LPUWF-LDM: Enhanced latent diffusion model for precise late-phase UWF-FA generation on limited dataset

作     者：Fang, Zhaojie Yu, Xiao Zhou, Guanyu Zhuang, Ke Chen, Yifei Ge, Ruiquan Wang, Changmiao Jia, Gangyong Wu, Qing Ye, Juan Nuliqiman, Maimaiti Xu, Peifang Elazab, Ahmed 

作者机构：Hangzhou Dianzi Univ Hangzhou 310018 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen 518037 Peoples R China Misr Higher Inst Commerce & Comp Mansoura 35516 Egypt Zhejiang Univ Affiliated Hosp 2 Eye Ctr Zhejiang Prov Key Lab OphthalmolSch Med Hangzhou 310000 Peoples R China 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (Expert Sys Appl)

年 卷 期：2025年第270卷

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Open Project Program of the State Key Laboratory of CADCG [A2410] Zhejiang University, Zhejiang Provincial Natural Science Foundation of China [LY21F020017, 2022C03043, 2023C03090] National Natural Science Foundation of China [61702146, 62076084, U20A20386, U22A2033] GuangDong Basic and Applied Basic Research Foundation [2022A1515110570] Shenzhen Longgang District Science and Technology Innovation Special Fund [LGKCYLWS2023018] Shenzhen Science and Technology Program [JCYJ20220818103001002] Shenzhen Medical Research Fund [C2401036] 

主　　题：Diffusion model Loss enhancement Cross-modal generation Ultra-wide-field fundus photo Variational autoencoder 

摘      要：Ultra-Wide-Field Fluorescein Angiography (UWF-FA) enables precise identification of ocular diseases using sodium fluorescein, which can be potentially harmful. Existing research has developed methods to generate UWF-FA from Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) to reduce the adverse reactions associated with injections. However, these methods have been less effective in producing high-quality late- phase UWF-FA, particularly in lesion areas and fine details. Two primary challenges hinder the generation of high-quality late-phase UWF-FA: the scarcity of paired UWF-SLO and early/late-phase UWF-FA datasets, and the need for realistic generation at lesion sites and potential blood leakage regions. This study introduces an improved latent diffusion model framework to generate high-quality late-phase UWF-FA from limited paired UWF images. To address the challenges as mentioned earlier, our approach employs a module utilizing Cross- temporal Regional Difference Loss, which encourages the model to focus on the differences between early and late phases. Additionally, we introduce a low-frequency enhanced noise strategy in the diffusion forward process to improve the realism of medical images. To further enhance the mapping capability of the variational autoencoder module, especially with limited datasets, we implement a gated convolutional encoder to extract additional information from conditional images. Our Latent Diffusion Model for Ultra-Wide-Field Late-Phase Fluorescein Angiography (LPUWF-LDM) effectively reconstructs fine details in late-phase UWF-FA and achieves state-of-the-art results compared to other existing methods when working with limited datasets. Our source code is available at: https://***/Tinysqua/LPUWF-LDM.