Contemporary deep learning methods have shown promising results over classical methods for image reconstruction. However, they are not robust to image alterations due to deviations in the imaging conditions at test time. The training process does not account for explicit variations in the contextual information about the imaging task, such as image control parameters and input settings that could hold a causal relationship to the visual content of the reconstructed image. In this work, we propose dynamic weight prediction (DWP) networks to learn the contextual settings and the relationships between them. This model-based meta-learning approach would offer two valuable capabilities in a single network - (i) scalability to multiple input settings and (ii) tunability to continuously varying control settings with the knowledge of a few settings.

The proposed network, MCI-HyperNet, is a controllable image reconstruction network with DWP sub-networks conditioned on multiple contextual settings. The proposed approach strikes a balance between reliable context-adaptive weight learning and context-agnostic weight learning to obtain robust image reconstructions. We have extensively experimented with our network for accelerated MRI reconstruction considering the essential research directions provided in the fastMRI challenge results.

In this talk, we are going to discuss the methodology behind the proposed dynamic weight prediction-based neural network and its efficacy for MRI reconstruction in terms of the scalability of the model to multiple deviated acquisition settings.