Deep Learning CT Image Reconstruction in Clinical Practice.

Journal: RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
Published Date:

Abstract

BACKGROUND:  Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomical slice images from the raw output the scanner measures, several methods have been developed, with filtered back projection (FBP) and iterative reconstruction (IR) subsequently providing criterion standards. Currently there are new approaches to reconstruction in the field of artificial intelligence utilizing the upcoming possibilities of machine learning (ML), or more specifically, deep learning (DL).

Authors

  • Clemens Arndt
    Department of Radiology, Jena University Hospital, Jena, Germany.
  • Felix Güttler
    Department of Radiology, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
  • Andreas Heinrich
    Department of Radiology, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
  • Florian Bürckenmeyer
    Department of Radiology, Jena University Hospital, Jena, Germany.
  • Ioannis Diamantis
    Department of Radiology, Jena University Hospital, Jena, Germany.
  • Ulf Teichgräber
    Department of Radiology, Jena University Hospital - Friedrich Schiller University, Jena, Germany.