Deep-learning–based optical coherence tomography reconstruction for high-speed and contrast morphology and vasculature imaging

Key Points

• Enhanced sensitivity and high acquisition speed are achieved simultaneously in image reconstruction.
• The deep neural network architecture improves optical coherence tomography image quality significantly.
• The proposed method utilizes deep learning to optimize the imaging process, focusing on morphology and vasculature.
• High-speed imaging capabilities may enable new applications in clinical and research settings.