Keywords: AI Diffusion Models, Cardiovascular
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords