Atherosclerosis quantification and cardiovascular risk: the ISCHEMIA trial.

BACKGROUND AND AIMS: The aim of this study was to determine the prognostic value of coronary computed tomography angiography (CCTA)-derived atherosclerotic plaque analysis in ISCHEMIA. METHODS: Atherosclerosis imaging quantitative computed tomography (AI-QCT) was performed on all available baseline CCTAs to quantify plaque volume, composition, and distribution. Multivariable Cox regression was used to examine the association between baseline risk factors (age, sex, smoking, diabetes, hypertension, ejection fraction, prior coronary disease, estimated glomerular filtration rate, and statin use), number of diseased vessels, atherosclerotic plaque characteristics determined by AI-QCT, and a composite primary outcome of cardiovascular death or myocardial infarction over a median follow-up of 3.3 (interquartile range 2.2-4.4) years. The predictive value of plaque quantification over risk factors was compared in an area under the curve (AUC) analysis. RESULTS: Analysable CCTA data were available from 3711 participants (mean age 64 years, 21% female, 79% multivessel coronary artery disease). Amongst the AI-QCT variables, total plaque volume was most strongly associated with the primary outcome (adjusted hazard ratio 1.56, 95% confidence interval 1.25-1.97 per interquartile range increase [559 mm3]; P = .001). The addition of AI-QCT plaque quantification and characterization to baseline risk factors improved the model's predictive value for the primary outcome at 6 months (AUC 0.688 vs. 0.637; P = .006), at 2 years (AUC 0.660 vs. 0.617; P = .003), and at 4 years of follow-up (AUC 0.654 vs. 0.608; P = .002). The findings were similar for the other reported outcomes. CONCLUSIONS: In ISCHEMIA, total plaque volume was associated with cardiovascular death or myocardial infarction. In this highly diseased, high-risk population, enhanced assessment of atherosclerotic burden using AI-QCT-derived measures of plaque volume and composition modestly improved event prediction.

Rapidly progressive coronary atherosclerosis in a young male: a retrospective advanced CCT phenotype analysis.

We present a real-life case of a very young man with multiple risk factors who progressed rapidly from minimally obstructive non-calcified plaque on computed tomography angiography (CCTA) to severe three-vessel coronary disease presenting with STEMI. It questions the reliability of zero coronary calcium in high-risk subgroups like familial hypercholesterolemia, high Lp(a), and the young. While CCTA can accurately visualize non-calcified plaque, its interpretation requires expertise and clinical judgment should consider both imaging and clinical risk factors for management. Advanced plaque quantification, peri-coronary (PCAT), and epicardial (EAT) adipose tissue could help better-stratified patients but the evidence-based clinical application remains unknown.

Uncovering a hidden danger: a case report of diffuse coronary spasm concealing spontaneous coronary artery dissection.

BACKGROUND: Over recent years, spontaneous coronary artery dissection (SCAD) has emerged as a no longer rare cause of acute coronary syndrome (ACS). On the other hand, coronary artery spasm (CAS) is the main cause of ischemic heart disease with non-obstructive coronary lesions. Clinical manifestations of both vary from stable angina to ACS or, rarely, sudden cardiac death. These entities may be underdiagnosed on a coronary angiography. CASE PRESENTATION: We report the case of a young woman presenting with acute chest pain and no coronary risk factors. Angiography revealed a focal subcritical stenosis of the right coronary artery. Coronary wiring resulted in diffuse and critical spasm. However, optical coherence tomography (OCT) and intravascular ultrasound (IVUS) showed extensive SCAD. She was therefore treated conservatively. On the fourth day, cardiac computed tomography angiography (CCTA) excluded disease progression, and then she was discharged on medical therapy. CONCLUSIONS: Combined IVI plays a vital role in providing accurate and detailed visualization of the coronary anatomy and thus allowing for more precise diagnosis, risk stratification, and treatment planning. CCTA can be considered a valuable tool in the noninvasive follow-up of SCAD.

Syncope due to recurrent ventricular tachycardias after transcatheter aortic valve implantation with unexpected diagnosis in cardiac computed tomography: a case report.

