Deep Image Prior with Structured Sparsity (DISCUS) for Dynamic MRI Reconstruction

We propose an unsupervised learning method for dynamic MRI reconstruction. Our method, Deep Image prior with StruCtUred Sparsity (DISCUS), is an extension of Deep Image Prior (DIP) and employs joint optimization of network parameters and latent code vectors to recover image series. We enforce group sparsity on code vectors to reveal the underlying low-dimensional manifold of the image series. Using two sets of in vivo measurements and digital phantom simulation, we show that our approach significantly improves the reconstruction quality in terms of Normalized Mean Square Error (NMSE) and Structure Similarity Index Measure (SSIM) compared to compressed sensing and DIP.