Perceptual cGAN for MRI Super-resolution
Published in 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022
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 an SR technique for MR images that is based on generative adversarial networks (GANs), which have proven to be quite useful in producing 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.
Citation
’ Nasser, S., S. Shamsi, V. Bundele, B. Garg and A. Sethi, “Perceptual cGAN for MRI Super-resolution,” 2022 44th Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), 2022, pp. 3035- 3038, doi: 10.1109/EMBC48229.2022.9871832.’