Background: Delayed coronary obstruction (DCO) is a rare but potentially life-threatening complication after transcatheter aortic valve implantation (TAVI) mostly affecting the left main coronary artery (LMCA) and often caused by prosthesis endothelialization or thrombus formations. Herein, we report an unusual case of a delayed LMCA-obstruction caused by a calcium nodule, which was diagnosed 4 months after TAVI due to recurrent ventricular tachycardia (VT) episodes. Case summary: A 73-year-old patient was readmitted to an external hospital with syncope three months after TAVI. Fast VT could be induced in electrophysiological examination, why the patient received a two-chamber implantable cardioverter defibrillator (ICD). However, after 1 month the patient was readmitted to our department with another syncope. Implantable cardioverter defibrillator records revealed multiple fast VT episodes (200-220 b.p.m.). In addition, the patient reported new-onset exertional dyspnoea (New York Class Association Stage III) and elevated high-sensitive cardiac troponin of 115 ng/L. Due to the symptoms and laboratory markers indicating potential myocardial ischaemia, a cardiac computed tomography angiography (CCTA) was performed. Cardiac computed tomography angiography revealed obstruction of the LMCA likely caused by calcium shift during TAVI. After CCTA-guided percutaneous coronary intervention, patient's course remained uneventful. Discussion: The present case report highlights the role of CCTA as a powerful non-invasive diagnostic tool in complex settings after TAVI. Delayed coronary obstruction as a procedural complication can occur after TAVI and manifest with various symptoms, including new-onset or recurrent VTs, like in the present case. Cardiac computed tomography angiography provided accurate assessment of the implanted prosthesis and detection of DCO, thus guiding the subsequent PCI.

The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management.

This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.

Beyond Stress Ischemia: Unveiling the Multifaceted Nature of Coronary Vulnerable Plaques Using Cardiac Computed Tomography.

Historically, cardiovascular prevention has been predominantly focused on stress-induced ischemia, but recent trials have challenged this paradigm, highlighting the emerging role of vulnerable, non-flow-limiting coronary plaques, leading to a shift towards integrating plaque morphology with functional data into risk prediction models. Coronary computed tomography angiography (CCTA) represents a high-resolution, low-risk, and largely available non-invasive modality for the precise delineation of plaque composition, morphology, and inflammatory activity, further enhancing our ability to stratify high-risk plaque and predict adverse cardiovascular outcomes. Coronary artery calcium (CAC) scoring, derived from CCTA, has emerged as a promising tool for predicting future cardiovascular events in asymptomatic individuals, demonstrating incremental prognostic value beyond traditional cardiovascular risk factors in terms of myocardial infarction, stroke, and all-cause mortality. Additionally, CCTA-derived information on adverse plaque characteristics, geometric characteristics, and hemodynamic forces provides valuable insights into plaque vulnerability and seems promising in guiding revascularization strategies. Additionally, non-invasive assessments of epicardial and pericoronary adipose tissue (PCAT) further refine risk stratification, adding prognostic significance to coronary artery disease (CAD), correlating with plaque development, vulnerability, and rupture. Moreover, CT imaging not only aids in risk stratification but is now emerging as a screening tool able to monitor CAD progression and treatment efficacy over time. Thus, the integration of CAC scoring and PCAT evaluation into risk stratification algorithms, as well as the identification of high-risk plaque morphology and adverse geometric and hemodynamic characteristics, holds promising results for guiding personalized preventive interventions, helping physicians in identifying high-risk individuals earlier, tailoring lifestyle and pharmacological interventions, and improving clinical outcomes in their patients.

Takes Two to Tangle: A Rare Case of Type IV Dual Left Anterior Descending Artery (LAD) and Dual Ostia of the LAD and Left Circumflex (LCx) Artery.

