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Image reconstruction from nonuniformly spaced samples in spectral-domain optical coherence tomography

机译:光谱域光学相干层析成像中非均匀间隔样本的图像重建

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摘要

In spectral-domain optical coherence tomography (SD-OCT), data samples are collected nonuniformly in the wavenumber domain, requiring a measurement re-sampling process before a conventional fast Fourier transform can be applied to reconstruct an image. This re-sampling necessitates extra computation and often introduces errors in the data. Instead, we develop an inverse imaging approach to reconstruct an SD-OCT image. We make use of total variation (TV) as a constraint to preserve the image edges, and estimate the two-dimensional cross-section of a sample directly from the SD-OCT measurements rather than processing for each A-line. Experimental results indicate that compared with the conventional method, our technique gives a smaller noise residual. The potential of using the TV constraint to suppress sensitivity falloff in SD-OCT is also demonstrated with experiment data.
机译:在光谱域光学相干断层扫描(SD-OCT)中,在波数域中不均匀地收集数据样本,需要先进行测量重新采样过程,然后才能应用常规快速傅里叶变换来重建图像。这种重新采样需要额外的计算,并且经常在数据中引入错误。相反,我们开发了一种逆成像方法来重建SD-OCT图像。我们利用总变化(TV)作为约束来保留图像边缘,并直接从SD-OCT测量而不是针对每个A线进行处理来估计样本的二维横截面。实验结果表明,与常规方法相比,我们的技术具有较小的噪声残留。实验数据还显示了使用电视约束来抑制SD-OCT中灵敏度下降的潜力。

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