P1777Non-ECG triggered CT assessment of coronary calcium does not equate to cardiac-gated true Agatston calcium scoring

Schuzer, John L.; Shanbhag, Sujata M.; Bandettini, W. Patricia; Chen, M Y

doi:10.1093/eurheartj/ehy565.p1777

Cited by 1 publication

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“…First, cardiac or respiratory motion artifacts could affect the reproducibility of CS 33 and nongated scans significantly underestimated the AS score by 60.8%. 34 Second, the variability of CT imaging parameters may affect CS significantly. For slice thickness, a unanimous 3 mm slice thickness was used for both CCT and ChCT, as thinner slice thickness is thought to have less variability and higher scoring in quantifying CAC in ChCT.…”

Section: Discussionmentioning

confidence: 99%

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The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels

Lin,

Peng

et al. 2023

Journal of Thoracic Imaging

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Purpose: To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels. Patients and Methods: Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CSCCT, by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CSAICCT, by AI from the standard CCT; CSChCTsoft, by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CSChCTsharp, by AI from CCT image with same parameters for CSChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland–Altman plots, and weighted kappa analysis (κ). Results: The CSAICCT was consistent with CSCCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CSChCTsoft and CSChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland–Altman mean difference and 95% upper and lower limits of agreements were 329.1 [–798.9 to 1457] and 335.3 [–651.9 to 1322], respectively). The CV risk category agreement between CSChCTsoft and CSChCTsharp was moderate (κ = 0.556 and 0.537, respectively). Conclusions: There was an excellent correlation between CSCCT and CSAICCT, with excellent agreement between CV risk categories. There was also a good correlation between CSCCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.

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Section: Discussionmentioning

confidence: 99%

“…This can be elucidated by the following facts. First, cardiac or respiratory motion artifacts could affect the reproducibility of CS 33 and nongated scans significantly underestimated the AS score by 60.8% 34 . Second, the variability of CT imaging parameters may affect CS significantly.…”

Section: Discussionmentioning

confidence: 99%

The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels

Lin,

Peng

et al. 2023

Journal of Thoracic Imaging

View full text Add to dashboard Cite

show abstract

P1777Non-ECG triggered CT assessment of coronary calcium does not equate to cardiac-gated true Agatston calcium scoring

Cited by 1 publication

References 0 publications

The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels

The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels

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