Published February 2022 | Version v1
Journal article

The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review

  • 1. Metropolia University of Applied Sciences (Finland)
  • 2. Singapore Institute of Technology (SIT) (Singapore)

Description

Highlights: • Less dose may lower patient radiation risk but may impact image quality of CT scans. • Artificial intelligence technologies can improve image quality in low-dose CT scans. • Different deep learning models have been developed to facilitate dose optimisation in low-dose CT. • Radiologists and radiographers should have proper education and knowledge about the techniques used. Low-dose computed tomography tends to produce lower image quality than normal dose computed tomography (CT) although it can help to reduce radiation hazards of CT scanning. Research has shown that Artificial Intelligence (AI) technologies, especially deep learning can help enhance the image quality of low-dose CT by denoising images. This scoping review aims to create an overview on how AI technologies, especially deep learning, can be used in dose optimisation for low-dose CT.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.radi.2021.07.010

Additional details

Identifiers

DOI
10.1016/j.radi.2021.07.010;
PII
S1078817421000900;

Publishing Information

Journal Title
Radiography (London 1995)
Journal Volume
28
Journal Issue
1
Journal Page Range
p. 208-214
ISSN
1078-8174

Optional Information

Copyright
Copyright (c) 2021 The College of Radiographers. Published by Elsevier Ltd.