Cardiac Cardiac Computed Tomography Scan Research Articles

Artificial Intelligence-Driven Advances in Coronary Calcium Scoring: Expanding Preventive Cardiology.

Coronary artery disease (CAD) remains a leading global cause of morbidity and mortality, underscoring the need for effective cardiovascular risk stratification and preventive strategies. Coronary artery calcium (CAC) scoring, traditionally performed using electrocardiogram (ECG)-gated cardiac computed tomography (CT) scans, has been widely validated as a robust tool for assessing cardiovascular risk. However, its application has been largely limited to high-risk populations due to the costs, technical requirements, and limited accessibility of cardiac CT scans. Recent advancements in artificial intelligence (AI) have introduced transformative opportunities to extend CAC detection to noncardiac CT scans, such as those performed for lung cancer screening, enabling broader and more accessible cardiovascular screening. This review provides a comprehensive analysis of AI-driven CAC detection, examining various types of AI models for CAC detection, like convolutional neural networks (CNNs) and U-Net architectures, and exploring the clinical, operational, and ethical implications of incorporating these technologies into routine practice. Technical challenges, including imaging variability, data privacy, and model bias, are discussed alongside essential areas for further research, such as standardization and validation across diverse populations. By leveraging widely available imaging data, AI-enabled CAC detection has the potential to advance preventive cardiology, supporting earlier risk identification, optimizing healthcare resources, and improving patient outcomes.

Relationship between epicardial adipose tissue and ventricular fibrillation in Brugada syndrome by cardiac computed tomography analysis

Abstract Background Fibrofatty changes in the myocardium of the right ventricular (RV) to right ventricular outflow tract (RVOT) in patients with Brugada syndrome (BrS) may contribute to ventricular conduction delay and ultimately in the development of ventricular fibrillation (VF). In patients with atrial fibrillation (AF), inflammatory infiltration of epicardial adipose tissue (EAT) into the atrial muscle, along with reduced quantity and quality of EAT, has been demonstrated to be associated with the development of AF. We hypothesized that EAT in the RVOT from the RV in patients with BrS may lead to organic abnormalities in the ventricular muscle, resulting in VF. Purpose The purpose of this study was to evaluate the quantity and quality of EAT in patients with BrS using cardiac computed tomography (CT) and to determine the relationship with the occurrence of VF. Methods We retrospectively evaluated 61 of 82 patients with BrS regularly followed in our outpatient clinic from January 2000 to December 2023, who underwent cardiac CT scans. Patients were divided into two groups: those with VF events during follow-up (VF event group) and those without events (VF non-event group). Patient background, various parameters, volume of epicardial adipose tissue (EAT), and CT values from cardiac CT scans were compared between the two groups. EAT volume and CT values were measured for the entire heart and four specific regions: right ventricle (RV-EAT), right ventricular outflow tract (RVOT) to pulmonary artery bifurcation (RVOT-EAT), left ventricle (LV-EAT), and the left coronary artery main trunk circumference (LV summit-EAT) were measured for four specific sites. Results Median age at first diagnosis of BrS was 43 years, 98% were male patients, 41 patients (71%) had symptomatic BrS, and 18 patients (31%) had a family history. During a median follow-up of 72 months (30-136 months), 22 patients (38%) had VF events, including 2 arrhythmic sudden deaths. The median time from diagnosis to the first VF event was 14.3 months (4.3-24.5 months). The VF event group had more overall EAT volume (98.3 cm3 [range: 77.4 to 120.3 cm3] vs. 70.1 cm3 [range: 53.8 to 98.0 cm3], P=0.02) and significantly more RV-EAT volume (18.4 cm3 [range: 11.5 to 28.0 cm3] vs. 11.0 cm3 [range: 7.9 to 18.3 cm3], P=0.008). The CT value of RV-EAT was significantly lower in the VF event group (-83.1±6.1 vs -78.0±6.2, P=0.003), although the RV-EAT/LV-EAT density ratio was not different between the two groups (1.01±0.05 vs 0.99±0.07, P=0.14). On the other hand, the density ratio of RVOT-EAT/LV-EAT was significantly lower in the VF event group (0.95±0.08 vs. 1.00±0.08, P=0.03). Conclusions EAT analysis via CT in patients with BrS revealed that those experiencing VF events during follow-up tended to exhibit greater RV-EAT volume, lower CT values of RV-EAT, and higher CT values of RVOT-EAT density. These findings suggest EAT involvement in VF occurrence in patients with BrS.

