Independent brain F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks.

Journal: Hellenic journal of nuclear medicine
Published Date: Oct 7, 2019

Abstract

OBJECTIVE: Attenuation correction (AC) of positron emission tomography (PET) data poses a challenge when no transmission data or computed tomography (CT) data are available, e.g. in stand alone PET scanners or PET/magnetic resonance imaging (MRI). In these cases, external imaging data or morphological imaging data are normally used for the generation of attenuation maps. Newly introduced machine learning methods however may allow for direct estimation of attenuation maps from non attenuation-corrected PET data (PET). Our purpose was thus to establish and evaluate a method for independent AC of brain fluorine-18-fluorodeoxyglucose (F-FDG) PET images only based on PETNAC using Generative Adversarial Networks (GAN).

Authors

  • Karim Armanious
    Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany.
  • Thomas Küstner
    Department of Radiology, Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, Germany.
  • Matthias Reimold
  • Konstantin Nikolaou
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen, Germany.
  • Christian la Fougère
    Department of Radiology, Nuclear Medicine, Eberhard Karls University Tübingen, Germany.
  • Bin Yang
    School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, PR China. Electronic address: yangbin@dlut.edu.cn.
  • Sergios Gatidis
    Department of Radiology, Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, Germany.

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