Claire J Steves | Searcharxiv

arXiv2025

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

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.

physics.med-ph↗

arXiv2023

Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets

Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.

stat.AP↗