The dual left anterior descending (LAD) artery is a rare anatomic variant of the LAD artery that refers to the duplication of the LAD into a short and long LAD. These two vessels, differentiated based on their lengths, ultimately provide blood supply to the areas normally covered by the LAD. In this case report, we describe an unusual case of a type IV dual LAD system with an additional finding of a separate origin for the short LAD and left circumflex (LCx) artery. These two findings have not been reported together in the literature previously. During diagnostic procedures like coronary angiography or when interpreting cardiac imaging, awareness of these anomalies prevents confusion with pathological conditions such as coronary artery disease or stenosis. Additionally, it is crucial for cardiologists and surgeons to identify these aberrant vessels to avoid any wrongful interventions.

A method to design a fast chaotic oscillator using CCTA.

This article introduces a novel method for designing a fast chaotic oscillator using a CCTA (Current Conveyor Transconductance Amplifier) based on Chua's circuit. The proposed method uses innovative configurations and advanced simulation techniques to overcome challenges in high-speed operation, nonlinear dynamics, and Analog Building Block (ABB) selection. The design begins with nonlinear negative resistance, essential for Chua's diode characteristics, including two negative resistances, NR1 and NR2. The circuit integrates one CCTA block, two grounded capacitors, two fixed resistors, one inductor, and one potentiometer. It is simulated using PSPICE with IC (Integrated Circuit) macro-models and 180nm CMOS (Complementary Metal Oxide Semiconductor) technology. Various chaotic waveforms and attractors are produced, validating the theoretical and mathematical predictions. By varying the resistance values (1450Ω, 1650Ω, 1800Ω, 1950Ω), the circuit exhibits different chaotic behaviors, such as large limit cycles, double-scroll attractors, Rossler-type attractors, and I-periodic attractors. FFT (Fast Fourier Transform) analysis confirms the highest dominant operating frequency of 37.5MHz. A Monte Carlo simulation with 100 runs shows maximum voltage variations in the chaotic waveforms of 5.21 % and 4.61 % across the capacitors, demonstrating robustness and reliability. This design offers significant advancements in implementing high-frequency chaotic oscillators, with potential applications in various fields requiring chaotic signal generation.•A novel design of Chua's diode and Chua's chaotic oscillator using only one CCTA block is presented in this paper.•The proposed chaotic oscillator achieves the highest operating frequency of 37.5MHz.•The proposed circuit is simulated using commercially available ICs (MAX435 and AD844) and CMOS 180nm technology in PSPICE to confirm its workability.

Quantification of subclinical plaque characteristics and perivascular fat using coronary computed tomography angiography (CCTA) among individuals with human immunodeficiency virus (HIV).

Background: People infected with human immunodeficiency virus (PIWH) have a higher risk of cardiovascular events. This study was designed to compare the differences in plaque characteristics and perivascular fat between subclinical coronary atherosclerosis in PIWH and healthy controls (HC) by coronary computed tomography angiography (CCTA). We also assessed the associations between human immunodeficiency virus (HIV) infection, antiretroviral therapy (ART), and coronary artery disease (CAD). Methods: This cross-sectional study included a total of 158 PIWH and 79 controls. CCTA was used to evaluate coronary artery plaque prevalence, coronary stenosis severity, plaque composition, plaque volume, and perivascular fat attenuation index (FAI). Logistic regression analyses were used to assess the associations between the prevalence of coronary artery plaque and HIV-related clinical indicators. Results: There was no difference in total coronary artery plaque prevalence between PIWH and controls (44.3% vs. 32.9%; P=0.09), but the prevalence of noncalcified plaque was significantly higher in PIWH compared with the controls (33.5% vs. 16.5%; P=0.006). After adjustment for age, sex, statin use, and family history of cardiovascular disease (CVD), the prevalence of noncalcified plaque remained 2 times higher in PIWH [odds ratio (OR), 2.082; 95% confidence interval (CI): 1.007-4.304; P=0.048]. The perivascular FAI measured around the left anterior descending artery (LAD) was higher in PIWH (-71.4±5.7 vs. -73.5±7.0; P=0.03) compared with that of the controls. The intra-group analyses of PIWH suggested that the decrease in nadir CD4+ T-cell count was associated with the increased prevalence of noncalcified plaque (OR, 4.139; 95% CI: 1.312-13.060; P=0.02). Conclusions: PIWH have a higher risk of developing noncalcified plaque and greater perivascular fat. In addition, the increased noncalcified plaque prevalence in PIWH may be associated with the immunodeficiency caused by HIV.

