{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/brain-mri-super-resolution-using-3d","title":"Brain MRI super-resolution using 3D generative adversarial networks","arxiv_id":"1812.11440","date":"2018-12-29","proceeding":null,"authors":["Irina Sanchez","Veronica Vilaplana"],"abstract":"In this work we propose an adversarial learning approach to generate high\nresolution MRI scans from low resolution images. The architecture, based on the\nSRGAN model, adopts 3D convolutions to exploit volumetric information. For the\ndiscriminator, the adversarial loss uses least squares in order to stabilize\nthe training. For the generator, the loss function is a combination of a least\nsquares adversarial loss and a content term based on mean square error and\nimage gradients in order to improve the quality of the generated images. We\nexplore different solutions for the upsampling phase. We present promising\nresults that improve classical interpolation, showing the potential of the\napproach for 3D medical imaging super-resolution. Source code available at\nhttps://github.com/imatge-upc/3D-GAN-superresolution","url_abs":"http://arxiv.org/abs/1812.11440v1","url_pdf":"http://arxiv.org/pdf/1812.11440v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"brain-mri-super-resolution-using-3d","repo_url":"https://github.com/imatge-upc/3D-GAN-superresolution","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.11440","atlas_url":"https://app.syntology.ai/?focus=1812.11440","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}