{"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/decomposition-based-variational-network-for","title":"Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and Reconstruction","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Pengcheng Lei","Faming Fang","Guixu Zhang","Tieyong Zeng"],"abstract":"    Multi-contrast MRI super-resolution (SR) and reconstruction methods aim to explore complementary information from the reference image to help the reconstruction of the target image. Existing deep learning-based methods usually manually design fusion rules to aggregate the multi-contrast images, fail to model their correlations accurately and lack certain interpretations. Against these issues, we propose a multi-contrast variational network (MC-VarNet) to explicitly model the relationship of multi-contrast images. Our model is constructed based on an intuitive motivation that multi-contrast images have consistent (edges and structures) and inconsistent (contrast) information. We thus build a model to reconstruct the target image and decompose the reference image as a common component and a unique component. In the feature interaction phase, only the common component is transferred to the target image. We solve the variational model and unfold the iterative solutions into a deep network. Hence, the proposed method combines the good interpretability of model-based methods with the powerful representation ability of deep learning-based methods. Experimental results on the multi-contrast MRI reconstruction and SR demonstrate the effectiveness of the proposed model. Especially, since we explicitly model the multi-contrast images, our model is more robust to the reference images with noises and large inconsistent structures. The code is available at https://github.com/lpcccc-cv/MC-VarNet.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Lei_Decomposition-Based_Variational_Network_for_Multi-Contrast_MRI_Super-Resolution_and_Reconstruction_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Lei_Decomposition-Based_Variational_Network_for_Multi-Contrast_MRI_Super-Resolution_and_Reconstruction_ICCV_2023_paper.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":"decomposition-based-variational-network-for","repo_url":"https://github.com/lpcccc-cv/mc-varnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"mri-reconstruction","task_name":"MRI Reconstruction"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[{"method_slug":"fail","method_name":"fail"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}