Cardiac computed tomography-derived coronary artery volume to myocardial mass in patients with severe coronary artery disease.

BACKGROUND: Coronary artery lumen volume (V) to myocardial mass (M) ratio (V/M) can show the mismatch between epicardial coronary arteries and the underlying myocardium. METHODS: The V, M and V/M were obtained from the coronary computed tomography angiography (CCTA) of patients in the FAST-TRACK CABG study, the first-in-human trial of coronary artery bypass grafting (CABG) guided solely by CCTA and fractional flow reserve derived from CCTA (FFRCT) in patients with complex coronary artery disease (CAD). The correlations between V/M ratios and baseline characteristics were determined and compared with those from the ADVANCE registry, an unselected cohort of historical controls with chronic CAD. RESULTS: The V/M ratio was obtained in 106 of the 114 pre-CABG CCTAs. Mean age was 65.6 years and 87% of them were male. The anatomical SYNTAX score from CCTA was significantly higher than the functional SYNTAX score derived using FFRCT [43.1 (15.2) vs 41.1 (16.5), p â€‹< â€‹0.001]. Mean V, M, and V/M were 2204 â€‹mm3, 137 â€‹g, and 16.5 â€‹mm3/g, respectively. There were weak negative correlations between V and anatomical and functional SYNTAX scores (Pearson's r â€‹= â€‹-0.26 and -0.34). V and V/M had a strong correlation (r â€‹= â€‹0.82). The V/M ratio in the current study was significantly lower than that in the ADVANCE registry (median 16.1 vs. 24.8 [1st quartile 20.1]). CONCLUSION: Systematically smaller V/M ratios were found in this population with severe CAD requiring CABG compared to an unselected cohort with chronic CAD. The V/M ratio could provide additional non-invasive assessment of CAD especially when combined with FFRCT.

Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus.

BACKGROUND: Diabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients. METHODS: R language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC. RESULTS: The results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset. CONCLUSIONS: In this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.

Relationship between different clinical characteristics and pericoronary adipose tissue attenuation values quantified from coronary computed tomographic angiography (CCTA) in patients without coronary heart disease (CHD).

Background: Pericoronary adipose tissue (PCAT) is a sensor of vascular inflammation. Elevated PCAT attenuation values indicate the presence of coronary inflammation in patients. However, it is unclear which clinical characteristics are associated with increased PCAT attenuation values in patients without coronary heart disease (CHD). The study aims to investigate the relationship between increased PCAT attenuation values and clinical characteristics of patients without CHD. Methods: We recruited 785 eligible patients without CHD who underwent coronary computed tomographic angiography (CCTA). Clinical data were recorded for each patient, and PCAT attenuation values for the left anterior descending branch (LADPCAT), left circumflex branch (LCXPCAT), and right coronary artery (RCAPCAT) were quantified by CCTA using fully automated software. Univariate and multivariate analyses were performed to identify the associations between different clinical characteristics and elevated LADPCAT, LCXPCAT, and RCAPCAT. Results: Univariate analysis showed body mass index (BMI) to be positively associated with LADPCAT (rs=0.109), LCXPCAT (rs=0.076), and RCAPCAT (rs=0.083). Moreover, the duration of smoking, and drinking was positively associated with LADPCAT (rs=0.099, 0.165). Hyperlipidemia was positively associated with LADPCAT (rs=0.089) and RCAPCAT (rs=0.334), while statin use was negatively associated with RCAPCAT (rs=-0.145). Multivariate analysis showed that the significant determinants of LADPCAT were BMI (ß=0.359, P=0.001), duration of smoking (ß=2.612, P=0.002), drinking (ß=4.106, P<0.001), and hyperlipidemia (ß=1.664, P=0.027). LCXPCAT was associated with BMI (ß=0.218, P=0.024), while RCAPCAT was associated with hyperlipidemia (ß=6.110, P<0.001) and statin use (ß=-3.338, P<0.001). Conclusions: In patients without CHD, the PCAT attenuation values measured using CCTA were associated with various clinical characteristics. LADPCAT was associated with BMI, smoking duration, drinking, and hyperlipidemia. On the other hand, LCXPCAT was associated with BMI, while RCAPCAT was associated with hyperlipidemia and statin use.

