暂无摘要。

In this review, the authors summarize the role of coronary computed tomography angiography and coronary artery calcium scoring in different clinical presentations of chest pain and preventative care and discuss future directions and new technologies such as pericoronary fat inflammation and the growing footprint of artificial intelligence in cardiovascular medicine.

Coronary Artery Disease (CAD) represents a persistent global health menace, particularly prevalent in Eastern European nations. Often asymptomatic until its advanced stages, CAD can precipitate life-threatening events like myocardial infarction or stroke. While conventional risk factors provide some insight into CAD risk, their predictive accuracy is suboptimal. Amidst this, Coronary Calcium Scoring (CCS), facilitated by non-invasive computed tomography (CT), emerges as a superior diagnostic modality. By quantifying calcium deposits in coronary arteries, CCS serves as a robust indicator of atherosclerotic burden, thus refining risk stratification and guiding therapeutic interventions. Despite certain limitations, CCS stands as an instrumental tool in CAD management and in thwarting adverse cardiovascular incidents. This review delves into the pivotal role of CCS in CAD diagnosis and treatment, elucidates the involvement of calcium in atherosclerotic plaque formation, and outlines the principles and indications of utilizing CCS for predicting major cardiovascular events.

BACKGROUND: The reference protocol for the quantification of coronary artery calcium (CAC) should be updated to meet the standards of modern imaging techniques.
OBJECTIVE: To assess the influence of filtered-back projection (FBP), hybrid iterative reconstruction (IR), and three levels of deep learning reconstruction (DLR) on CAC quantification on both in vitro and in vivo studies.
METHODS: In vitro study was performed with a multipurpose anthropomorphic chest phantom and small pieces of bones. The real volume of each piece was measured using the water displacement method. In the in vivo study, 100 patients (84 men; mean age = 71.2 ± 8.7 years) underwent CAC scoring with a tube voltage of 120 kVp and image thickness of 3 mm. The image reconstruction was done with FBP, hybrid IR, and three levels of DLR including mild (DLRmild), standard (DLRstd), and strong (DLRstr).
RESULTS: In the in vitro study, the calcium volume was equivalent (P = 0.949) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. In the in vivo study, the image noise was significantly lower in images that used DLRstr-based reconstruction, when compared images other reconstructions (P < 0.001). There were no significant differences in the calcium volume (P = 0.987) and Agatston score (P = 0.991) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. The highest overall agreement of Agatston scores was found in the DLR groups (98%) and hybrid IR (95%) when compared to standard FBP reconstruction.
CONCLUSIONS: The DLRstr presented the lowest bias of agreement in the Agatston scores and is recommended for the accurate quantification of CAC.

In this review, we aim to summarize state-of-the-art artificial intelligence (AI) approaches applied to cardiovascular CT and their future implications.
Recent studies have shown that deep learning networks can be applied for rapid automated segmentation of coronary plaque from coronary CT angiography, with AI-enabled measurement of total plaque volume predicting future heart attack. AI has also been applied to automate assessment of coronary artery calcium on cardiac and ungated chest CT and to automate the measurement of epicardial fat. Additionally, AI-based prediction models integrating clinical and imaging parameters have been shown to improve prediction of cardiac events compared to traditional risk scores. Artificial intelligence applications have been applied in all aspects of cardiovascular CT - in image acquisition, reconstruction and denoising, segmentation and quantitative analysis, diagnosis and decision assistance and to integrate prognostic risk from clinical data and images. Further incorporation of artificial intelligence in cardiovascular imaging holds important promise to enhance cardiovascular CT as a precision medicine tool.

We aimed to develop a novel deep-learning based method for automatic coronary artery calcium (CAC) quantification in low-dose ungated computed tomography attenuation correction maps (CTAC). In this study, we used convolutional long-short -term memory deep neural network (conv-LSTM) to automatically derive coronary artery calcium score (CAC) from both standard CAC scans and low-dose ungated scans (CT-attenuation correction maps). We trained convLSTM to segment CAC using 9543 scans. A U-Net model was trained as a reference method. Both models were validated in the OrCaCs dataset (n=32) and in the held-out cohort (n=507) without prior coronary interventions who had CTAC standard CAC scan acquired contemporarily. Cohen\'s kappa coefficients and concordance matrices were used to assess agreement in four CAC score categories (very low: <10, low:10-100; moderate:101-400 and high >400). The median time to derive results on a central processing unit (CPU) was significantly shorter for the conv-LSTM model- 6.18s (inter quartile range [IQR]: 5.99, 6.3) than for UNet (10.1s, IQR: 9.82, 15.9s, p<0.0001). The memory consumption during training was much lower for our model (13.11Gb) in comparison with UNet (22.31 Gb). Conv-LSTM performed comparably to UNet in terms of agreement with expert annotations, but with significantly shorter inference times and lower memory consumption.

Stress electrocardiography (sECG) or treadmill stress testing is a well validated noninvasive diagnostic modality available to clinicians at low cost yet providing valuable functional data for coronary artery disease (CAD) diagnostic and prognostic evaluation. With the advances in cardiac imaging in both functional and anatomic fronts and the existing limitations of sECG testing, this modality appears less favored worldwide as reflected in some recent guideline updates. We review the past present and future of sECG to provide a viewpoint on where it stands in CAD evaluation and if it will remain relevant as a diagnostic modality or be retired going forward. We also provide our perspectives on how sECG can co-exist with other modalities such as calcium scoring and discuss the role of such testing in the Indian population.

