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Neurosense: deep sensing of full or near-full coverage head/brain scans in human magnetic resonance imaging

  • Baris KanberEmail author
  • James Ruffle
  • Jorge Cardoso
  • Sebastien Ourselin
  • Olga Ciccarelli
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Introduction

The application of automated algorithms to imaging requires knowledge of its content, a curatorial task, for which we ordinarily rely on the Digital Imaging and Communications in Medicine (DICOM) header as the only source of image meta-data. However, identifying brain MRI scans that have full or near-full coverage among a large number (e.g. >5000) of scans comprising both head/brain and other body parts is a time-consuming task that cannot be automated with the use of the information stored in the DICOM header attributes alone. Depending on the clinical scenario, an entire set of scans acquired in a single visit may often be labelled “BRAIN” in the DICOM field 0018,0015 (Body Part Examined), while the individual scans will often not only include brain scans with full coverage, but also others with partial brain coverage, scans of the spinal cord, and in some cases other body parts. DICOM field 0018,1250 (Receive Coil Name)can be used to determine the type of receiver...

Keywords

Neurosense MRI Classification Sensing Detection Head Brain 

Notes

Acknowledgements

We are grateful to Professor Frederik Barkhof, Professor Daniel Alexander, Dr. Parashkev Nachev, Dr. Robert Gray, Dr. Ferran Prados, and Dr. Eugenio Iglesias for their help and support of this study, and for providing comments on a previous versions of the article. The data used were provided in part by OASIS and were part based upon data generated by the TCGA Research Network. This work was funded by the National Institute for Health Research University College London Hospitals Biomedical Research Centre, and the Wellcome Trust.

Compliance with Ethical Standards

This study was approved by the University College London Hospitals NHS Trust. The study was classified as a service evaluation and optimization project using irrevocably anonymized data, which does not require ethical approval or consent.

Information Sharing Statement

Neurosense is available for use as a virtual machine from CMIClab. The datasets used during development are publicly available except for the UCLH dataset which is not a public database.

Conflict of Interest

The authors declare that they have no conflicts of interest.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.UCL Centre for Medical Image ComputingUniversity College LondonLondonUK
  2. 2.Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
  3. 3.National Institute for Health Research University College London Hospitals Biomedical Research CentreLondonUK
  4. 4.Department of RadiologyUniversity College London Hospitals NHS Foundation TrustLondonUK
  5. 5.School of Biomedical Engineering & Imaging SciencesKing’s College LondonLondonUK
  6. 6.Department of Neuroinflammation, Queen Square Institute of NeurologyUniversity College LondonLondonUK

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