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Related Experiment Video

Updated: Jan 13, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

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

Qing Liu¹, Hongqing Zhu¹, Tianwei Qian²

¹School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China.

Medical Image Analysis

|January 6, 2026

Summary

This summary is machine-generated.

This study introduces a new AI model, SFDC-MambaDiff, to create non-invasive retinal images from standard eye photos. This innovation offers a safer alternative to traditional dye-based angiography for diagnosing eye conditions.

Keywords:

Biconditional constraint Cross-modal FFA image generation Mamba diffusion model Spatial-frequency domain

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Published on: November 9, 2011

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A Multimodal Wide-Field Fourier-Transform Raman Microscope

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Published on: December 30, 2025

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Fundus Fluorescein Angiography (FFA) is vital for diagnosing retinal diseases but carries risks associated with intravenous dye injections.
Adverse reactions to fluorescein dye, including allergies and severe complications, necessitate safer diagnostic alternatives.

Purpose of the Study:

To develop a novel AI model, Spatial-Frequency Dual-Constraint Mamba Diffusion Model (SFDC-MambaDiff), for generating non-invasive FFA images from Color Fundus Photography (CFP).
To provide a safe and cost-effective method for retinal screening and diagnostic support, mitigating the risks of traditional FFA.

Main Methods:

A Learnable Wavelet Frequency-domain Extractor (LWFE) with spiral Mamba scanning captures frequency-domain vascular features as structural priors.
A Dual-pyramid Spatial-domain Feature Extractor (DSFE) uses convolutions and bidirectional scanning for spatial representations as lesion-aware priors.
Integration of these priors via a Dual-domain Conditional Constraint Mamba module (DCCM) with dual attention guides the diffusion model's denoising process.

Main Results:

SFDC-MambaDiff successfully synthesizes high-fidelity, non-invasive FFA images from CFP.
The model demonstrates strong performance on both public and private datasets, validating its efficacy.
Generated images maintain crucial diagnostic information without requiring invasive dye injection.

Conclusions:

SFDC-MambaDiff presents a promising, non-invasive alternative to conventional FFA for retinal imaging.
This AI-driven approach enhances patient safety and offers potential for widespread, low-cost diagnostic applications in ophthalmology.
The developed model paves the way for safer, more accessible retinal disease screening.