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Traumatic Brain Lesion Quantification Based on Mean Diffusivity Changes

  • Christophe Maggia
  • Thomas Mistral
  • Senan Doyle
  • Florence Forbes
  • Alexandre Krainik
  • Damien Galanaud
  • Emmanuelle Schmitt
  • Stéphane Kremer
  • Irène Troprès
  • Emmanuel L. Barbier
  • Jean-François Payen
  • Michel DojatEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)

Abstract

We report the evaluation of an automated method for quantification of brain tissue damage, caused by a severe traumatic brain injury, using mean diffusivity computed from MR diffusion images. Our automatic results obtained on realistic phantoms and real patient images 10 days post-event provided by nine different centers were coherent with four expert manually identified lesions. For realistic phantoms automated method scores were equal to 0.77, 0.77 and 0.83 for Dice, Precision and Sensibility respectively compared to 0.78, 0.72 and 0.86 for the experts. The inter correlation class (ICC) was 0.79. For 7/9 real cases 0.57, 0.50 and 0.70 were respectively obtained for automated method compared to 0.60, 0.52 and 0.78 for experts with ICC = 0.71. Additionally, we detail the quality control module used to pool data from various image provider centers. This study clearly demonstrates the validity of the proposed automated method to eventually compute in a multi-centre project, the lesional load following brain trauma based on MD changes.

Notes

Acknowledgments

Grenoble MRI facility IRMaGe was partly funded by the French program Investissement d’avenir run by the Agence Nationale pour la Recherche; grant Infrastructure d’avenir en Biologie Santé - ANR-11-INBS-0006. Research funded by French ministry of research and education under the Projet Hospitalier de Recherche Clinique grant OXY-TC to JFP.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Christophe Maggia
    • 1
    • 2
    • 3
  • Thomas Mistral
    • 1
    • 2
    • 3
  • Senan Doyle
    • 4
  • Florence Forbes
    • 2
    • 5
  • Alexandre Krainik
    • 1
    • 2
    • 3
  • Damien Galanaud
    • 6
  • Emmanuelle Schmitt
    • 7
  • Stéphane Kremer
    • 8
  • Irène Troprès
    • 2
    • 3
    • 9
    • 10
  • Emmanuel L. Barbier
    • 1
    • 2
  • Jean-François Payen
    • 1
    • 2
    • 3
  • Michel Dojat
    • 1
    • 2
    Email author
  1. 1.INSERM, U1216GrenobleFrance
  2. 2.Université Grenoble Alpes, GINGrenobleFrance
  3. 3.CHUGAGrenobleFrance
  4. 4.PixylGrenobleFrance
  5. 5.Inria, MISTISMontbonnotFrance
  6. 6.APHP, Hopital Pitié SalpétrièreParisFrance
  7. 7.CHU, Hopital CentralNancyFrance
  8. 8.CHU de StrasbourgStrasbourgFrance
  9. 9.CNRS, UMR 3552GrenobleFrance
  10. 10.INSERM, U17GrenobleFrance

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