Graph Image Prior for Unsupervised Dynamic MRI Reconstruction

Motivation: Current unsupervised dynamic-MRI reconstruction algorithms based on DIP uses very low-dimensional latent variables and a single generator for direct non-linear mapping, which may limit the performance. Goal(s): To propose a new model and algorithm for unsupervised dynamic MRI reconstruction. Approach: We propose a novel Graph-Image-Prior(GIP) model, which uses branched CNN generators to recover the image structure, and use a Graph-Neural-Network(GNN) to discover the best spatio-temporal manifold. Besides, we devise an ADMM algorithm to alternately optimize the dynamic image and network. Results: The proposed method achieves the state-of-art performance even compared with supervised deep-learning methods, without the need for any fully-sampled data. Impact: The proposed Graph-Image-Prior(GIP) scheme is a new unsupervised image reconstruction model, which has a significant value for further research. Besides, GIP is promising to be used in other multi-frame MRI reconstruction applications where fully-sampled data is scarce or unavailable.