{"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/i2i-mamba-multi-modal-medical-image-synthesis","title":"I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling","arxiv_id":"2405.14022","date":"2024-05-22","proceeding":null,"authors":["Omer F. Atli","Bilal Kabas","Fuat Arslan","Arda C. Demirtas","Mahmut Yurt","Onat Dalmaz","Tolga Çukur"],"abstract":"Multi-modal medical image synthesis involves nonlinear transformation of tissue signals between source and target modalities, where tissues exhibit contextual interactions across diverse spatial distances. As such, the utility of a network architecture in synthesis depends on its ability to express these contextual features. Convolutional neural networks (CNNs) offer high local precision at the expense of poor sensitivity to long-range context. While transformers promise to alleviate this issue, they suffer from an unfavorable trade-off between sensitivity to long- versus short-range context due to the intrinsic complexity of attention filters. To effectively capture contextual features while avoiding the complexity-driven trade-offs, here we introduce a novel multi-modal synthesis method, I2I-Mamba, based on the state space modeling (SSM) framework. Focusing on semantic representations across a hybrid residual architecture, I2I-Mamba leverages novel dual-domain Mamba (ddMamba) blocks for complementary contextual modeling in image and Fourier domains, while maintaining spatial precision with convolutional layers. Diverting from conventional raster-scan trajectories, ddMamba leverages novel SSM operators based on a spiral-scan trajectory to learn context with enhanced radial coverage and angular isotropy, and a channel-mixing layer to aggregate context across the channel dimension. Comprehensive demonstrations on multi-contrast MRI and MRI-CT protocols indicate that I2I-Mamba offers superior performance against state-of-the-art CNNs, transformers and SSMs.","url_abs":"https://arxiv.org/abs/2405.14022v5","url_pdf":"https://arxiv.org/pdf/2405.14022v5.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":"i2i-mamba-multi-modal-medical-image-synthesis","repo_url":"https://github.com/icon-lab/I2I-Mamba","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"mamba","task_name":"Mamba"},{"task_slug":"sensitivity","task_name":"Sensitivity"}],"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}