Balancing Fidelity, Utility, and Privacy in Synthetic Cardiac MRI Generation: A Comparative Study

2026-03-04

AI Summary

This study addresses the challenges of limited data availability and privacy constraints in deep learning for cardiac MRI, which hinder the acquisition of high-quality, compliant training datasets. The authors propose an anatomy-mask-guided two-stage generative framework and present the first systematic evaluation of denoising diffusion probabilistic models (DDPM), latent diffusion models (LDM), and flow matching (FM) in the context of synthetic cardiac MRI generation. The evaluation focuses on the trade-offs among image fidelity, utility for downstream segmentation tasks, and privacy preservation. Experimental results demonstrate that DDPM achieves the best overall balance across these three criteria under data-scarce conditions, while FM, although slightly inferior in task performance, offers stronger privacy guarantees.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Security and privacy of machine learning and AI applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Deep learning in cardiac MRI (CMR) is fundamentally constrained by both data scarcity and privacy regulations. This study systematically benchmarks three generative architectures: Denoising Diffusion Probabilistic Models (DDPM), Latent Diffusion Models (LDM), and Flow Matching (FM) for synthetic CMR generation. Utilizing a two-stage pipeline where anatomical masks condition image synthesis, we evaluate generated data across three critical axes: fidelity, utility, and privacy. Our results show that diffusion-based models, particularly DDPM, provide the most effective balance between downstream segmentation utility, image fidelity, and privacy preservation under limited-data conditions, while FM demonstrates promising privacy characteristics with slightly lower task-level performance. These findings quantify the trade-offs between cross-domain generalization and patient confidentiality, establishing a framework for safe and effective synthetic data augmentation in medical imaging.

Problem

• synthetic data
• cardiac MRI
• privacy
• fidelity
• utility

Innovation

• synthetic cardiac MRI
• diffusion models
• privacy-preserving generation
• data augmentation
• medical image synthesis