arXiv Open Access 2022

Perceptual cGAN for MRI Super-resolution

Sahar Almahfouz Nasser Saqib Shamsi Valay Bundele Bhavesh Garg Amit Sethi

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Abstrak

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.

Topik & Kata Kunci

eess.IV cs.CV cs.LG

Penulis (5)

Sahar Almahfouz Nasser

Saqib Shamsi

Valay Bundele

Bhavesh Garg

Amit Sethi

Format Sitasi

APA MLA BibTeX

Nasser, S.A., Shamsi, S., Bundele, V., Garg, B., Sethi, A. (2022). Perceptual cGAN for MRI Super-resolution. https://arxiv.org/abs/2201.09314

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