Systematic analysis on the relationship between luminal enhancement, convolution kernel, plaque density, and luminal diameter of coronary artery stenosis: a CT phantom study

Abstract

To systematically investigate into the relationships between luminal enhancement, convolution kernel, plaque density, and stenosis severity in coronary computed tomography (CT) angiography. A coronary phantom including 63 stenoses (stenosis severity, 10-90 %; plaque densities, -100 to 1,000 HU) was loaded with increasing solutions of contrast material (luminal enhancement, 0-700 HU) and scanned in an anthropomorphic chest. CT data was acquired with prospective triggering using 64-section dual-source CT; reconstructions were performed with soft-tissue (B26f) and sharp convolution kernels (B46f). Two blinded and independent readers quantitatively assessed luminal diameter and CT number of plaque using electronic calipers. Measurement bias between phantom dimensions and CT measurements were calculated. Multivariate linear regression models identified predictors of bias. Inter-and intra-reader agreements of luminal diameter and CT number measurements were excellent (ICCs [ 0.91, p \ 0.01, each). Measurement bias of luminal diameter and plaque density was significantly (p \ 0.01, each) lower (-12 % and 58 HU, respectively) with B46f as opposed to B26f, especially in plaque densities [200 HU. Measurement bias was significantly (p \ 0.01, each) correlated (q = 0.37-55 and q = -0.70-85) with the differences between luminal enhancement and plaque density. In multivariate models, bias of luminal diameter assessment with CT was correlated with plaque density (b = 0.09, p \ 0.05). Convolution kernel (b = -0.29 and -0.38), stenosis severity (b = -0.45 and -0.38), and luminal enhancement (b = -0.11 and -0.29) represented independent (p \ 0.05,each) predictors of measurement bias of luminal diameter and plaque number, respectively. Significant independent relationships exist between luminal enhancement, convolution kernel, plaque density, and luminal diameter, which have to be taken into account when performing, evaluating, and interpreting coronary CT angiography.

The effect of heart rate, vessel angulation and acquisition protocol on the estimation accuracy of calcified artery stenosis in dual energy cardiac CT: A phantom study

“…3). This finding is in consistence with previous studies and consensus clinical perception in CCTA [15][16][17]. As recommended by the reference protocol guide, to minimize artifacts due to cardiac motion for heart rates above 75 bpm, protocol C should be employed [12].…”

The Diagnostic Performance of Coronary CT Angiography for the Assessment of Coronary Stenosis in Calcified Plaque

“…In our study, 4955 Calcified plaques were detected on 3645 (28.4%) segments by CCTA. In order to reduce blooming artifacts of calcified plaque, the severity of stenosis in calcified plaque were calculated in CCTA images which were reconstructed using a conventional FBP (filtered back-projection) algorithm with an edge-enhancing tissue convolution kernel (B46f), this method was used previously in literature [ 17 , 19 , 20 ]. The overall sensitivity, specificity, PPV, NPV and accuracy were 97.78%, 90.11%,71.23%, 99.39% and 91.64%, respectively, which is partly compatible with the previous literature studies [ 16 , 21 – 23 ].…”

Quantification of uncertainty in the assessment of coronary plaque in CCTA through a dynamic cardiac phantom and 3D-printed plaque model

“…This is consistent with previous studies and current consensus clinical thought of CCTA. [5][6][7][8][9][10][11] Our results show that this motion influence manifests itself in a negative stenosis measurement bias, reaching 18.5% at the highest heart rate (90 bpm). The large magnitude of the negative stenosis bias is partially due to the average positive diameter measurement bias of 1.69 mm.…”

“…[6][7][8][9] But, there are few quantitative studies investigating this relationship using a known ground truth with dynamic anatomic realism. [10][11][12][13] Reasons for this include the challenge of developing suitable phantoms to realistically mimic cardiac and plaque anatomy, and heart motion with a corresponding electrocardiogram (ECG) signal.…”