  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.
