Keywords: AI Diffusion Models, AI/ML Image Reconstruction
Motivation: An all-in-one universal MR image restoration framework can reduce scan times while preserving image resolution and signal-to-noise ratio (SNR) across various clinical and technical scenarios.
Goal(s): Combine the traditional plug-and-play (PnP) method with the diffusion sampling framework to restore complex MRI data accurately and robustly with a reasonable inference time.
Approach: A powerful MRI model is trained on diverse and extensive complex-valued MRI datasets and then integrated into the PnP method for universal MR image restoration tasks.
Results: Experimental findings indicate that our method provides accurate reconstructions for different MR inverse problems and demonstrates improved generalizability to cases outside the training data distribution.
Impact: The Proposed Diffusion PnP method enables fast and accurate MRI reconstructions using a pre-trained diffusion prior, without the need for fine-tuning or retraining. This approach demonstrates strong potential for diverse clinical applications in MRI.
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