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Fourier domain optical coherence tomography artifact and speckle reduction by autoregressive spectral estimation without a loss of resolution

机译:傅里叶域光学相干断层扫描伪像和斑点减少,通过自回归光谱估计而不会降低分辨率

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摘要

Fourier Domain (FD) Optical Coherence Tomography (OCT) interferograms require a Fourier transformation in order to be converted to A-Scans representing the backscattering intensity from the different depths of the tissue microstructure. Most often, this transformation is performed using a discrete Fourier transform, i.e. the well-known Fast Fourier Transform (FFT). However, there are many alternatives for performing the necessary spectral conversion. Autoregressive (AR) spectral estimation techniques are one such example. The parametric nature of AR techniques offers several advantages, compared to the commonly-used FFT, including better convergence and less susceptibility to noise. They can also be adjusted to represent more or less of the signal detail depending on the order of the autoregression. These features make them uniquely suited for processing the FD OCT data. The advantages of the proposed methodology are illustrated on in vivo skin imaging data and the resolution is verified on single back-reflections from a glass surface. AR spectral estimation can be used to convert the interferograms to A-Scans while at the same time reducing the artifacts caused by high intensity back-reflections (by -20 dB) and diminishing the speckle (by -12 dB) all without the degradation in the resolution associated with other techniques.
机译:傅立叶域(FD)光学相干断层扫描(OCT)干涉图需要进行傅立叶变换,以便转换为代表来自组织微结构不同深度的反向散射强度的A扫描。通常,使用离散傅立叶变换,即众所周知的快速傅立叶变换(FFT)来执行该变换。但是,有许多替代方法可以执行必要的光谱转换。自回归(AR)频谱估计技术就是这样一种示例。与常用的FFT相比,AR技术的参数性质具有多个优点,包括更好的收敛性和对噪声的敏感性。也可以根据自回归的顺序调整它们以表示或多或少的信号细节。这些功能使它们特别适合处理FD OCT数据。在体内皮肤成像数据上说明了所提出方法的优势,并通过玻璃表面的单向后向反射验证了分辨率。 AR频谱估计可用于将干涉图转换为A扫描,同时减少由高强度背向反射(-20 dB)引起的伪像并减少斑点(-12 dB),而不会降低与其他技术相关的分辨率。

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  • 来源
  • 会议地点 San Francisco CA(US)
  • 作者

    Evgenia Bousi; Costas Pitris;

  • 作者单位

    KIOS Research Center for Intelligent System and Networks, Dept. of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos St, Nicosia, Cyprus, 1678;

    KIOS Research Center for Intelligent System and Networks, Dept. of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos St, Nicosia, Cyprus, 1678;

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