  Capturing high-resolution magnetic resonance (MR) images is a time consuming
process, which makes it unsuitable for medical emergencies and pediatric
patients. Low-resolution MR imaging, by contrast, is faster than its
high-resolution counterpart, but it compromises on fine details necessary for a
more precise diagnosis. Super-resolution (SR), when applied to low-resolution
MR images, can help increase their utility by synthetically generating
high-resolution images with little additional time. In this paper, we present a
SR technique for MR images that is based on generative adversarial networks
(GANs), which have proven to be quite useful in generating sharp-looking
details in SR. We introduce a conditional GAN with perceptual loss, which is
conditioned upon the input low-resolution image, which improves the performance
for isotropic and anisotropic MRI super-resolution.
