Perceptual cGAN for MRI Super-resolution

• URL: http://arxiv.org/abs/2201.09314v1
• Date: Sun, 23 Jan 2022 16:58:56 GMT
• Title: Perceptual cGAN for MRI Super-resolution
• Authors: Sahar Almahfouz Nasser, Saqib Shamsi, Valay Bundele, Bhavesh Garg, and Amit Sethi
• Abstract summary: Super-resolution (SR) can help increase their utility by synthetically generating high-resolution images with little additional time. 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.
• Score: 1.6656334450183463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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