Spatial-frequency dual-constrained Mamba diffusion model for cross-modal generation from CFP to FFA

Fundus Fluorescein Angiography (FFA) is a critical imaging technique for visualizing retinal vascular dynamics and diagnosing various retinal pathologies. However, its reliance on intravenous fluorescein dye may pose risks of nausea, allergies, and even life-threatening complications, with potential adverse reaction hazards. To overcome these limitations, we propose a novel Spatial-Frequency Dual-Constraint Mamba Diffusion Model (SFDC-MambaDiff) for cross-modal generation of FFA images from non-invasive Color Fundus Photography (CFP). Specifically, we design a Learnable Wavelet Frequency-domain Extractor (LWFE) that integrates spiral Mamba scanning to capture frequency-domain vascular features, serving as structural priors. In parallel, a Dual-pyramid Spatial-domain Feature Extractor (DSFE) is developed by combining convolution and average pooling operations with row-column bidirectional scanning to model both global and local spatial representations, which serve as lesion-aware priors. These two domain-specific priors are further integrated through a Dual-domain Conditional Constraint Mamba module (DCCM), which employs a dual attention mechanism to guide the progressive denoising process of the diffusion model. Experimental results on both public and private datasets demonstrate that SFDC-MambaDiff can synthesize high-fidelity, non-invasive FFA images, offering a promising alternative for safe retinal screening and low-cost diagnostic support. The code for this project is available at https://github.com/MISlq/SFDC-MambaDiff.