{"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/learning-a-variational-network-for","title":"Learning a Variational Network for Reconstruction of Accelerated MRI Data","arxiv_id":"1704.00447","date":"2017-04-03","proceeding":null,"authors":["Kerstin Hammernik","Teresa Klatzer","Erich Kobler","Michael P. Recht","Daniel K. Sodickson","Thomas Pock","Florian Knoll"],"abstract":"Purpose: To allow fast and high-quality reconstruction of clinical\naccelerated multi-coil MR data by learning a variational network that combines\nthe mathematical structure of variational models with deep learning.\n  Theory and Methods: Generalized compressed sensing reconstruction formulated\nas a variational model is embedded in an unrolled gradient descent scheme. All\nparameters of this formulation, including the prior model defined by filter\nkernels and activation functions as well as the data term weights, are learned\nduring an offline training procedure. The learned model can then be applied\nonline to previously unseen data.\n  Results: The variational network approach is evaluated on a clinical knee\nimaging protocol. The variational network reconstructions outperform standard\nreconstruction algorithms in terms of image quality and residual artifacts for\nall tested acceleration factors and sampling patterns.\n  Conclusion: Variational network reconstructions preserve the natural\nappearance of MR images as well as pathologies that were not included in the\ntraining data set. Due to its high computational performance, i.e.,\nreconstruction time of 193 ms on a single graphics card, and the omission of\nparameter tuning once the network is trained, this new approach to image\nreconstruction can easily be integrated into clinical workflow.","url_abs":"http://arxiv.org/abs/1704.00447v1","url_pdf":"http://arxiv.org/pdf/1704.00447v1.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":"learning-a-variational-network-for","repo_url":"https://github.com/liuvictoria/multiTaskLearning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"learning-a-variational-network-for","repo_url":"https://github.com/visva89/varnetrecon","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"learning-theory","task_name":"Learning Theory"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.00447","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}