{"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/multi-modal-aggregation-network-for-fast-mr","title":"Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality","arxiv_id":"2110.08080","date":"2021-10-15","proceeding":null,"authors":["Chun-Mei Feng","Huazhu Fu","Tianfei Zhou","Yong Xu","Ling Shao","David Zhang"],"abstract":"Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vulnerable to say motion artifacts. Recently, many approaches have been developed to reconstruct full-sampled images from partially observed measurements to accelerate MR imaging. However, most approaches focused on reconstruction over a single modality, neglecting the discovery of correlation knowledge between the different modalities. Here we propose a Multi-modal Aggregation network for mR Image recOnstruction with auxiliary modality (MARIO), which is capable of discovering complementary representations from a fully sampled auxiliary modality, with which to hierarchically guide the reconstruction of a given target modality. This implies that our method can selectively aggregate multi-modal representations for better reconstruction, yielding comprehensive, multi-scale, multi-modal feature fusion. Extensive experiments on IXI and fastMRI datasets demonstrate the superiority of the proposed approach over state-of-the-art MR image reconstruction methods in removing artifacts.","url_abs":"https://arxiv.org/abs/2110.08080v3","url_pdf":"https://arxiv.org/pdf/2110.08080v3.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":"multi-modal-aggregation-network-for-fast-mr","repo_url":"https://github.com/chunmeifeng/manet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"multi-modal-aggregation-network-for-fast-mr","repo_url":"https://github.com/chunmeifeng/mario","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}