{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T21:11:43Z","timestamp":1783199503061,"version":"3.54.6"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100011721","name":"Chronic Disease Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011721","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012041","name":"Versus Arthritis","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012041","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013524","name":"Cystinosis Research Network","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013524","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014461","name":"Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014461","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2026,7]]},"abstract":"ABSTRACT<\/jats:title>\n \n Purpose<\/jats:title>\n To develop and validate a practical, open\u2010source framework to overcome common issues in inline deployment of established offline MR reconstruction, including (1) scan disruption from lengthy reconstructions, (2) limited support for multi\u2010scan input reconstructions, (3) needs to adapt scripts for different raw\u2010data formats, and (4) limited guidance and experience in retaining scanner reconstructions and applying scanner\u2010based post\u2010processing to custom\u2010reconstructed images.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n The framework builds upon the Gadgetron platform as implemented on Siemens scanners and includes: (1) a general input converter to convert Gadgetron\u2010used ISMRMRD format raw into a Siemens format raw structure, facilitating reuse of code; (2) an asynchronous trigger\u2010and\u2010retrieve mechanism enabling long custom reconstructions without delaying scanner processes; (3) resource\u2010aware scheduling for parallel execution of reconstructions; (4) integrated file management to support multi\u2010scan inputs; and (5) preservation of scanner\u2010based reconstructions and post\u2010processing. The framework was validated on 2 Siemens scanners for SENSE, AlignedSENSE, and NUFFT reconstructions, and in a large\u2010cohort study.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Minimum code modification for inline deployment was demonstrated, and all reconstructions were successfully executed inline without disrupting scanner workflows. Images were retrieved automatically via retrieval scans or manually via retro\u2010reconstruction, with scanner\u2010based post\u2010processing applied to custom outputs. Multi\u2010sequence reconstructions were executed using GPU\u2010aware scheduling, confirming feasibility for large\u2010scale applications. In 480 examinations, inline reconstructions were retrieved in 99% of cases without disruptions.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The framework lowers the technical barrier to inline deployment of offline reconstructions, enabling robust, scalable, and post\u2010processing\u2010compatible integration. It is openly available with documentation and demonstration cases to support reproducibility and community adoption.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/mrm.70304","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T06:00:00Z","timestamp":1775109600000},"page":"448-459","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From Offline to Inline Without Pain: A Practical Framework for Translating Offline\n MR<\/scp>\n Reconstructions to Inline Deployment Using the Gadgetron Platform"],"prefix":"10.1002","volume":"96","author":[{"given":"Zihan","family":"Ning","sequence":"first","affiliation":[{"name":"Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences Kings College London London 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