扫描之前,OCT系统使用伪彩色映射获取具有归一化强度和变量的色彩空间中的二维强度图。然后,系统会根据地图数据增加或减少参考强度,以使用给定算法进行优化。在我们的实验中,所提出的方法在保持光谱形状的情况下成功地校正了参考强度,无需手动校准参考强度即可更改积分时间,并防止了由于过饱和和参考强度不足而导致的图像劣化。同样,可以通过自动调整参考强度来增加积分时间来提高SNR和灵敏度。我们相信我们的发现可以极大地帮助优化光学相干断层扫描系统的SNR和灵敏度。 (C)2015 Elsevier B.V.保留所有权利。

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