Automated detection and classification of coronary atherosclerotic plaques on coronary CT angiography using deep learning algorithm.

Background: Coronary artery disease (CAD) is the leading cause of mortality worldwide. Recent advances in deep learning technology promise better diagnosis of CAD and improve assessment of CAD plaque buildup. The purpose of this study is to assess the performance of a deep learning algorithm in detecting and classifying coronary atherosclerotic plaques in coronary computed tomographic angiography (CCTA) images. Methods: Between January 2019 and September 2020, CCTA images of 669 consecutive patients with suspected CAD from Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine were included in this study. There were 106 patients included in the retrospective plaque detection analysis, which was evaluated by a deep learning algorithm and four independent physicians with varying clinical experience. Additionally, 563 patients were included in the analysis for plaque classification using the deep learning algorithm, and their results were compared with those of expert radiologists. Plaques were categorized as absent, calcified, non-calcified, or mixed. Results: The deep learning algorithm exhibited higher sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy {92% [95% confidence interval (CI): 89.5-94.1%], 87% (95% CI: 84.2-88.5%), 79% (95% CI: 76.1-82.4%), 95% (95% CI: 93.4-96.3%), and 89% (95% CI: 86.9-90.0%)} compared to physicians with ≤5 years of clinical experience in CAD diagnosis for the detection of coronary plaques. The algorithm's overall sensitivity, specificity, PPV, NPV, accuracy, and Cohen's kappa for plaque classification were 94% (95% CI: 92.3-94.7%), 90% (95% CI: 88.8-90.3%), 70% (95% CI: 68.3-72.1%), 98% (95% CI: 97.8-98.5%), 90% (95% CI: 89.8-91.1%) and 0.74 (95% CI: 0.70-0.78), indicating strong performance. Conclusions: The deep learning algorithm has demonstrated reliable and accurate detection and classification of coronary atherosclerotic plaques in CCTA images. It holds the potential to enhance the diagnostic capabilities of junior radiologists and junior intervention cardiologists in the CAD diagnosis, as well as to streamline the triage of patients with acute coronary symptoms.

Using artificial intelligence to study atherosclerosis from computed tomography imaging: A state-of-the-art review of the current literature.

With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients.

Coronary artery segmentation in CCTA images based on multi-scale feature learning.

BACKGROUND: Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy. OBJECTIVE: A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries. METHODS: The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance. RESULTS: The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets. CONCLUSION: Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine.

Predicting coronary artery severity in patients undergoing coronary computed tomographic angiography: Insights from pan-immune inflammation value and atherogenic index of plasma.

BACKGROUND AND AIMS: Coronary computed tomographic angiography (CCTA) is pivotal in diagnosing coronary artery disease (CAD). We explored the link between CAD severity and two biomarkers, Pan-Immune Inflammation Value (PIV) and Atherogenic Index of Plasma (AIP), in stable CAD patients. METHODS AND RESULTS: A retrospective observational study of 409 CCTA patients with stable angina pectoris. Logistic regression identified predictors of severe CAD, stratified by CAD-RADS score. Receiver Operating Characteristic (ROC) curves evaluated predictive performance. PIV and AIP were significant predictors of severe CAD (PIV: OR 1.002, 95% CI: 1.000-1.004, p < 0.021; AIP: OR 0.963, 95% CI: 0.934-0.993, p < 0.04). AUC values for predicting severe CAD were 0.563 (p < 0.001) for PIV and 0.625 (p < 0.05) for AIP. Combined with age, AUC improved to 0.662 (p < 0.02). CONCLUSIONS: PIV and AIP were associated with severe CAD, with AIP demonstrating superior predictive capability. Incorporating AIP into risk assessment could enhance CAD prediction, offering a cost-effective and accessible method for identifying individuals at high risk of coronary atherosclerosis.

