CMRDiff: Multi-sequence CMR synthesis

Motivation: The synthesis of multi-sequence cardiac magnetic resonance (CMR) images is of great significance to shorten the scan durations and expand the beneficiary population from CMR examination. Goal(s): Achieving accurate synthesis is particularly challenging due to the inherent suboptimal image quality and the persistent interference from noise. Approach: We first propose a novel method based on diffusion model, CMRDiff, for multi-sequence CMR synthesis. Results: We evaluated the proposed CMRDiff on the MICCAI2020 MyoPS Challenge dataset. Our experiments demonstrate that CMRDiff outperforms other state of-the-art multi-modal MRI synthesis methods. Impact: We design the first denoising diffusion probabilistic modelin the literature for multi-sequence CMR synthesis, promising to serve as an effective tool for multi-sequence CMR synthesis.