Structural MRI Synthesis for Alzheimer's Disease via Conditional Diffusion on Anatomical Masks

2026-06-16

AI Summary

This study addresses the challenge of synthesizing structural MRI scans for Alzheimer’s disease (AD), where neurodegenerative changes are subtle, region-specific, and progressive. The authors propose the first application of the conditional diffusion model Med-DDPM to generate AD-specific 3D MRI images, using anatomical segmentation masks from the ADNI dataset as conditioning inputs to accurately capture AD-related pathological alterations. Experimental results demonstrate that a segmentation model trained solely on synthetic data achieves a Dice score of 0.6532, comparable to that obtained with real data (0.6513). Furthermore, training with a hybrid dataset combining real and synthetic images yields the best performance, improving the Dice score to 0.7244 and significantly enhancing model recall and clinical diagnostic potential.

Abstract

Recent advances in generative machine learning models have significantly improved medical imaging, offering promising solutions for data augmentation, privacy preservation, and improved model generalization. However, synthesizing high-quality structural MRI data for Alzheimer's Disease (AD) remains challenging due to the subtle, region-specific, and progressive anatomical changes associated with neurodegeneration. In this paper, we extend the Med-DDPM conditional diffusion model -- originally designed for brain tumor synthesis -- to generate 3D structural MRIs specifically tailored to AD. We adopted Med-DDPM due to its established stability and structural fidelity compared to other generative models, which makes it particularly suitable for capturing the subtle anatomical changes characteristic of AD. Our approach conditions the diffusion process on anatomical segmentation masks derived from the ADNI dataset, incorporating key AD-relevant brain structures into the generation process. We systematically evaluate the quality and utility of the synthetic images by training segmentation models on real, synthetic, and hybrid (mixed) datasets. Experimental results demonstrate that segmentation models trained exclusively on synthetic data achieve comparable Dice scores (0.6532) to those trained on real data (0.6513), while exhibiting significantly enhanced recall. Notably, models trained on hybrid datasets (mixing real and synthetic images) outperform both real and synthetic-only baselines, achieving a Dice score of 0.7244. These findings underscore the successful use of conditional diffusion models for generating anatomically accurate, AD-specific synthetic MRIs, and highlight their potential for enhancing training data availability, improving diagnostic accuracy, and promoting research reproducibility in neuroimaging studies.

Problem

• Alzheimer's Disease
• structural MRI synthesis
• anatomical changes
• neurodegeneration
• medical image generation

Innovation

• conditional diffusion
• structural MRI synthesis
• Alzheimer's Disease
• anatomical masks
• Med-DDPM