From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline Deployment Using the Gadgetron Platform
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912576224362496 |
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| author | Ning, Zihan Brackenier, Yannick McElroy, Sarah Silva, Sara Neves Cordero-Grande, Lucilio Rot, Sam Canas, Liane S. Thornley, Rebecca E Leitão, David Poccecai, Davide Cantell, Andrew Kerosi, Rene Price, Anthony N Cleary, Jon Tournier, Donald J Hutter, Jana Bridgen, Philippa Di Cio, Pierluigi Cleri, Michela Granlund, Inka Billimoria, Lucy Blunck, Yasmin Malik, Shaihan Modat, Marc Steves, Claire J Hajnal, Joseph V |
| author_facet | Ning, Zihan Brackenier, Yannick McElroy, Sarah Silva, Sara Neves Cordero-Grande, Lucilio Rot, Sam Canas, Liane S. Thornley, Rebecca E Leitão, David Poccecai, Davide Cantell, Andrew Kerosi, Rene Price, Anthony N Cleary, Jon Tournier, Donald J Hutter, Jana Bridgen, Philippa Di Cio, Pierluigi Cleri, Michela Granlund, Inka Billimoria, Lucy Blunck, Yasmin Malik, Shaihan Modat, Marc Steves, Claire J Hajnal, Joseph V |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline Deployment Using the Gadgetron Platform Ning, Zihan Brackenier, Yannick McElroy, Sarah Silva, Sara Neves Cordero-Grande, Lucilio Rot, Sam Canas, Liane S. Thornley, Rebecca E Leitão, David Poccecai, Davide Cantell, Andrew Kerosi, Rene Price, Anthony N Cleary, Jon Tournier, Donald J Hutter, Jana Bridgen, Philippa Di Cio, Pierluigi Cleri, Michela Granlund, Inka Billimoria, Lucy Blunck, Yasmin Malik, Shaihan Modat, Marc Steves, Claire J Hajnal, Joseph V Medical Physics 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. |
| title | From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline Deployment Using the Gadgetron Platform |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2509.06473 |