A Dual-domain Deep Lattice Network for Rapid MRI Reconstruction

Compressed sensing is utilized with the aims of reconstructing an MRI using a fraction of measurements to accelerate magnetic resonance imaging called compressed sensing magnetic resonance imaging (CS-MRI). Conventional optimization-based CS-MRI methods use random under-sampling patterns and model the MRI data in the image domain as the classic CS-MRI paradigm. Instead, we design a uniform under-sampling strategy and explore the potential of modeling the MRI data directly in the measured Fourier domain. We propose a dual-domain deep lattice network (DD-DLN) for CS-MRI with variable density uniform under-sampling. We train the networks to learn the mapping between both image and frequency domains. We observe the dual networks have complementary advantages, which motivates their combination via a lattice structure. Experiments show that the proposed DD-DLN model provides promising performance in CS-MRI under the designed variable density uniform under-sampling.