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

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

Automated summary

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.