Synthetic Cardiac MRI Image Generation using Deep Generative Models

2026-03-25

AI Summary

This work proposes a deep generative framework for synthesizing realistic cardiac MRI images to address challenges posed by scarce annotated data, substantial variability across scanner vendors, and high privacy risks. The method integrates segmentation map guidance, cross-vendor style conditioning, and standardized preprocessing, while combining flow matching with diffusion mechanisms to enhance anatomical fidelity and boundary sharpness. To balance utility and privacy, the framework incorporates differentially private training and is rigorously evaluated against membership inference attacks. Experimental results demonstrate that the synthesized data significantly improve segmentation accuracy in downstream tasks and enhance model robustness across multi-vendor settings, all while maintaining a low risk of privacy leakage.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Synthetic cardiac MRI (CMRI) generation has emerged as a promising strategy to overcome the scarcity of annotated medical imaging data. Recent advances in GANs, VAEs, diffusion probabilistic models, and flow-matching techniques aim to generate anatomically accurate images while addressing challenges such as limited labeled datasets, vendor variability, and risks of privacy leakage through model memorization. Maskconditioned generation improves structural fidelity by guiding synthesis with segmentation maps, while diffusion and flowmatching models offer strong boundary preservation and efficient deterministic transformations. Cross-domain generalization is further supported through vendor-style conditioning and preprocessing steps like intensity normalization. To ensure privacy, studies increasingly incorporate membership inference attacks, nearest-neighbor analyses, and differential privacy mechanisms. Utility evaluations commonly measure downstream segmentation performance, with evidence showing that anatomically constrained synthetic data can enhance accuracy and robustness across multi-vendor settings. This review aims to compare existing CMRI generation approaches through the lenses of fidelity, utility, and privacy, highlighting current limitations and the need for integrated, evaluation-driven frameworks for reliable clinical workflows.

Problem

• synthetic cardiac MRI
• data scarcity
• vendor variability
• privacy leakage
• medical image generation

Innovation

• synthetic cardiac MRI
• mask-conditioned generation
• diffusion models
• flow-matching
• differential privacy