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Contrast-to-Noise Ratio Optimization in Coronary Computed Tomography Angiography: Validation in a Swine Model.

Authors :
Hubbard L
Malkasian S
Zhao Y
Abbona P
Molloi S
Source :
Academic radiology [Acad Radiol] 2019 Jun; Vol. 26 (6), pp. e115-e125. Date of Electronic Publication: 2018 Aug 30.
Publication Year :
2019

Abstract

Rationale and Objectives: The accuracy of coronary computed tomography (CT) angiography depends upon the degree of coronary enhancement as compared to the background noise. Unfortunately, coronary contrast-to-noise ratio (CNR) optimization is difficult on a patient-specific basis. Hence, the objective of this study was to validate a new combined diluted test bolus and CT angiography protocol for improved coronary enhancement and CNR.<br />Materials and Methods: The combined diluted test bolus and CT angiography protocol was validated in six swine (28.9 ± 2.7 kg). Specifically, the aortic and coronary enhancement and CNR of a standard CT angiography protocol, and a new combined diluted test bolus and CT angiography protocol were compared to a reference retrospective CT angiography protocol. Comparisons for all data were made using box plots, t tests, regression, Bland-Altman, root-mean-square error and deviation, as well as Lin's concordance correlation.<br />Results: The combined diluted test bolus and CT angiography protocol was found to improve aortic and coronary enhancement by 26% and 13%, respectively, as compared to the standard CT angiography protocol. More importantly, the combined protocol was found to improve aortic and coronary CNR by 29% and 20%, respectively, as compared to the standard protocol.<br />Conclusion: A new combined diluted test bolus and CT angiography protocol was shown to improve coronary enhancement and CNR as compared to an existing standard CT angiography protocol.<br /> (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1878-4046
Volume :
26
Issue :
6
Database :
MEDLINE
Journal :
Academic radiology
Publication Type :
Academic Journal
Accession number :
30172714
Full Text :
https://doi.org/10.1016/j.acra.2018.06.026