From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline Deployment Using the Gadgetron Platform
Abstract
Purpose: To develop and validate a practical framework to overcome common issues in inline deployment of established offline MR reconstruction, including (1) delay from lengthy reconstructions, (2) limited support for multi-scan input reconstructions, (3) the need to adapt scripts for different raw formats, and (4) limited guidance and experience in retaining scanner reconstructions and applying scanner-based post-processing to custom outputs. Methods: The framework builds upon the Gadgetron platform and includes: (1) an input converter to transform ISMRMRD format raw into a Siemens format raw structure, facilitating reuse of existing code; (2) an asynchronous trigger-and-retrieve mechanism enabling long reconstructions without delaying scanner processes; (3) resource-aware scheduling for parallel execution; (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 has been shown, and all reconstructions were successfully executed inline without disrupting scanner workflows. Images were retrieved via automated or retro-reconstruction, with scanner-based post-processing applied to custom outputs. Multi-scan input reconstructions were executed using GPU-aware scheduling, confirming feasibility for routine and large-scale applications. In 480 consecutive 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.
Cite
@article{arxiv.2509.06473,
title = {From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline Deployment Using the Gadgetron Platform},
author = {Zihan Ning and Yannick Brackenier and Sarah McElroy and Sara Neves Silva and Lucilio Cordero-Grande and Sam Rot and Liane S. Canas and Rebecca E Thornley and David Leitão and Davide Poccecai and Andrew Cantell and Rene Kerosi and Anthony N Price and Jon Cleary and Donald J Tournier and Jana Hutter and Philippa Bridgen and Pierluigi Di Cio and Michela Cleri and Inka Granlund and Lucy Billimoria and Yasmin Blunck and Shaihan Malik and Marc Modat and Claire J Steves and Joseph V Hajnal},
journal= {arXiv preprint arXiv:2509.06473},
year = {2025}
}
Comments
17 pages, 5 figures