Physics-guided deep learning-based real-time image reconstruction of Fourier-domain optical coherence tomography

We present a novel physics-guided deep learning approach for high-quality, real-time Fourier-domain optical coherence tomography (FD-OCT) image reconstruction. The proposed framework leverages the underlying OCT imaging physics to guide learning and generates high-quality images while providing a physically sound solution to the original problem. Evaluations on synthetic and experimental datasets demonstrate the superior performance of our proposed physics-guided deep learning approach for FD-OCT image reconstruction. The method achieves the highest image quality metrics compared to the inverse discrete Fourier transform (IDFT), the optimization-based methods, a state-of-the-art image enhancement network, and conventional supervised learning. Furthermore, our method enables real-time frame rates of 232 fps for synthetic images and 87 fps for experimental images, which represents significant improvements over existing techniques. Our physics-guided deep learning-based approach could offer a promising solution for high-quality, real-time FD-OCT image reconstruction, which potentially paves the way for leveraging the power of deep learning in real-world OCT imaging applications.