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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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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.

Magnetic Resonance in Medicine
|April 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an open-source framework for seamless inline deployment of magnetic resonance (MR) reconstructions, overcoming common challenges and ensuring workflow integration. The framework enables robust, scalable, and reproducible MR imaging analysis.

Keywords:
MR translationinline MR reconstructionopen platform for reconstruction

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Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)
  • Computational Imaging

Background:

  • Offline magnetic resonance (MR) reconstruction methods face challenges during inline deployment.
  • Common issues include scan disruption, limited multi-scan support, data format adaptation, and post-processing integration.

Purpose of the Study:

  • To develop and validate a practical, open-source framework for inline deployment of established offline MR reconstruction techniques.
  • To address scan disruption, multi-scan input limitations, data format variability, and scanner post-processing integration.

Main Methods:

  • The framework utilizes the Gadgetron platform on Siemens scanners.
  • Key features include an ISMRMRD to Siemens raw format converter, asynchronous trigger-and-retrieve, resource-aware scheduling, and integrated file management.
  • Validation was performed on two Siemens scanners across SENSE, AlignedSENSE, and NUFFT reconstructions.

Main Results:

  • The framework demonstrated minimal code modification for inline deployment, successfully executing reconstructions without disrupting scanner workflows.
  • Automated and manual image retrieval was achieved, with scanner-based post-processing applied to custom outputs.
  • Multi-sequence reconstructions were feasible for large-scale applications, with 99% of inline reconstructions retrieved successfully in 480 examinations.

Conclusions:

  • The developed framework significantly reduces the technical barrier for inline deployment of offline MR reconstructions.
  • It offers a robust, scalable, and post-processing-compatible solution for integrated MR imaging analysis.
  • The open-source availability with documentation promotes reproducibility and community adoption.