AI-based algorithm for assessing coronary artery calcium score on contrast-enhanced cardiac computed tomography scans

Abstract Introduction In cardiac computed tomography (CT) examinations, non-contrast scans are often performed in addition to contrast-enhanced scans to quantify the coronary artery calcification (CAC) score. These calcium score scans are associated with additional exposure to ionizing radiation. Purpose We sought to develop a fully automated artificial intelligence (AI)-based algorithm capable of determining CAC score based solely on contrast-enhanced CT scans, eliminating the need for additional non-contrast scans. Methods An automated CAC scoring algorithm on contrast-enhanced CT scans was developed and trained using a dataset of 297 cardiac CT studies. Coronary artery calcifications were manually segmented in contrast-enhanced scans using a threshold of 2 standard deviations above the mean attenuation value of the ascending aorta. On non-contrast scans, CAC was manually assessed using a standard threshold of ≥130 Hounsfield Units. A correction factor for calcium score results assessed on contrast-enhanced scans was determined using linear regression. The algorithm was tested on an independent set of 90 contrast-enhanced CT scans (four manufacturers, eight scanner models). We compared automated CAC scores to manual expert reader reference assessments on non-contrast CT scans. CAC scores were categorized into four risk categories following the Society of Cardiovascular Computed Tomography recommendations: 0, 1-100, 101-300, and >300 Agatston Units. Results In the assessment of 90 CT studies (mean age 61.0±11.4 years, 46.7% males), the AI-model detected CAC in 69 contrast-enhanced scans (76.7%), comparable to the human reader's detection rate of CAC in 71 non-contrast scans (78.9%) (p = 0.63). The CAC score was initially calculated using AI-based segmentation of coronary calcification on contrast-enhanced scans and then the result was multiplied by established linear correction factor of 1.97. There was an excellent correlation between AI-model and manual reference total CAC scores (Pearson’s r = 0.96, 95% CI 0.94–0.97, p < 0.001). The model correctly classified 77 patients (85.6%) into the same CAC risk category as the human reader (Figure 1). Among 19 patients (21.1%) with a CAC score of zero, only 1 patient (5.3%) was reclassified with a non-zero CAC score by the AI-model. Cohen’s kappa value for CAC score risk categorization was 0.80 (p < 0.001), indicating very good agreement (Figure 2). Bland–Altman analysis revealed minimal bias of -9.7 Agatston Unit with 95% limits of agreement ranging from -184.8 to 165.5 Agatston Unit. Conclusions CAC score can be accurately quantified on contrast-enhanced cardiac CT scans using an automated AI-based algorithm. This approach has the potential to eliminate the necessity for an additional non-contrast CT scan, thereby reducing the patient's exposure to ionizing radiation.

Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasets type of manuscript: Original research.

To evaluate the quantitative and qualitative image quality using deep learning image reconstruction (DLIR) of pediatric cardiac computed tomography (CT) compared with conventional image reconstruction methods. Between January 2020 and December 2022, 109 pediatric cardiac CT scans were included in this study. The CT scans were reconstructed using an adaptive statistical iterative reconstruction-V (ASiR-V) with a blending factor of 80% and three levels of DLIR with TrueFidelity (low-, medium-, and high-strength settings). Quantitative image quality was measured using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The edge rise distance (ERD) and angle between 25% and 75% of the line density profile were drawn to evaluate sharpness. Qualitative image quality was assessed using visual grading analysis scores. A gradual improvement in the SNR and CNR was noted among the strength levels of the DLIR in sequence from low to high. Compared to ASiR-V, high-level DLIR showed significantly improved SNR and CNR (P<0.05). ERD decreased with increasing angle as the level of DLIR increased. High-level DLIR showed improved SNR and CNR compared to ASiR-V, with better sharpness on pediatric cardiac CT scans.

