  Acquiring High Resolution (HR) Magnetic Resonance (MR) images requires the
patient to remain still for long periods of time, which causes patient
discomfort and increases the probability of motion induced image artifacts. A
possible solution is to acquire low resolution (LR) images and to process them
with the Super Resolution Generative Adversarial Network (SRGAN) to create an
HR version. Acquiring LR images requires a lower scan time than acquiring HR
images, which allows for higher patient comfort and scanner throughput. This
work applies SRGAN to MR images of the prostate to improve the in-plane
resolution by factors of 4 and 8. The term 'super resolution' in the context of
this paper defines the post processing enhancement of medical images as opposed
to 'high resolution' which defines native image resolution acquired during the
MR acquisition phase. We also compare the SRGAN to three other models: SRCNN,
SRResNet, and Sparse Representation. While the SRGAN results do not have the
best Peak Signal to Noise Ratio (PSNR) or Structural Similarity (SSIM) metrics,
they are the visually most similar to the original HR images, as portrayed by
the Mean Opinion Score (MOS) results.
