{"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/unsupervised-learning-of-multi-modal-affine","title":"Unsupervised Learning of Multi-modal Affine Registration for PET/CT","arxiv_id":"2409.13863","date":"2024-09-20","proceeding":null,"authors":["Junyu Chen","Yihao Liu","Shuwen Wei","Aaron Carass","Yong Du"],"abstract":"Affine registration plays a crucial role in PET/CT imaging, where aligning PET with CT images is challenging due to their respective functional and anatomical representations. Despite the significant promise shown by recent deep learning (DL)-based methods in various medical imaging applications, their application to multi-modal PET/CT affine registration remains relatively unexplored. This study investigates a DL-based approach for PET/CT affine registration. We introduce a novel method using Parzen windowing to approximate the correlation ratio, which acts as the image similarity measure for training DNNs in multi-modal registration. Additionally, we propose a multi-scale, instance-specific optimization scheme that iteratively refines the DNN-generated affine parameters across multiple image resolutions. Our method was evaluated against the widely used mutual information metric and a popular optimization-based technique from the ANTs package, using a large public FDG-PET/CT dataset with synthetic affine transformations. Our approach achieved a mean Dice Similarity Coefficient (DSC) of 0.870, outperforming the compared methods and demonstrating its effectiveness in multi-modal PET/CT image registration.","url_abs":"https://arxiv.org/abs/2409.13863v1","url_pdf":"https://arxiv.org/pdf/2409.13863v1.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":"unsupervised-learning-of-multi-modal-affine","repo_url":"https://github.com/junyuchen245/correlation_ratio","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-registration","task_name":"Image Registration"}],"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}