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Updated: Jun 30, 2025
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Effects of adaptive or fixed thresholds and different platforms on the assessment of plaque characteristics using
G B John Mancini1, Craig Kamimura1, Eunice Yeoh1
1University of British Columbia, Division of Cardiology, Centre for Cardiovascular Innovation, Cardiovascular Imaging Research Core Laboratory (CIRCL), Vancouver, British Columbia, Canada.
Background:
Coronary computed tomography angiography (CCTA) is used to evaluate components of atherosclerosis. Either adaptive or diverse, fixed Hounsfield Units (HU) are used to define components such as low attenuation (LAP), mixed (MP) and calcified plaque (CP). Comparisons of different platforms and different thresholding approaches have not been extensively evaluated. We compare two fixed threshold options to an adaptive threshold option within a specific platform and to fixed threshold options measured with another platform.
Methods:
Coronary segments (n = 24) of good image quality, with well-defined boundaries and representing a broad range of atheroma were analyzed for LAP, MP and CP. Thresholds for LAP vs MP and MP vs CP were either Fixed30/350, Fixed75/350 or based on an automatically determined Adaptive option. Pearson correlation and Bland-Altman analyses were undertaken.
Results:
Within a single platform, measures were highly correlated irrespective of use of Adaptive or Fixed30/350 and Fixed75/350 thresholds (R ≥ 0.819, p < 0.000001). The correlation slope for measures of LAP progressively diminished comparing the Adaptive versus Fixed30/350 and the Fixed75/350 versus the Fixed30/350 approaches but bias was small. Between-platform comparisons yielded less optimal results, particularly with respect to measures of LAP and with one platform yielding both very small LAP volumes and very small ranges of volumes.
Conclusion:
Measures of plaque components are highly correlated irrespective of use of Adaptive or Fixed threshold approaches within a given platform. But measures are more affected by the specific proprietary algorithms employed than by specific thresholding options, especially for LAP.
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