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Domain-specific adaptation for MR image synthesis with text-guided diffusion.

Yannuo Wen¹, John Healy¹, Yang Song²

¹School of Electrical and Electronic Engineering, University College Dublin, Dublin 4, D04V1W8 Dublin , Republic of Ireland.

Physics in Medicine and Biology

|June 3, 2026

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This summary is machine-generated.

This study introduces a novel domain-specific Latent Diffusion Model (LDM) for synthesizing medical images, overcoming data scarcity challenges. The method effectively generates realistic brain MRI scans, improving AI model performance and aiding research.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Deep learning in medical imaging is hindered by limited data availability.
Existing generative models struggle to replicate pathological textures in small datasets.

Purpose of the Study:

To develop a domain-specific, partition-based Latent Diffusion Model (LDM) for enhanced medical image synthesis.
To address data scarcity and improve texture restoration in synthetic medical images.

Main Methods:

A parallel text-guided LDM framework was adapted for specific medical imaging domains.
Diseased regions were identified, and healthy regions were subdivided using Voronoi-grayscale adaptation for localized texture preservation.
Independent synthesis of image partitions followed by merging and denoising created complete synthetic images with segmentation masks.

Keywords:

domain-specific synthesis glioma MRI latent diffusion model synthetic medical imaging

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Main Results:

The model achieved a Frechet Inception Distance (FID) of 13.65 and a Structural Similarity Index Measure (SSIM) of 0.9674 on glioma MRI data.
Radiologists had a 74.5% deception rate in identifying synthetic images, with 41% universally misclassified as real.
Training a U-Net model on synthetic data improved segmentation accuracy (DSC) by 14%.

Conclusions:

The proposed framework generates perceptually plausible, structure-preserving synthetic MRI slices in data-limited settings.
This approach enhances downstream segmentation performance and shows potential for AI development, clinical teaching, and rare disease research.