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Abstract #1199

Universal MR Image Restoration with Diffusion Models as Plug-and-Play Priors

Mahmoud Mostapha1, Radu Miron2, Nirmal Janardhanan1, Mariappan S. Nadar1, Omar Darwish3, Till Huelnhagen3, Tobias Würfl3, Hersh Chandarana4, David Grodzki3, and Rainer Schneider3
1Siemens Healthineers, Princeton, NJ, United States, 2Siemens Industry Software România, Brasov, Romania, 3Siemens Healthineers AG, Erlangen, Germany, 4Department of Radiology, NYU Grossman School of Medicine, New York, NY, United States

Synopsis

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.

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Keywords