  High-resolution (HR) magnetic resonance imaging (MRI) provides detailed
anatomical information that is critical for diagnosis in the clinical
application. However, HR MRI typically comes at the cost of long scan time,
small spatial coverage, and low signal-to-noise ratio (SNR). Recent studies
showed that with a deep convolutional neural network (CNN), HR generic images
could be recovered from low-resolution (LR) inputs via single image
super-resolution (SISR) approaches. Additionally, previous works have shown
that a deep 3D CNN can generate high-quality SR MRIs by using learned image
priors. However, 3D CNN with deep structures, have a large number of parameters
and are computationally expensive. In this paper, we propose a novel 3D CNN
architecture, namely a multi-level densely connected super-resolution network
(mDCSRN), which is light-weight, fast and accurate. We also show that with the
generative adversarial network (GAN)-guided training, the mDCSRN-GAN provides
appealing sharp SR images with rich texture details that are highly comparable
with the referenced HR images. Our results from experiments on a large public
dataset with 1,113 subjects showed that this new architecture outperformed
other popular deep learning methods in recovering 4x resolution-downgraded
images in both quality and speed.
