Speeding up Magnetic Resonance Imaging (MRI) is an inevitable task in capturing multi-contrast MR images for medical diagnosis. In MRI, some sequences, e.g., in T2 weighted imaging, require long scanning time, while T1 weighted images are captured by short-time sequences. To accelerate MRI, in this paper, we propose a model-driven deep attention network, dubbed as MD-DAN, to reconstruct highly undersampled long-time sequence MR image with the guidance of a certain short-time sequence MR image. MD-DAN is a novel deep architecture inspired by the iterative algorithm optimizing a novel MRI reconstruction model regularized by cross-contrast prior using a guided contrast image. The network is designed to automatically learn cross-contrast prior by learning corresponding proximal operator. The backbone network to model the proximal operator is designed as a dual-path convolutional network with channel and spatial attention modules. Experimental results on a brain MRI dataset substantiate the superiority of our method with significantly improved accuracy. For example, MD-DAN achieves PSNR up to 35.04 dB at the ultra-fast 1/32 sampling rate.
