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Pre- to post-contrast medical image synthesis with outline-guide accelerate diffusion model.

Xueying Fan¹, Liming Xu², Bochuan Zheng¹

¹School of Computer Science, China West Normal University, Nanchong, 637009, China.

Neural Networks : the Official Journal of the International Neural Network Society

|July 22, 2025

Summary

This study introduces a novel outline-guided diffusion model for synthesizing medical images without contrast agents. The model improves anatomical consistency and reduces synthesis time, enhancing diagnostic accuracy.

Keywords:

Diffusion model Multi-frequency enhanced attention Non-uniform sampling Outline-guided Structure preservation

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Contrast agents enhance medical image visibility but can cause adverse reactions.
Generative models aim to synthesize post-contrast images from pre-contrast scans to avoid contrast agent risks.
Diffusion models show promise for image synthesis but face challenges with anatomical deformation and slow processing.

Purpose of the Study:

To develop an improved diffusion model for synthesizing post-contrast medical images from pre-contrast images.
To address limitations of existing diffusion models, including anatomical structure deformation and slow synthesis speeds.
To enhance the accuracy and efficiency of generating contrast-enhanced medical images without contrast agents.

Main Methods:

Proposed a novel outline-guide accelerated diffusion model for medical image synthesis.
Utilized outline information from pre-contrast images to ensure anatomical structure consistency.
Incorporated a multi-frequency enhanced attention module for improved feature distinction and a non-uniform sampling strategy to accelerate synthesis.

Main Results:

The proposed model maintains clearer detail texture compared to existing methods.
Achieved high-quality post-contrast medical image synthesis with significantly reduced training time.
Demonstrated average improvements: 10.2% SSIM, 43.7% PSNR, 9.5% MSIM, and a 58.4% NRMSE reduction.

Conclusions:

The outline-guide accelerated diffusion model effectively synthesizes post-contrast medical images with improved anatomical consistency and detail.
The model offers a faster and more accurate alternative to traditional contrast-enhanced imaging, reducing risks associated with contrast agents.
This approach holds significant potential for enhancing medical diagnostics and patient safety.