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High-resolution (HR) magnetic resonance imaging (MRI) provides detailedanatomical information that is critical for diagnosis in the clinicalapplication. However, HR MRI typically comes at the cost of long scan time,small spatial coverage, and low signal-to-noise ratio (SNR). Recent studiesshowed that with a deep convolutional neural network (CNN), HR generic imagescould be recovered from low-resolution (LR) inputs via single imagesuper-resolution (SISR) approaches. Additionally, previous works have shownthat a deep 3D CNN can generate high-quality SR MRIs by using learned imagepriors. However, 3D CNN with deep structures, have a large number of parametersand are computationally expensive. In this paper, we propose a novel 3D CNNarchitecture, namely a multi-level densely connected super-resolution network(mDCSRN), which is light-weight, fast and accurate. We also show that with thegenerative adversarial network (GAN)-guided training, the mDCSRN-GAN providesappealing sharp SR images with rich texture details that are highly comparablewith the referenced HR images. Our results from experiments on a large publicdataset with 1,113 subjects showed that this new architecture outperformedother popular deep learning methods in recovering 4x resolution-downgradedimages in both quality and speed.

MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better