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Insights into Imaging

, Volume 6, Issue 3, pp 393–401

First Online: 16 December 2014Received: 15 April 2014Revised: 16 November 2014Accepted: 21 November 2014


ObjectivesTo evaluate visual grading characteristics VGC and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment VGA, traditionally employed to score anatomical visualisation.

MethodsPatient images n = 66 were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists n = 6 performed VGA followed by VGC and ordinal regression analysis.

ResultsVGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations including trauma in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases.

ConclusionsThe combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings.

Main Messages• There is need for scientifically based image quality evaluation during CT optimisation.

• VGC and ordinal regression analysis in combination led to better informed clinical decisions.

• VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

KeywordsCT Optimisation Image quality Visual grading characteristics Ordinal regression  Download fulltext PDF

Autor: Francis Zarb - Mark F. McEntee - Louise Rainford


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