Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

Journal: European radiology
Published Date: Jan 1, 2024

Abstract

OBJECTIVES: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep neural network to optimize the delimitation of scan ranges in CT localizers to reduce the radiation dose.

Authors

  • Aydin Demircioglu
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
  • Denise Bos
    Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
  • Ender Demircioğlu
    Department of Thoracic and Cardiovascular Surgery, West German Heart and Vascular Centre Essen, University Hospital Essen, 45147, Essen, Germany.
  • Sahar Qaadan
    Department of Mechatronics and Artificial Intelligence Engineering, German Jordanian University, Madaba, JO-11180, Jordan.
  • Tobias Glasmachers
  • Oliver Bruder
    Department of Cardiology and Angiology, Contilia Heart and Vascular Center, Elisabeth-Krankenhaus Essen, 45138, Essen, Germany.
  • Lale Umutlu
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, Departments of.
  • Kai Nassenstein
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany.

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