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From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline
Zihan Ning1, Yannick Brackenier1, Sarah McElroy1,2
1Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK.
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.
