C2Diff: Cine-Controllable Diffusion Models for Late Gadolinium-Enhanced Cardiac MRI Synthesis

Deep learning-based methods for synthesizing late gadolinium-enhanced (LGE) cardiac magnetic resonance (CMR) images have attracted increasing attention at present, as they enable the generation of LGE images without the use of gadolinium-based contrast agents (GBCA). Traditional solutions such as generative adversarial networks (GANs) have demonstrated potential but are frequently limited by issues including mode collapse and training instability, which compromise their reliability in clinical applications. Diffusion models have recently gained attention as a promising alternative, owing to their stable training dynamics and superior ability to produce high resolution and realistic images. In this study, we introduce a diffusion model framework that leverages cine CMR, a noninvasive imaging modality, as a conditional input to synthesize LGE images. By incorporating the stability of diffusion modeling with cine-guided conditioning, our method generates controllable and consistent LGE images. We validate our method on the CARE2025 challenge dataset, and the experimental results demonstrate that our model outperforms previous approaches in terms of both image realism and overall visual quality. These findings highlight the potential of our cine-controllable diffusion framework as a reliable and accurate solution for LGE image synthesis, paving the way for safer and more accessible myocardial assessment without contrast administration.