  High-resolution (HR) magnetic resonance images (MRI) provide detailed
anatomical information important for clinical application and quantitative
image analysis. However, HR MRI conventionally comes at the cost of longer scan
time, smaller spatial coverage, and lower signal-to-noise ratio (SNR). Recent
studies have shown that single image super-resolution (SISR), a technique to
recover HR details from one single low-resolution (LR) input image, could
provide high-quality image details with the help of advanced deep convolutional
neural networks (CNN). However, deep neural networks consume memory heavily and
run slowly, especially in 3D settings. In this paper, we propose a novel 3D
neural network design, namely a multi-level densely connected super-resolution
network (mDCSRN) with generative adversarial network (GAN)-guided training. The
mDCSRN quickly trains and inferences and the GAN promotes realistic output
hardly distinguishable from original HR images. Our results from experiments on
a dataset with 1,113 subjects show that our new architecture beats other
popular deep learning methods in recovering 4x resolution-downgraded im-ages
and runs 6x faster.
