Perceptual cGAN for MRI Super-resolution
This addresses the need for faster MRI scans in medical emergencies and pediatric cases, though it is an incremental improvement over existing GAN-based methods.
The paper tackles the problem of generating high-resolution MRI images from low-resolution inputs to reduce scan time, presenting a conditional GAN with perceptual loss that improves performance for isotropic and anisotropic super-resolution.
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.