To evaluate the accuracy of coronary artery calcium (CAC) scoring at various tube voltages and different monoenergetic image reconstructions on a first-generation dual-source photon-counting detector CT (PCD-CT). A commercially available anthropomorphic chest phantom with calcium inserts was scanned at different tube voltages (90 kV, Sn100kV, 120 kV, and Sn140kV) on a first-generation dual-source PCD-CT system with quantum technology using automatic exposure control with an image quality (IQ) level of 20. The same phantom was also scanned on a conventional energy-integrating detector CT (120 kV; weighted filtered back projection) for reference. Extension rings were used to emulate different patient sizes. Virtual monoenergetic images at 65 keV and 70 keV applying different levels of quantum iterative reconstruction (QIR) were reconstructed from the PCD-CT data sets. CAC scores were determined and compared to the reference. Radiation doses were noted. At an IQ level of 20, radiation doses ranged between 1.18 mGy and 4.64 mGy, depending on the tube voltage and phantom size. Imaging at 90 kV or Sn100kV was associated with a size-dependent radiation dose reduction between 23% and 48% compared to 120 kV. Tube voltage adapted image reconstructions with 65 keV and QIR 3 at 90 kV and with 70 keV and QIR 1 at Sn100kV allowed to calculate CAC scores comparable to conventional EID-CT scans with a percentage deviation of ≤ 5% for all phantom sizes. Our phantom study indicates that CAC scoring with dual-source PCD-CT is accurate at various tube voltages, offering the possibility of substantial radiation dose reduction.

We evaluated the accuracy of coronary artery calcium (CAC) scoring on a dual-source photon-counting detector CT (PCD-CT). An anthropomorphic chest phantom underwent ECG-gated sequential scanning on a PCD-CT at 120 kV with four radiation dose levels (CTDIvol, 2.0-8.6 mGy). Polychromatic images at 120 kV (T3D) and virtual monoenergetic images (VMI), from 60 to 75 keV without quantum iterative reconstruction (no QIR) and QIR strength levels 1-4, were reconstructed. For reference, the same phantom was scanned on a conventional energy-integrating detector CT (120 kV; filtered back projection) at identical radiation doses. CAC scoring in 20 patients with PCD-CT (120 kV; no QIR and QIR 1-4) were included. In the phantom, there were no differences between CAC scores of different radiation doses (all, p > 0.05). Images with 70 keV, no QIR (CAC score, 649); 65 keV, QIR 3 (656); 65 keV; QIR4 (648) and T3D, QIR4 (656) showed a <1% deviation to the reference (653). CAC scores significantly decreased at increasing QIR levels (all, p < 0.001) and for each 5 keV-increase (all, p < 0.001). Patient data (median CAC score: 86 [inter-quartile range: 38-978] at 70 keV) confirmed relationships and differences between reconstructions from the phantom. First phantom and in-vivo experience with a clinical dual-source PCD-CT system shows accurate CAC scoring with VMI reconstructions at different radiation dose levels.

OBJECTIVE: To establish tailored preventive treatment, we studied the ability of coronary artery calcium scoring to reclassify patients with intermediate cardiovascular risk and its association with additional risk factors in our Mexican preventive care center.
METHODS: In this retrospective cohort study, we analyzed 520 asymptomatic patients from a Mexican primary prevention population between 2014 and 2018. Coronary artery calcium scoring, laboratory results, and anthropometric measurements (abdominal circumference and body mass index) were assessed. The Framingham risk score and American Heart Association/American College of Cardiology (AHA/ACC) atherosclerotic cardiovascular disease risk algorithm were calculated. Correlations between coronary artery calcium scoring, anthropometric measurements, and clinical cardiovascular risk scores were assessed. We assessed the ability of coronary artery calcium scoring to reclassify patients recommended for statin therapy compared with the cardiovascular risk scores.
RESULTS: Patients had a mean age of 67.5 years (SD ± 9.8) and 294 subjects (56.5%) were male. Coronary artery calcium scoring has a positive correlation with age, AHA/ACC atherosclerotic cardiovascular disease risk algorithm, and Framingham risk score (p < 0.001 for all). Coronary artery calcium scoring was prevalent, occurring in 63.2% of patients with a median Agatston score of 22 with and interquartile range of 178. Male gender, older age, smoking habit, diabetes, and abdominal circumference were independent predictors of coronary artery calcium scoring (p < 0.001). Coronary artery calcium scoring downwardly reclassified 44.9% of patients in intermediate cardiovascular risk categories by the AHA/ACC atherosclerotic cardiovascular disease risk algorithm and 43.9% by the Framingham risk score. Coronary artery calcium scoring upwardly reclassified 46.8% of patients in intermediate risk categories by the AHA/ACC atherosclerotic cardiovascular disease risk algorithm and 56% by the Framingham risk score.
CONCLUSIONS: Coronary artery calcium scoring is prevalent in this Mexican primary prevention cohort and has the ability to reclassify a significant percentage of intermediate cardiovascular risk patients.