{"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/synthesis-of-positron-emission-tomography-pet","title":"Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)","arxiv_id":"1707.09747","date":"2017-07-31","proceeding":null,"authors":["Lei Bi","Jinman Kim","Ashnil Kumar","Dagan Feng","Michael Fulham"],"abstract":"Positron emission tomography (PET) image synthesis plays an important role,\nwhich can be used to boost the training data for computer aided diagnosis\nsystems. However, existing image synthesis methods have problems in\nsynthesizing the low resolution PET images. To address these limitations, we\npropose multi-channel generative adversarial networks (M-GAN) based PET image\nsynthesis method. Different to the existing methods which rely on using\nlow-level features, the proposed M-GAN is capable to represent the features in\na high-level of semantic based on the adversarial learning concept. In\naddition, M-GAN enables to take the input from the annotation (label) to\nsynthesize the high uptake regions e.g., tumors and from the computed\ntomography (CT) images to constrain the appearance consistency and output the\nsynthetic PET images directly. Our results on 50 lung cancer PET-CT studies\nindicate that our method was much closer to the real PET images when compared\nwith the existing methods.","url_abs":"http://arxiv.org/abs/1707.09747v1","url_pdf":"http://arxiv.org/pdf/1707.09747v1.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":"synthesis-of-positron-emission-tomography-pet","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":"synthesis-of-positron-emission-tomography-pet","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":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"image-generation","task_name":"Image Generation"}],"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}