Efficient and Effective Control Adapters for Diffusion Plug-and-Play models: A DWI Application

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.