Low-dose chest CT and the impact on nodule visibility

Tugwell-Allsup, J, Owen, BW and England, A ORCID: https://orcid.org/0000-0001-6333-7776 2021, 'Low-dose chest CT and the impact on nodule visibility' , Radiography, 27 (1) , pp. 24-30.

Full text not available from this repository.


The need to continually optimise CT protocols is essential to ensure the lowest possible radiation dose for the clinical task and individual patient. The aim of this study was to explore the effect of reducing effective mAs on nodule detection and radiation dose across six scanners. An anthropomorphic chest phantom was scanned using a low-dose chest CT protocol, with the effective mAs lowered to the lowest permissible level. All other acquisition parameters remained consistent. Images were evaluated by five radiologists to determine their sensitivity in detecting six simulated nodules within the phantom. Image noise was calculated together with DLP. The lowest possible mAs achievable ranged from 7 to 19 mAs. The two highest mAs setting (17 mAs + 19 mAs) had kV modulation enabled (100 kV instead of 120 kV) which consequently resulted in a higher nodule detection rate. Overall nodule detection averaged at 91% (range 80-97%). Out of a possible 180 nodules, 16 were missed, with 12 of those 16 being the same nodule. Noise was double for the Somatom Sensation scanner when compared to the others; however, this scanner did not have iterative reconstruction and it was installed over 10 years ago. There was a strong correlation between image noise and scanner age. This study highlighted that nodules can be detected at very low effective mAs (<20 mAs) but only when other acquisition parameters are optimised i.e. iterative reconstruction and kV modulation. Nodule detection rates were affected by nodule location and image noise. This study consolidates previous findings on how to successfully optimise low-dose chest CT. It also highlights the difficulty with standardisation owing to factors such as scanner age and different vendor attributes. [Abstract copyright: Crown Copyright © 2020. Published by Elsevier Ltd. All rights reserved.]

Item Type: Article
Additional Information: ** From PubMed via Jisc Publications Router **Journal IDs: eissn 1532-2831 **Article IDs: pubmed: 32499090; pii: S1078-8174(20)30076-6 **History: accepted 08-05-2020; revised 07-05-2020; submitted 24-02-2020
Schools: Schools > School of Health and Society
Journal or Publication Title: Radiography
Publisher: Elsevier
ISSN: 1078-8174
Related URLs:
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 22 Jun 2020 12:47
Last Modified: 27 Aug 2021 21:41
URI: http://usir.salford.ac.uk/id/eprint/57338

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)