This paper presents a real-time functional decomposition adaptive algorithm for the optimal sampling of the interferometric signal in Swept-Source Optical Coherence Tomography imaging systems, which completely eliminates the input signal dependent nonlinearities that are problematic in current state-of-the-art OCT realizations that use interpolation and resampling. The proposed adaptive calibration algorithm uses the Kalman approach to estimate the wavenumber index parameter k from the Mach-Zender Interferometer signal which is then applied to an adaptive level crossing sampler to generate a sampling clock that k-linearizes the data on real-time during the sampling process. Such a system implements an artifact-free realization of the technology removing the need for classical interpolation and resampling. The new real-time linearization scheme has the additional capability of increasing the imaging acquisition speed by 10X while providing robustness to noise, properties that are demonstrated through mathematical analysis and simulation results throughout the paper.

中文翻译：

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基于卡尔曼的实时功能分解，用于扫频光源光学相干层析成像中的光谱校准。

本文提出了一种实时功能分解自适应算法，用于在扫频光学相干断层扫描成像系统中对干涉信号进行最佳采样，从而完全消除了当前最新OCT中存在问题的依赖于输入信号的非线性问题使用插值和重采样的实现。提出的自适应校准算法使用卡尔曼方法从Mach-Zender干涉仪信号中估计波数指数参数k，然后将其应用于自适应电平交叉采样器以生成采样时钟，该采样时钟在采样过程中实时k线性化数据。采样过程。这样的系统实现了该技术的无伪造实现，从而消除了对经典内插和重采样的需求。