Graph Image Prior for Unsupervised Dynamic MRI Reconstruction
Zhongsen Li Wenxuan Chen Shuai Wang Chuyu Liu Rui Li
The inductive bias of the convolutional neural network (CNN) can act as a
strong prior for image restoration, which is known as the Deep Image Prior
(DIP). In recent years, DIP has been utilized in unsupervised dynamic MRI
reconstruction, which adopts a generative model from the latent space to the
image space. However, existing methods usually utilize a single pyramid-shaped
CNN architecture to parameterize the generator, which cannot effectively
exploit the spatio-temporal correlations within the dynamic data. In this work,
we propose a novel scheme to exploit the DIP prior for dynamic MRI
reconstruction, named ``Graph Image Prior'' (GIP). The generative model is
decomposed into two stages: image recovery and manifold discovery, which is
bridged by a graph convolutional network to exploit the spatio-temporal
correlations. In addition, we devise an ADMM algorithm to alternately optimize
the images and the network parameters to further improve the reconstruction
performance. Experimental results demonstrate that GIP outperforms compressed
sensing methods and unsupervised methods over different sampling trajectories,
and significantly reduces the performance gap with the state-of-art supervised
deep-learning methods. Moreover, GIP displays superior generalization ability
when transferred to a different reconstruction setting, without the need for
any additional data.