{"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/general-phase-regularized-reconstruction","title":"General Phase Regularized Reconstruction using Phase Cycling","arxiv_id":"1709.05374","date":"2017-09-15","proceeding":null,"authors":["Frank Ong","Joseph Cheng","Michael Lustig"],"abstract":"Purpose: To develop a general phase regularized image reconstruction method,\nwith applications to partial Fourier imaging, water-fat imaging and flow\nimaging.\n  Theory and Methods: The problem of enforcing phase constraints in\nreconstruction was studied under a regularized inverse problem framework. A\ngeneral phase regularized reconstruction algorithm was proposed to enable\nvarious joint reconstruction of partial Fourier imaging, water-fat imaging and\nflow imaging, along with parallel imaging (PI) and compressed sensing (CS).\nSince phase regularized reconstruction is inherently non-convex and sensitive\nto phase wraps in the initial solution, a reconstruction technique, named phase\ncycling, was proposed to render the overall algorithm invariant to phase wraps.\nThe proposed method was applied to retrospectively under-sampled in vivo\ndatasets and compared with state of the art reconstruction methods.\n  Results: Phase cycling reconstructions showed reduction of artifacts compared\nto reconstructions with- out phase cycling and achieved similar performances as\nstate of the art results in partial Fourier, water-fat and divergence-free\nregularized flow reconstruction. Joint reconstruction of partial Fourier +\nwater-fat imaging + PI + CS, and partial Fourier + divergence-free regularized\nflow imaging + PI + CS were demonstrated.\n  Conclusion: The proposed phase cycling reconstruction provides an alternative\nway to perform phase regularized reconstruction, without the need to perform\nphase unwrapping. It is robust to the choice of initial solutions and\nencourages the joint reconstruction of phase imaging applications.","url_abs":"http://arxiv.org/abs/1709.05374v1","url_pdf":"http://arxiv.org/pdf/1709.05374v1.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":"general-phase-regularized-reconstruction","repo_url":"https://github.com/mikgroup/phase_cycling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}