Airway Tree Caliber and Susceptibility to Pollution-associated Emphysema: MESA Air and Lung Studies.

Rationale: Airway tree morphology varies in the general population and may modify the distribution and uptake of inhaled pollutants. Objectives: We hypothesized that smaller airway caliber would be associated with emphysema progression and would increase susceptibility to air pollutant-associated emphysema progression. Methods: MESA (Multi-Ethnic Study of Atherosclerosis) is a general population cohort of adults 45-84 years old from six U.S. communities. Airway tree caliber was quantified as the mean of airway lumen diameters measured from baseline cardiac computed tomography (CT) (2000-2002). Percentage emphysema, defined as percentage of lung pixels below -950 Hounsfield units, was assessed up to five times per participant via cardiac CT scan (2000-2007) and equivalent regions on lung CT scan (2010-2018). Long-term outdoor air pollutant concentrations (particulate matter with an aerodynamic diameter ⩽2.5 μm, oxides of nitrogen, and ozone) were estimated at the residential address with validated spatiotemporal models. Linear mixed models estimated the association between airway tree caliber and emphysema progression; modification of pollutant-associated emphysema progression was assessed using multiplicative interaction terms. Measurements and Main Results: Among 6,793 participants (mean ± SD age, 62 ± 10 yr), baseline airway tree caliber was 3.95 ± 1.1 mm and median (interquartile range) of percentage emphysema was 2.88 (1.21-5.68). In adjusted analyses, 10-year emphysema progression rate was 0.75 percentage points (95% confidence interval, 0.54-0.96%) higher in the smallest compared with largest airway tree caliber quartile. Airway tree caliber also modified air pollutant-associated emphysema progression. Conclusions: Smaller airway tree caliber was associated with accelerated emphysema progression and modified air pollutant-associated emphysema progression. A better understanding of the mechanisms of airway-alveolar homeostasis and air pollutant deposition is needed.

The Feasibility of Using a Deep Learning-Based Model to Determine Cardiac Computed Tomographic Contrast Dose.

This study aimed to predict contrast effects in cardiac computed tomography (CT) from CT localizer radiographs using a deep learning (DL) model and to compare the prediction performance of the DL model with that of conventional models based on patients' physical size. This retrospective study included 473 (256 men and 217 women) cardiac CT scans between May 2014 and August 2017. We developed and evaluated DL models that predict milligrams of iodine per enhancement of the aorta from CT localizer radiographs. To assess the model performance, we calculated and compared Pearson correlation coefficient ( r ) between the actual iodine dose that was necessary to obtain a contrast effect of 1 HU (iodine dose per contrast effect [IDCE]) and IDCE predicted by DL, body weight, lean body weight, and body surface area of patients. The model was tested on 52 cases for the male group (mean [SD] age, 63.7 ± 11.4) and 44 cases for the female group (mean [SD] age, 69.8 ± 11.6). Correlation coefficients between the actual and predicted IDCE were 0.607 for the male group and 0.412 for the female group, which were higher than the correlation coefficients between the actual IDCE and body weight (0.539 for male, 0.290 for female), lean body weight (0.563 for male, 0.352 for female), and body surface area (0.587 for male, 0.349 for female). The performance for predicting contrast effects by analyzing CT localizer radiographs with the DL model was at least comparable with conventional methods using the patient's body size, notwithstanding that no additional measurements other than CT localizer radiographs were required.

Methods and Reproducibility of Liver Fat Measurement Using 3-Dimensional Liver Segmentation From Noncontrast Computed Tomography in EVAPORATE Cohort.

