{"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/learning-from-a-handful-volumes-mri","title":"Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests","arxiv_id":"1802.05518","date":"2018-02-15","proceeding":null,"authors":["Aline Sindel","Katharina Breininger","Johannes Käßer","Andreas Hess","Andreas Maier","Thomas Köhler"],"abstract":"Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical\nstructures. However, the acquisition of MR volumes with high spatial resolution\nleads to long scan times. To this end, we propose volumetric super-resolution\nforests (VSRF) to enhance MRI resolution retrospectively. Our method learns a\nlocally linear mapping between low-resolution and high-resolution volumetric\nimage patches by employing random forest regression. We customize features\nsuitable for volumetric MRI to train the random forest and propose a median\ntree ensemble for robust regression. VSRF outperforms state-of-the-art\nexample-based super-resolution in term of image quality and efficiency for\nmodel training and inference in different MRI datasets. It is also superior to\nunsupervised methods with just a handful or even a single volume to assemble\ntraining data.","url_abs":"http://arxiv.org/abs/1802.05518v1","url_pdf":"http://arxiv.org/pdf/1802.05518v1.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":"learning-from-a-handful-volumes-mri","repo_url":"https://github.com/asindel/VSRF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}