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Enhancing Clinical MRI Perfusion Maps with Data-Driven Maps of Complementary Nature for Lesion Outcome Prediction

  • Adriano PintoEmail author
  • Sérgio Pereira
  • Raphael Meier
  • Victor Alves
  • Roland Wiest
  • Carlos A. Silva
  • Mauricio Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11072)

Abstract

Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient’s life. To perform the revascularization procedure, the decision making of physicians considers its risks and benefits based on multi-modal MRI and clinical experience. Therefore, automatic prediction of the ischemic stroke lesion outcome has the potential to assist the physician towards a better stroke assessment and information about tissue outcome. Typically, automatic methods consider the information of the standard kinetic models of diffusion and perfusion MRI (e.g. Tmax, TTP, MTT, rCBF, rCBV) to perform lesion outcome prediction. In this work, we propose a deep learning method to fuse this information with an automated data selection of the raw 4D PWI image information, followed by a data-driven deep-learning modeling of the underlying blood flow hemodynamics. We demonstrate the ability of the proposed approach to improve prediction of tissue at risk before therapy, as compared to only using the standard clinical perfusion maps, hence suggesting on the potential benefits of the proposed data-driven raw perfusion data modelling approach.

Notes

Acknowledgments

Adriano Pinto was supported by a scholarship from the Fundação para a Ciência e Tecnologia (FCT), Portugal (scholarship number PD/BD/113968/2015). This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Adriano Pinto
    • 1
    • 2
    Email author
  • Sérgio Pereira
    • 1
    • 2
  • Raphael Meier
    • 3
  • Victor Alves
    • 2
  • Roland Wiest
    • 3
  • Carlos A. Silva
    • 1
  • Mauricio Reyes
    • 4
  1. 1.CMEMS-UMinho Research UnitUniversity of MinhoGuimarãesPortugal
  2. 2.Centro AlgoritmiUniversity of MinhoBragaPortugal
  3. 3.Support Center for Advanced Neuroimaging - Institute for Diagnostic and Interventional NeuroradiologyUniversity Hospital Inselspital and University of BernBernSwitzerland
  4. 4.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland

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