Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images-Application in Brain Proton Therapy.

Journal: International journal of radiation oncology, biology, physics
Published Date: Nov 1, 2019

Abstract

PURPOSE: The first aim of this work is to present a novel deep convolution neural network (DCNN) multiplane approach and compare it to single-plane prediction of synthetic computed tomography (sCT) by using the real computed tomography (CT) as ground truth. The second aim is to demonstrate the feasibility of magnetic resonance imaging (MRI)-based proton therapy planning for the brain by assessing the range shift error within the clinical acceptance threshold.

Authors

  • Maria Francesca Spadea
    Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy.
  • Giampaolo Pileggi
    Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy; Biomedical Physics in Radiation Oncology, DKFZ-Deutsches Krebsforschungszentrum, Heidelberg, Germany.
  • Paolo Zaffino
    Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, 88100, Catanzaro, Italy.
  • Patrick Salome
    Biomedical Physics in Radiation Oncology, DKFZ-Deutsches Krebsforschungszentrum, Heidelberg, Germany.
  • Ciprian Catana
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts ccatana@mgh.harvard.edu.
  • David Izquierdo-Garcia
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts.
  • Francesco Amato
    Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.
  • Joao Seco
    Biomedical Physics in Radiation Oncology, DKFZ-Deutsches Krebsforschungszentrum, Heidelberg, Germany; Department of Physics and Astronomy, Heidelberg University, Germany. Electronic address: j.seco@dkfz.de.

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