Xu Jingjiang, Feng Zhongwu, Qiu Haixia, Tang Peijun, Gao Kai, Huang Yanping, Lan Gongpu, Qin Jia, An Lin, Jia Gangyong, Wu Qing
Foshan University, Guangdong-Hong Kong-Macao Intelligent Micro-Nano Optoelectronic Technology Joint Laboratory, Foshan, China.
Guangdong Weiren Meditech Co., Ltd, Innovation and Entrepreneurship Teams Project of Guangdong Pearl River Talents Program, Foshan, China.
J Biomed Opt. 2025 May;30(5):056006. doi: 10.1117/1.JBO.30.5.056006. Epub 2025 May 9.
Optical coherence tomography angiography (OCTA) usually suffers from the inherent random fluctuations of noise and speckles in the imaging system. Previous deep learning methods have mainly focused on improving the quality of B-scan blood flow images or projection images. We propose a deep learning method to reconstruct high-quality 3D vasculature, which fully utilizes the volumetric OCTA data and the topological features of the vascular network.
We propose a deep learning method called the three-dimensional C-scan-based generation adversarial network (3DCS-GAN) to improve vascular visualization for volumetric OCTA data.
To train the network, we superimposed the single-shot OCTA images on avascular noisy C-scan images to synthesize the input data and used the multiple averaged OCTA images as the reference labels. The deep learning algorithm is based on Pix2Pix architecture and consists of a generator model and a discriminator model. A perceptual loss function was utilized by combining content loss and adversarial loss. The proposed algorithm is applied to the C-scan images depth-by-depth to suppress the background noise and enhance vascular visualization in the 3D OCTA data.
The proposed method has improved the contrast-to-noise ratio of cross-sectional OCTA images by times. It greatly enhances the visualization of blood vessels in the deep layer and offers much clearer blood vessel topology in the 3D volume-rendering of OCTA. 3DCS-GAN has exhibited superior image enhancement compared with alternative methods. It has been used to enhance the OCTA images of port wine stain disease for clinical investigation.
It demonstrates that the proposed 3DCS-GAN can greatly improve vascular visualization in the deep layer, provide better image quality than the multiple averaged OCTA images, and achieve superior image enhancement for volumetric OCTA data.
光学相干断层扫描血管造影（OCTA）通常会受到成像系统中固有噪声和斑点的随机波动影响。以往的深度学习方法主要集中在提高B扫描血流图像或投影图像的质量上。我们提出一种深度学习方法来重建高质量的三维血管系统，该方法充分利用了体层OCTA数据和血管网络的拓扑特征。
我们提出一种名为基于三维C扫描的生成对抗网络（3DCS-GAN）的深度学习方法，以改善体层OCTA数据的血管可视化。
为了训练网络，我们将单次采集的OCTA图像叠加在无血管的噪声C扫描图像上以合成输入数据，并使用多个平均OCTA图像作为参考标签。深度学习算法基于Pix2Pix架构，由一个生成器模型和一个判别器模型组成。通过结合内容损失和对抗损失来使用感知损失函数。所提出的算法逐深度应用于C扫描图像，以抑制背景噪声并增强三维OCTA数据中的血管可视化。
所提出的方法将横断面OCTA图像的对比度噪声比提高了 倍。它极大地增强了深层血管的可视化效果，并在OCTA的三维体积渲染中提供了更清晰的血管拓扑结构。与其他方法相比，3DCS-GAN表现出卓越的图像增强效果。它已被用于增强葡萄酒色斑疾病的OCTA图像以进行临床研究。
结果表明，所提出的3DCS-GAN可以极大地改善深层血管的可视化效果，提供比多个平均OCTA图像更好的图像质量，并实现体层OCTA数据的卓越图像增强。
