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Numerical study on spectral domain optical coherence tomography spectral calibration and re-sampling importance

机译：光谱域光学相干层析成像的数值研究及重采样重要性

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A spectral calibration technique, a data processing method and the importance of calibration and re-sampling methods for the spectral domain optical coherence tomography system were numerically studied, targeted to optical coherence tomography (OCT) signal processing implementation under graphics processing unit (GPU) architecture. Accurately, assigning the wavelength to each pixel of the detector is of paramount importance to obtain high quality images and increase signal to noise ratio (SNR). High quality imaging can be achieved by proper calibration methods, here performed by phase calibration and interpolation. SNR was assessed employing two approaches, single spectrum moving window averaging and consecutive spectra data averaging, to investigate the optimized method and factor for background noise reduction. It was demonstrated that the consecutive spectra averaging had better SNR performance.
机译：以图形处理单元（GPU）架构下的光学相干层析成像（OCT）信号处理为目标，对光谱校准技术，数据处理方法以及光谱域光学相干层析成像系统的校准和重采样方法的重要性进行了数值研究。准确地，将波长分配给检测器的每个像素对于获取高质量图像并提高信噪比（SNR）至关重要。高质量的成像可以通过适当的校准方法来实现，此处通过相位校准和插值来执行。使用单频谱移动窗口平均和连续频谱数据平均两种方法评估SNR，以研究降低背景噪声的优化方法和因素。结果表明，连续频谱平均具有更好的SNR性能。

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