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Contrast-to-Noise Ratio Optimization in Coronary Computed Tomography Angiography: Validation in a Swine Model.
- Source :
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Academic radiology [Acad Radiol] 2019 Jun; Vol. 26 (6), pp. e115-e125. Date of Electronic Publication: 2018 Aug 30. - Publication Year :
- 2019
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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