Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.

Journal: International journal of computer assisted radiology and surgery
Published Date: Mar 22, 2023

Abstract

PURPOSE: The usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening allergic-like reactions, and environmental contamination. Deep learning (DL) models can generate full-dose ICM CT images from non-contrast or low-dose ICM administration or generate non-contrast CT from full-dose ICM CT. Eliminating the need for both contrast-enhanced and non-enhanced imaging or reducing the amount of required contrast while maintaining diagnostic capability may reduce overall patient risk, improve efficiency and minimize costs. We reviewed the current capabilities of DL to reduce the need for contrast administration in CT.

Authors

  • Ghazal Azarfar
    Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK, Canada. azarfar.g@gmail.com.
  • Seok-Bum Ko
    University of Saskatchewan, Department of Electrical and Computer Engineering, 57 Campus Drive, Saskatoon, Canada S7N 5A9. Electronic address: seokbum.ko@usask.ca.
  • Scott J Adams
    College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. Electronic address: scott.adams@usask.ca.
  • Paul S Babyn
    Department of Medical Imaging, University of Saskatchewan and Saskatoon Health Region, Saskatoon, Saskatchewan, Canada.

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