{"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/deep-ct-to-mr-synthesis-using-paired-and","title":"Deep CT to MR Synthesis using Paired and Unpaired Data","arxiv_id":"1805.10790","date":"2018-05-28","proceeding":null,"authors":["Cheng-Bin Jin","Hakil Kim","Wonmo Jung","Seongsu Joo","Ensik Park","Ahn Young Saem","In Ho Han","Jae Il Lee","Xuenan Cui"],"abstract":"MR imaging will play a very important role in radiotherapy treatment planning\nfor segmentation of tumor volumes and organs. However, the use of MR-based\nradiotherapy is limited because of the high cost and the increased use of metal\nimplants such as cardiac pacemakers and artificial joints in aging society. To\nimprove the accuracy of CT-based radiotherapy planning, we propose a synthetic\napproach that translates a CT image into an MR image using paired and unpaired\ntraining data. In contrast to the current synthetic methods for medical images,\nwhich depend on sparse pairwise-aligned data or plentiful unpaired data, the\nproposed approach alleviates the rigid registration challenge of paired\ntraining and overcomes the context-misalignment problem of the unpaired\ntraining. A generative adversarial network was trained to transform 2D brain CT\nimage slices into 2D brain MR image slices, combining adversarial loss, dual\ncycle-consistent loss, and voxel-wise loss. The experiments were analyzed using\nCT and MR images of 202 patients. Qualitative and quantitative comparisons\nagainst independent paired training and unpaired training methods demonstrate\nthe superiority of our approach.","url_abs":"http://arxiv.org/abs/1805.10790v2","url_pdf":"http://arxiv.org/pdf/1805.10790v2.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":"deep-ct-to-mr-synthesis-using-paired-and","repo_url":"https://github.com/ChengBinJin/MRGAN-TensorFlow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.10790","atlas_url":"https://app.syntology.ai/?focus=1805.10790","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}