A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET-CT scans.

Journal: Medical physics
Published Date: Jan 6, 2020

Abstract

PURPOSE: The purpose of this work was to assess the potential of deep convolutional neural networks in automated measurement of cerebellum tracer uptake in F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) scans.

Authors

  • Xiaofan Xiong
    Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, 52242, USA.
  • Timothy J Linhardt
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA.
  • Weiren Liu
    Roy J. and Lucille A. Carver College of Medicine, The University of Iowa, Iowa City, IA, 52242, USA.
  • Brian J Smith
    Department of Biostatistics, University of Iowa, 145 N. Riverside Drive, 100 CPHB, Iowa City, IA, 52242, USA.
  • Wenqing Sun
    Department of Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA.
  • Christian Bauer
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA.
  • John J Sunderland
    Department of Radiology, The University of Iowa, Iowa City, IA, 52242, USA.
  • Michael M Graham
    Department of Radiology, The University of Iowa, Iowa City, IA, 52242, USA.
  • John M Buatti
    Department of Radiation Oncology, Carver College of Medicine, University of Iowa Carver College of Medicine, LL-W Pomerantz Family Pavilion, 200 Hawkins Drive, Iowa City, IA, 52242-1089, USA.
  • Reinhard R Beichel
    Iowa Institute for Biomedical Imaging, Department of Electrical and Computer Engineering, Department of Internal Medicine, The University of Iowa, Iowa City, 52242, IA, USA.

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