Fundus imaging is a critical tool in ophthalmology, with different imaging modalities offering unique advantages. For instance, fundus fluorescein angiography (FFA) can accurately identify eye diseases. However, traditional invasive FFA involves the injection of sodium fluorescein, which can cause discomfort and risks. Generating corresponding FFA images from non-invasive fundus images holds significant practical value but also presents challenges. First, limited datasets constrain the performance and effectiveness of models. Second, previous studies have primarily focused on generating FFA for single diseases or single modalities, often resulting in poor performance for patients with various ophthalmic conditions. To address these issues, we propose a novel latent diffusion model-based framework, Diffusion, which introduces a fine-tuning protocol to overcome the challenge of limited medical data and unleash the generative capabilities of diffusion models. Furthermore, we designed a new approach to tackle the challenges of generating across different modalities and disease types. On limited datasets, our framework achieves state-of-the-art results compared to existing methods, offering significant potential to enhance ophthalmic diagnostics and patient care. Our code will be released soon to support further research in this field.

翻译：眼底成像在眼科学中是一种关键工具，不同的成像模态具有独特的优势。例如，眼底荧光血管造影（FFA）能够准确识别眼部疾病。然而，传统的侵入性FFA需要注射荧光素钠，可能引起不适和风险。从非侵入性眼底图像生成对应的FFA图像具有重要的实用价值，但也面临挑战。首先，有限的数据集限制了模型的性能和效果。其次，先前的研究主要集中于生成单一疾病或单一模态的FFA，对于患有多种眼科疾病的患者往往表现不佳。为解决这些问题，我们提出了一种新颖的基于潜在扩散模型的框架Diffusion，该框架引入了一种微调协议，以克服有限医学数据的挑战并释放扩散模型的生成能力。此外，我们设计了一种新方法来应对跨不同模态和疾病类型生成的挑战。在有限数据集上，与现有方法相比，我们的框架取得了最先进的结果，为增强眼科诊断和患者护理提供了显著潜力。我们的代码即将发布，以支持该领域的进一步研究。