Keywords: AI Diffusion Models, Diffusion Reconstruction, Diffusion Models, Image Reconstruction, ControlNet
Motivation: High-b value DWI suffer from low SNR. Existing deep learning methods for acquisition speed-up are sensitive to protocol changes. A powerful model robust to different clinic scenarios would be ideal.
Goal(s): We aim to use plug-and-play techniques with a diffusion prior with spatial conditioning.
Approach: We first train a powerful diffusion prior on a diverse dataset. An adapter is trained for high-b value DWI reconstruction using the low-b value DWI as condition.
Results: Based on our evaluation, the usage of adapters improves upon the existing plug-and-play with diffusion methods, offering faster convergence.
Impact: The proposed PnP method offers a flexible solution for using a pre-trained diffusion prior in a flexible framework for image reconstruction. Using the adapter can improve certain scenarios, only if needed, without the need for re-training the prior.
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