用于谱域光学相干断层扫描色散补偿的人工神经网络(ANN)

IF 1.3 4区工程技术 Q4 CHEMISTRY, ANALYTICAL

Instrumentation Science & Technology Pub Date : 2022-03-09 DOI:10.1080/10739149.2022.2048008

Dan Yang, W. Guo, T. Cheng, Zhulin Wei, Bin Xu

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引用次数: 2

摘要

色散是导致光学相干层析成像(OCT)轴向分辨率降低的一个因素。本文提出了一种用于校正色散问题的人工神经网络(ANN)。通过训练神经网络，只从给定的干扰谱信号中预测无色散谱信号。首先，分析了OCT的色散原理。其次，将寻找无色散频谱信号分布全局最优的过程描述为一个训练过程，并引入了人工神经网络模型。仿真和实验结果表明，该方法提高了系统的轴向分辨率。因此，人工神经网络模型拟合了输入和输出之间的非线性关系，光谱响应显示了OCT中色散引起的半峰全宽(FWHM)问题，具有重要意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Artificial neural network (ANN) for dispersion compensation of spectral domain – optical coherence tomography (SD-OCT)

Abstract Dispersion is a factor that causes the axial resolution to decrease in optical coherence tomography (OCT). In this paper, an artificial neural network (ANN) is reported for correcting the dispersion problem. By training a neural network, the dispersion-free spectral signal is predicted only from a given interference spectral signal. First, the dispersion principle of OCT is analyzed. Next, the process for finding the global optimum of dispersion-free spectral signal distribution is described as a training process, and the ANN model is introduced. Lastly, simulation and experiments show that the presented method improves the axial resolution of the system. Accordingly, the ANN model fits the non-linear relationship between input and output, and the spectral response shows the problem of full width at half maximum (FWHM) due to dispersion in OCT which is of great significance.

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来源期刊

Instrumentation Science & Technology 工程技术-分析化学

CiteScore

3.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Instrumentation Science & Technology is an internationally acclaimed forum for fast publication of critical, peer reviewed manuscripts dealing with innovative instrument design and applications in chemistry, physics biotechnology and environmental science. Particular attention is given to state-of-the-art developments and their rapid communication to the scientific community. Emphasis is on modern instrumental concepts, though not exclusively, including detectors, sensors, data acquisition and processing, instrument control, chromatography, electrochemistry, spectroscopy of all types, electrophoresis, radiometry, relaxation methods, thermal analysis, physical property measurements, surface physics, membrane technology, microcomputer design, chip-based processes, and more. Readership includes everyone who uses instrumental techniques to conduct their research and development. They are chemists (organic, inorganic, physical, analytical, nuclear, quality control) biochemists, biotechnologists, engineers, and physicists in all of the instrumental disciplines mentioned above, in both the laboratory and chemical production environments. The journal is an important resource of instrument design and applications data.