{"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-mr-to-ct-synthesis-using-unpaired-data","title":"Deep MR to CT Synthesis using Unpaired Data","arxiv_id":"1708.01155","date":"2017-08-03","proceeding":null,"authors":["Jelmer M. Wolterink","Anna M. Dinkla","Mark H. F. Savenije","Peter R. Seevinck","Cornelis A. T. van den Berg","Ivana Isgum"],"abstract":"MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis.\nCurrent deep learning methods for MR-to-CT synthesis depend on pairwise aligned\nMR and CT training images of the same patient. However, misalignment between\npaired images could lead to errors in synthesized CT images. To overcome this,\nwe propose to train a generative adversarial network (GAN) with unpaired MR and\nCT images. A GAN consisting of two synthesis convolutional neural networks\n(CNNs) and two discriminator CNNs was trained with cycle consistency to\ntransform 2D brain MR image slices into 2D brain CT image slices and vice\nversa. Brain MR and CT images of 24 patients were analyzed. A quantitative\nevaluation showed that the model was able to synthesize CT images that closely\napproximate reference CT images, and was able to outperform a GAN model trained\nwith paired MR and CT images.","url_abs":"http://arxiv.org/abs/1708.01155v1","url_pdf":"http://arxiv.org/pdf/1708.01155v1.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-mr-to-ct-synthesis-using-unpaired-data","repo_url":"https://github.com/ChengBinJin/MRGAN-TensorFlow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deep-mr-to-ct-synthesis-using-unpaired-data","repo_url":"https://github.com/ChengBinJin/SpineC2M","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1708.01155","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}