Computed Tomography and Coronary Plaque Analysis
Abstract
1. Introduction
1.1. Types of CT Imaging Modalities
1.1.1. Dual-Layer Spectral CT Angiography (DL-SCTA)
1.1.2. Photon-Counting CT (PCCT)
1.1.3. Dual-Energy CT (DECT)
1.1.4. CT-Derived Fractional Flow Reserve (CT-FFR)
1.2. Significance of Plaque Burden
1.3. Artificial Intelligence and CT-Derived Plaque Analysis
2. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Imaging Modality | Key Features | Clinical Utility | Diagnostic Performance | Limitations |
---|---|---|---|---|
Dual-Layer Spectral CT Angiography (DL-SCTA) | Differentiates calcified, non-calcified, and mixed plaques; identifies high-risk plaques using low attenuation values (<30 HU); assesses plaque burden and arterial remodeling. | Risk stratification, high-risk plaque identification, improved assessment of plaque burden. | Sensitivity 77%, Specificity 56% for high-risk plaques [4]; enhanced identification of lipid-rich plaques and neovascularization. | Lower specificity; limited data in certain populations; motion artifacts can impact image quality. |
Photon-Counting CT (PCCT) | Measures individual photon interactions; improves spatial resolution and reduces noise; detects microcalcifications and thin-cap fibroatheromas with high sensitivity and specificity. | Early detection of vulnerable plaques, superior stent visualization, monitoring of disease progression. | Sensitivity 94%, Specificity 89% for lipid-rich plaques [5]; AUC 0.93 for CAD detection [6]; 100% sensitivity for stent patency [7]. | Cost and availability; motion artifacts still a concern; limited widespread use. |
Dual-Energy CT (DECT) | Utilizes X-rays at two distinct energy levels; enables material decomposition for precise plaque characterization; correlates plaque composition with myocardial infarction risk. | Identification of rupture-prone plaques, prediction of ischemic events, accurate tissue characterization. | Strong correlation with MI risk; RMSE < 5% for plaque component quantification [8]; ORs up to 20.0 for stroke prediction based on plaque features [9]. | Radiation exposure; complexity in interpretation; limited by image noise and patient motion. |
CT-Derived Fractional Flow Reserve (CT-FFR) | Combines anatomical and hemodynamic assessment; calculates functional significance of stenoses; improves specificity of CCTA and reduces unnecessary invasive angiography. | Improves clinical decision-making, guides revascularization, predicts major adverse cardiac events (MACE). | HR up to 5.05 for MACE with CT-FFR 0.80 [10]; improves specificity of CCTA; diagnostic accuracy reduced in high calcium scores or stents [11,12,13,14,15]. | Accuracy impacted by high calcium or metallic stents; manual editing needed in complex cases; poor image quality reduces utility. |
AI Application | Key Benefits | Study Findings |
---|---|---|
Plaque Detection and Classification | High sensitivity (90%) and specificity (93%) in detecting high-risk plaques. | Meta-analysis (1484 patients) reported AUROC of 0.96 for detecting high-risk plaques [53]. |
Quantification of Plaque Burden | Reduces inter-observer variability, aligns with IVUS standards. | AI-QCPA altered management in 66% of cases in DECODE study [54]. |
CT-FFR Calculation | Machine learning accelerates CT-FFR calculations, improving workflow efficiency. | AI-QCPA significantly changed decisions for <50% stenosis plaques [54]. |
Calcium Scoring | Automates and enhances calcium score accuracy, reducing reader variability. | AI achieved intra-class correlation coefficient of 0.98 with expert readings [52,54]. |
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Alhammouri, H.; Ibrahim, R.; Alasmar, R.; Abdelnabi, M.; Habib, E.; Allam, M.; Pham, H.N.; Elbenawi, H.; Farina, J.; Tamarappoo, B.; et al. Computed Tomography and Coronary Plaque Analysis. Tomography 2025, 11, 85. https://doi.org/10.3390/tomography11080085
Alhammouri H, Ibrahim R, Alasmar R, Abdelnabi M, Habib E, Allam M, Pham HN, Elbenawi H, Farina J, Tamarappoo B, et al. Computed Tomography and Coronary Plaque Analysis. Tomography. 2025; 11(8):85. https://doi.org/10.3390/tomography11080085
Chicago/Turabian StyleAlhammouri, Hashim, Ramzi Ibrahim, Rahmeh Alasmar, Mahmoud Abdelnabi, Eiad Habib, Mohamed Allam, Hoang Nhat Pham, Hossam Elbenawi, Juan Farina, Balaji Tamarappoo, and et al. 2025. "Computed Tomography and Coronary Plaque Analysis" Tomography 11, no. 8: 85. https://doi.org/10.3390/tomography11080085
APA StyleAlhammouri, H., Ibrahim, R., Alasmar, R., Abdelnabi, M., Habib, E., Allam, M., Pham, H. N., Elbenawi, H., Farina, J., Tamarappoo, B., Jokerst, C., Lee, K., Ayoub, C., & Arsanjani, R. (2025). Computed Tomography and Coronary Plaque Analysis. Tomography, 11(8), 85. https://doi.org/10.3390/tomography11080085