The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review
Creators
- 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.2021.07.010Additional 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
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 53124000
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- CAT SCANNING; EDUCATION; IMAGES; MACHINE LEARNING; OPTIMIZATION; PATIENTS; RADIATION DOSES; RADIATION HAZARDS; RADIOLOGICAL PERSONNEL; REVIEWS
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTERIZED TOMOGRAPHY; DIAGNOSTIC TECHNIQUES; DOCUMENT TYPES; DOSES; HAZARDS; HEALTH HAZARDS; LEARNING; MATHEMATICAL LOGIC; MEDICAL PERSONNEL; PERSONNEL; TOMOGRAPHY
Optional Information
- Copyright
- Copyright (c) 2021 The College of Radiographers. Published by Elsevier Ltd.