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.
@article{arxiv.2603.04340,
title = {Balancing Fidelity, Utility, and Privacy in Synthetic Cardiac MRI Generation: A Comparative Study},
author = {Madhura Edirisooriya and Dasuni Kawya and Ishan Kumarasinghe and Isuri Devindi and Mary M. Maleckar and Roshan Ragel and Isuru Nawinne and Vajira Thambawita},
journal= {arXiv preprint arXiv:2603.04340},
year = {2026}
}