Compressed sensing MR image reconstruction via a deep frequency-division network

International audienceCompressed sensing MRI (CS-MRI) is considered as a powerful technique for decreasing the scan time of MRI while ensuring the image quality. However, state of the art reconstruction algorithms are still subjected to two challenges including terrible parameters tuning and image details loss resulted from over-smoothing. In this paper, we propose a deep frequency-division network (DFDN) to face these two image reconstruction issues. The proposed DFDN approach applies a deep iterative reconstruction network (DIRN) to replace the regularization terms and the corresponding parameters by a stacked convolution neural network (CNN). And then multiple DIRN blocks are cascaded continuously as one deeper neural network. Data consistency (DC) layer is incorporated after each DIRN block to correct the k-space data of intermediate results. Image content loss is computed after each DC layer and frequency-division loss is gained by weighting the high frequency loss and low frequency loss after each DIRN block. The combination of image content loss and frequency-division loss is considered as the total loss for constraining the network training procedure. Validations of the proposed method have been performed on two brain datasets. Visual results and quantitative evaluations show that the proposed DFDN algorithm has better performance in sparse MRI reconstruction than other comparative methods