From Offline to Inline Without Pain: A Practical Framework for Translating Offline <scp>MR</scp> Reconstructions to Inline Deployment Using the Gadgetron Platform

PURPOSE: To develop and validate a practical, open-source 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-scan input reconstructions, (3) needs to adapt scripts for different raw-data formats, and (4) limited guidance and experience in retaining scanner reconstructions and applying scanner-based post-processing to custom-reconstructed images. METHODS: The framework builds upon the Gadgetron platform as implemented on Siemens scanners and includes: (1) a general input converter to convert Gadgetron-used ISMRMRD format raw into a Siemens format raw structure, facilitating reuse of code; (2) an asynchronous trigger-and-retrieve mechanism enabling long custom reconstructions without delaying scanner processes; (3) resource-aware scheduling for parallel execution of reconstructions; (4) integrated file management to support multi-scan inputs; and (5) preservation of scanner-based reconstructions and post-processing. The framework was validated on 2 Siemens scanners for SENSE, AlignedSENSE, and NUFFT reconstructions, and in a large-cohort study. RESULTS: 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-reconstruction, with scanner-based post-processing applied to custom outputs. Multi-sequence reconstructions were executed using GPU-aware scheduling, confirming feasibility for large-scale applications. In 480 examinations, inline reconstructions were retrieved in 99% of cases without disruptions. CONCLUSION: The framework lowers the technical barrier to inline deployment of offline reconstructions, enabling robust, scalable, and post-processing-compatible integration. It is openly available with documentation and demonstration cases to support reproducibility and community adoption.