Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina. Nonetheless, UWF fluorescein angiography (UWF-FA) necessitates the administration of a fluorescent dye via injection into the patient's hand or elbow unlike UWF scanning laser ophthalmoscopy (UWF-SLO). To mitigate potential adverse effects associated with injections, researchers have proposed the development of cross-modality medical image generation algorithms capable of converting UWF-SLO images into their UWF-FA counterparts. Current image generation techniques applied to fundus photography encounter difficulties in producing high-resolution retinal images, particularly in capturing minute vascular lesions. To address these issues, we introduce a novel conditional generative adversarial network (UWAFA-GAN) to synthesize UWF-FA from UWF-SLO. This approach employs multi-scale generators and an attention transmit module to efficiently extract both global structures and local lesions. Additionally, to counteract the image blurriness issue that arises from training with misaligned data, a registration module is integrated within this framework. Our method performs non-trivially on inception scores and details generation. Clinical user studies further indicate that the UWF-FA images generated by UWAFA-GAN are clinically comparable to authentic images in terms of diagnostic reliability. Empirical evaluations on our proprietary UWF image datasets elucidate that UWAFA-GAN outperforms extant methodologies. The code is accessible at https://github.com/Tinysqua/UWAFA-GAN.

翻译：眼底摄影结合超广角眼底技术，通过提供更全面的视网膜视野，已成为临床诊断中不可或缺的工具。然而，与超广角扫描激光检眼镜不同，超广角荧光素血管造影需要通过向患者手部或肘部注射荧光染料。为减轻注射带来的潜在不良反应，研究人员提出开发跨模态医学图像生成算法，将超广角扫描激光检眼镜图像转换为对应的超广角荧光素血管造影图像。当前应用于眼底摄影的图像生成技术在生成高分辨率视网膜图像时面临困难，尤其在捕捉微小血管病变方面。针对这些问题，我们提出一种新型条件生成对抗网络UWAFA-GAN，用于从超广角扫描激光检眼镜合成超广角荧光素血管造影图像。该方法采用多尺度生成器与注意力传输模块，高效提取全局结构及局部病变。此外，为应对非配准数据训练导致的图像模糊问题，该框架中集成了配准模块。我们的方法在初始分数和细节生成方面表现显著。临床用户研究进一步表明，UWAFA-GAN生成的超广角荧光素血管造影图像在诊断可靠性方面与真实图像具有临床可比性。基于我们专有超广角眼底数据集的实验评估证明，UWAFA-GAN优于现有方法。代码开源地址为https://github.com/Tinysqua/UWAFA-GAN。