Wang Mengyuan, Mao Jianing, Su Hang, Ling Yuye, Zhou Chuanqing, Su Yikai
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
College of Medical Instrument, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Biomed Opt Express. 2024 Oct 30;15(11):6619-6637. doi: 10.1364/BOE.538756. eCollection 2024 Nov 1.
In this paper, we introduce a physics-guided deep learning approach for high-quality, real-time Fourier-domain optical coherence tomography (FD-OCT) image reconstruction. Unlike traditional supervised deep learning methods, the proposed method employs unsupervised learning. It leverages the underlying OCT imaging physics to guide the neural networks, which could thus generate high-quality images and provide 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. The method achieves the highest image quality metrics compared to the inverse discrete Fourier transform (IDFT), the optimization-based methods, and several state-of-the-art methods based on deep learning. 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 FD-OCT image reconstruction, which potentially paves the way for leveraging the power of deep learning in real-world OCT imaging applications.
在本文中，我们介绍了一种用于高质量、实时傅里叶域光学相干断层扫描（FD-OCT）图像重建的物理引导深度学习方法。与传统的监督深度学习方法不同，该方法采用无监督学习。它利用潜在的OCT成像物理原理来指导神经网络，从而能够生成高质量图像，并为原始问题提供符合物理原理的解决方案。对合成数据集和实验数据集的评估证明了我们提出的物理引导深度学习方法的卓越性能。与逆离散傅里叶变换（IDFT）、基于优化的方法以及几种基于深度学习的最新方法相比，该方法实现了最高的图像质量指标。我们的方法能够实现合成图像232帧每秒和实验图像87帧每秒的实时帧率，这相较于现有技术有显著提升。我们基于物理引导深度学习的方法可为FD-OCT图像重建提供一个有前景的解决方案，这可能为在实际的OCT成像应用中利用深度学习的力量铺平道路。
