Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization.

Journal: European journal of radiology
Published Date: Nov 11, 2022

Abstract

PURPOSE: Extracting water equivalent diameter (DW), as a good descriptor of patient size, from the CT localizer before the spiral scan not only minimizes truncation errors due to the limited scan field-of-view but also enables prior size-specific dose estimation as well as scan protocol optimization. This study proposed a unified methodology to measure patient size, shape, and attenuation parameters from a 2D anterior-posterior localizer image using deep learning algorithms without the need for labor-intensive vendor-specific calibration procedures.

Authors

  • Yazdan Salimi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
  • Isaac Shiri
    Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Azadeh Akhavanallaf
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
  • Zahra Mansouri
    Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • AmirHosein Sanaat
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.
  • Masoumeh Pakbin
    Imaging Department, Qom University of Medical Sciences, Qum, Iran.
  • Mohammadreza Ghasemian
    Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qum, Iran.
  • Hossein Arabi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

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