  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 a
super-resolved version. This work applies SRGAN to MR images of the prostate
and performs three experiments. The first experiment explores improving the
in-plane MR image resolution by factors of 4 and 8, and shows that, while the
PSNR and SSIM (Structural SIMilarity) metrics are lower than the isotropic
bicubic interpolation baseline, the SRGAN is able to create images that have
high edge fidelity. The second experiment explores anisotropic super-resolution
via synthetic images, in that the input images to the network are
anisotropically downsampled versions of HR images. This experiment demonstrates
the ability of the modified SRGAN to perform anisotropic super-resolution, with
quantitative image metrics that are comparable to those of the anisotropic
bicubic interpolation baseline. Finally, the third experiment applies a
modified version of the SRGAN to super-resolve anisotropic images obtained from
the through-plane slices of the volumetric MR data. The output super-resolved
images contain a significant amount of high frequency information that make
them visually close to their HR counterparts. Overall, the promising results
from each experiment show that super-resolution for MR images is a successful
technique and that producing isotropic MR image volumes from anisotropic slices
is an achievable goal.
