[2603.24764] Synthetic Cardiac MRI Image Generation using Deep Generative Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.24764 (cs) [Submitted on 25 Mar 2026] Title:Synthetic Cardiac MRI Image Generation using Deep Generative Models Authors:Ishan Kumarasinghe, Dasuni Kawya, Madhura Edirisooriya, Isuri Devindi, Isuru Nawinne, Vajira Thambawita View a PDF of the paper titled Synthetic Cardiac MRI Image Generation using Deep Generative Models, by Ishan Kumarasinghe and 5 other authors View PDF 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 s...