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.

本研究解决了如何在有限数据下生成眼底荧光血管造影（FFA）图像的问题，提出了一种新的基于潜在扩散模型的框架。该框架通过引入微调协议，优化了模型的生成能力，并在不同模态和疾病类型之间有效应对生成挑战，最终在有限数据集上实现了领先的研究成果，有望显著提升眼科诊断和患者护理。

基于扩散的眼底荧光血管造影合成在有限数据下的重新思考