What question did this study set out to answer?

To develop an open-source framework that streamlines the inline deployment of MR reconstructions while addressing common issues.

April 3, 2026Open Access

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

Key Points

To develop an open-source framework that streamlines the inline deployment of MR reconstructions while addressing common issues.
Utilized the Gadgetron platform on Siemens scanners for validation.
Developed a general input converter for raw data format adaptation.
Implemented an asynchronous trigger-and-retrieve mechanism for custom reconstructions.
Established resource-aware scheduling for parallel reconstruction execution.
Integrated file management to handle multi-scan input.
Achieved 99% successful inline reconstructions without disrupting scanner workflows.
Executed all MR reconstructions inline with minimal code changes required.
Confirmed feasibility for large-scale applications using GPU-aware scheduling.
Successfully applied scanner-based post-processing to custom outputs.

Abstract

ABSTRACT 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.

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