Casting Light on The Hidden Prevalence: A Novel Perspective on Hypoplastic Coronary Artery Disease.

Background and Objectives: Coronary artery anomalies (CAAs) represent a group of rare cardiac abnormalities with an incidence of up to 1.2%. The aim of this retrospective study was to conduct a comprehensive epidemiological assessment of the prevalence of hypoplastic coronary arteries using coronary computed tomography angiography (CCTA) in patients with diagnosed CAAs and individuals presenting with cardiovascular manifestations in the north-eastern region of Romania. This study was motivated by the limited investigation of the CAAs conducted in this area. Methods: We analyzed data collected from 12,758 coronary computed tomography angiography (CCTA) records available at the "Prof. Dr. George I.M. Georgescu" Cardiovascular Diseases Institute, spanning the years 2012 to 2022. Results: Among 350 individuals with CAAs (2.7% of the total cohort), 71 patients (20.3% of the anomaly presenting group and 0.5% of the entire CCTA cohort) exhibited at least one hypoplastic coronary artery. The mean age of individuals diagnosed with hypoplastic coronary artery disease (HCAD) was 61 years, while the age distribution among them ranged from 22 to 84 years. Nearly equal cases of right and left dominance (33 and 31, respectively) were observed, with only 7 cases of co-dominance. Conclusions: HCAD may be considered underexplored in current published research, despite its potentially significant implications ranging to an increased risk of sudden cardiac arrest. The specific prevalence of HCAD among CAAs might be higher than previously reported, possibly reflecting better diagnostic accuracy of CCTA over classic coronary imaging. The absence of standard diagnostic and therapeutic protocols for HCAD underscores the necessity of a personalized approach for such cases.

Assessment of atherosclerotic plaque burden: comparison of AI-QCT versus SIS, CAC, visual and CAD-RADS stenosis categories.

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.

Computed Tomography Angiography: Principles and Advances.

This review describes current state-of-the-art computed tomography technology required to address human-physiology-based challenges unique to angiographic imaging. Challenges are based on the need to image a bolus of contrast agent traversing inside rapidly moving structures. This article reviews the latest methods to optimize contrast timing and minimize motion.

Virtual calcium removal in calcified coronary arteries with photon-counting detector CT-first in-vivo experience.

Purpose: To evaluate the feasibility and accuracy of quantification of calcified coronary stenoses using virtual non-calcium (VNCa) images in coronary CT angiography (CCTA) with photon-counting detector (PCD) CT compared with quantitative coronary angiography (QCA). Materials and methods: This retrospective, institutional-review board approved study included consecutive patients with calcified coronary artery plaques undergoing CCTA with PCD-CT and invasive coronary angiography between July and December 2022. Virtual monoenergetic images (VMI) and VNCa images were reconstructed. Diameter stenoses were quantified on VMI and VNCa images by two readers. 3D-QCA served as the standard of reference. Measurements were compared using Bland-Altman analyses, Wilcoxon tests, and intraclass correlation coefficients (ICC). Results: Thirty patients [mean age, 64 years ± 8 (standard deviation); 26 men] with 81 coronary stenoses from calcified plaques were included. Ten of the 81 stenoses (12%) had to be excluded because of erroneous plaque subtraction on VNCa images. Median diameter stenosis determined on 3D-QCA was 22% (interquartile range, 11%-35%; total range, 4%-88%). As compared with 3D-QCA, VMI overestimated diameter stenoses (mean differences -10%, p < .001, ICC: .87 and -7%, p < .001, ICC: .84 for reader 1 and 2, respectively), whereas VNCa images showed similar diameter stenoses (mean differences 0%, p = .68, ICC: .94 and 1%, p = .07, ICC: .93 for reader 1 and 2, respectively). Conclusion: First experience in mainly minimal to moderate stenoses suggests that virtual calcium removal in CCTA with PCD-CT, when feasible, has the potential to improve the quantification of calcified stenoses.