MR Image Reconstruction Based on Densely Connected Residual Generative Adversarial Network–DCR-GAN

Magnetic Resonance Image (MRI) reconstruction from undersampled data is an important ill-posed problem for biomedical imaging. For this problem, there is a significant tradeoff between the reconstructed image quality and image acquisition time reduction due to data sampling. Recently a plethora of solutions based on deep learning have been proposed in the literature to reach improved image reconstruction quality compared to traditional analytical reconstruction methods. In this paper, a novel densely connected residual generative adversarial network (DCR-GAN) is being proposed for fast and high-quality reconstruction of MR images. DCR blocks enable the reconstruction network to go deeper by preventing feature loss in the sequential convolutional layers. DCR block concatenates feature maps from multiple steps and gives them as the input to subsequent convolutional layers in a feed-forward manner. In this new model, the DCR block’s potential to train relatively deeper structures is utilized to improve quantitative and qualitative reconstruction results in comparison to the other conventional GAN-based models. We can see from the reconstruction results that the novel DCR-GAN leads to improved reconstruction results without a significant increase in the parameter complexity or run times.