{"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/scan-specific-parameter-free-artifact","title":"Scan-specific, Parameter-free Artifact Reduction in K-space (SPARK)","arxiv_id":"1911.07219","date":"2019-11-17","proceeding":null,"authors":[],"abstract":"We propose a convolutional neural network (CNN) approach that works\nsynergistically with physics-based reconstruction methods to reduce artifacts\nin accelerated MRI. Given reconstructed coil k-spaces, our network predicts a\nk-space correction term for each coil. This is done by matching the difference\nbetween the acquired autocalibration lines and their erroneous reconstructions,\nand generalizing this error term over the entire k-space. Application of this\napproach on existing reconstruction methods show that SPARK suppresses\nreconstruction artifacts at high acceleration, while preserving and improving\non detail in moderate acceleration rates where existing reconstruction\nalgorithms already perform well; indicating robustness. Introduction Parallel","url_abs":"http://arxiv.org/abs/1911.07219v1","url_pdf":"http://arxiv.org/pdf/1911.07219v1.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":"scan-specific-parameter-free-artifact","repo_url":"https://github.com/bekeronur/SPARK","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}