Real-time OCT image denoising using a self-fusion neural network.
Real-time OCT image denoising using a self-fusion neural network.
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DOI：
10.1364/boe.451029
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发表时间：
2022-02
影响因子：
3.4
通讯作者：
Jose J. Rico-Jimenez;Dewei Hu;Eric M. Tang;I. Oguz;Yuankai K. Tao
中科院分区：
文献类型：
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作者：
Jose J. Rico-Jimenez;Dewei Hu;Eric M. Tang;I. Oguz;Yuankai K. Tao
光学相干断层扫描（OCT）已成为眼科诊断成像的黄金标准。然而，临床OCT图像质量是高度可变的，并且有限的可视化可能在感兴趣的解剖和病理特征的定量分析中引入误差。帧平均是提高图像质量的标准方法，然而，在存在块运动的情况下的帧平均会降低横向分辨率和缩短总采集时间。我们最近引入了一种称为自融合的方法，该方法通过使用相邻帧之间的相似性来减少斑点噪声并提高OCT信噪比（SNR），并且比帧平均对运动伪影更鲁棒。然而，由于自融合是基于可变形配准，它是计算昂贵的。在这项研究中，实现了卷积神经网络来抵消自融合的计算开销，并实时执行OCT去噪。自融合网络被预先训练为融合3帧以实现接近视频速率的帧速率。我们的研究结果表明，在原始和帧平均OCT B扫描的自融合图像的峰值SNR的明显增益。这种方法提供了一种快速而强大的OCT去噪方法，可替代帧平均法，而无需重复图像采集。实时自融合图像增强将能够改善OCT视野相对于感兴趣特征的定位，并提高疾病解剖特征的灵敏度。
Optical coherence tomography (OCT) has become the gold standard for ophthalmic diagnostic imaging. However, clinical OCT image-quality is highly variable and limited visualization can introduce errors in the quantitative analysis of anatomic and pathologic features-of-interest. Frame-averaging is a standard method for improving image-quality, however, frame-averaging in the presence of bulk-motion can degrade lateral resolution and prolongs total acquisition time. We recently introduced a method called self-fusion, which reduces speckle noise and enhances OCT signal-to-noise ratio (SNR) by using similarity between from adjacent frames and is more robust to motion-artifacts than frame-averaging. However, since self-fusion is based on deformable registration, it is computationally expensive. In this study a convolutional neural network was implemented to offset the computational overhead of self-fusion and perform OCT denoising in real-time. The self-fusion network was pretrained to fuse 3 frames to achieve near video-rate frame-rates. Our results showed a clear gain in peak SNR in the self-fused images over both the raw and frame-averaged OCT B-scans. This approach delivers a fast and robust OCT denoising alternative to frame-averaging without the need for repeated image acquisition. Real-time self-fusion image enhancement will enable improved localization of OCT field-of-view relative to features-of-interest and improved sensitivity for anatomic features of disease.