4.6 Article

Real-time OCT image denoising using a self-fusion neural network

Journal

BIOMEDICAL OPTICS EXPRESS
Volume 13, Issue 3, Pages 1398-1409

Publisher

OPTICAL SOC AMER
DOI: 10.1364/BOE.451029

Keywords

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Funding

  1. National Institutes of Health [R01-EY030490, R01-EY031769]
  2. Vanderbilt Institute for Surgery and Engineering (VISE)
  3. NVIDIA Applied Research Accelerator Program

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Optical coherence tomography (OCT) is widely used in ophthalmic diagnostic imaging, but image quality varies and can introduce errors in analysis. Frame-averaging is a common method to improve image quality, but it is affected by bulk motion and takes longer acquisition time. A new method called self-fusion, which uses similarity between adjacent frames, is more robust to motion artifacts. In this study, a convolutional neural network was used to perform real-time OCT denoising and offset the computational overhead of self-fusion, resulting in improved image quality.
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. (c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

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