Nonalcoholic fatty liver disease not only shares multiple risk factors with cardiovascular disease but also independently predicts its increased risk and related outcomes. Here, we evaluate reproducibility of 3-dimensional (3D) liver volume segmentation method to identify fatty liver on noncontrast cardiac computed tomography (CT) and compare measures with previously validated 2-dimensional (2D) segmentation CT criteria for the measurement of liver fat. The study included 68 participants enrolled in the EVAPORATE trial and underwent serial noncontrast cardiac CT. Liver attenuation < 40 Hounsfield units (HU) was used for diagnosing fatty liver, as done in the MESA study. Two-dimensional and 3D segmentation of the liver were performed by Philips software. Bland-Altman plot analysis was used to assess reproducibility. Interreader reproducibility of 3D liver mean HU measurements was 96% in a sample of 111 scans. Reproducibility of 2D and 3D liver mean HU measurements was 93% in a sample of 111 scans. Reproducibility of change in 2D and 3D liver mean HU was 94% in 68 scans. Kappa, a measure of agreement in which the 2D and 3D measures both identified fatty liver, was excellent at 96.4% in 111 scans. Fatty liver can be reliably diagnosed and measured serially in a stable and reproducible way by 3D liver segmentation of noncontrast cardiac CT scans. Future studies need to explore the sensitivity and stability of measures for low liver fat content by 3D segmentation, over the current 2D methodology. This measure can serve as an imaging biomarker to understand mechanistic correlations between atherosclerosis, fatty liver, and cardiovascular disease risk.

Anatomical mapping of the membranous septum in tricuspid and bicuspid aortic valves by cardiac computed tomography.

A higher incidence of conduction disturbances and permanent pacemaker implantation (PPI) has been observed after transcatheter aortic valve implantation (TAVI) in patients with bicuspid aortic valves (BAVs) as compared to those with tricuspid aortic valves (TAVs). This study aimed to provide an anatomical explanation for this observation, supported by an in-depth anatomical mapping of the membranous septum (MS) in a large cohort of BAVs and TAVs using cardiac computed tomography (CT). A total of 300 cardiac CT scans were analysed, revealing a significantly shorter sub-annular length of the MS in BAVs at all measuring points compared to TAVs (p < 0.001). In the current BAV cohort, the MS was found to be at its shortest at the RCC site, measuring less than 1 mm in depth. In addition, the MS was located more anteriorly towards the RCC in BAVs, where the transcatheter aortic valve tends to be implanted deeper, and we observed a trend towards a higher PPI rate in BAVs. Future studies should investigate whether anatomical mapping of the MS in patients undergoing TAVI could be a useful tool for decision-making and potentially mitigate the risk of conduction disturbances.

Outflow cannula alignment in continuous flow left ventricular devices is associated with stroke.

We sought to evaluate whether differences in left ventricular assist device (LVAD) canula alignment are associated with stroke. There is a paucity of clinical data on contribution of LVAD canulae alignment to strokes. We conducted a retrospective analysis of patients who underwent LVAD implantation at Houston Methodist hospital from 2011 to 2016 and included those who had undergone cardiac computed tomography (CT) with contrast. LVAD graft alignment using X-ray, echocardiography, and cardiac CT was evaluated. The primary outcome was stroke within 1 year of LVAD implantation. Of the 101 patients that underwent LVAD Implantation and cardiac CT scan during the study period, 78 met inclusion criteria. The primary outcome occurred in 12 (15.4%) patients with a median time to stroke of 77 days (interquartile range: 42-132 days). Of these, 10 patients had an ischemic and two had hemorrhagic strokes. The predominant device type was Heart Mate II (94.8%). Patients with LVAD outflow cannula to aortic angle lesser than 37.5° and those with outflow graft diameter of anastomosis less than 1.5 cm (assessed by cardiac CT) had significantly higher stroke risk (p < 0.001 and p = 0.01 respectively). In HMII patients, a lower LVAD speed at the time of CT scan was associated with stroke. Further studies are needed to identify optimal outflow graft configuration to mitigate stroke risk.

Novel non-invasive ECG imaging method based on the 12-lead ECG for reconstruction of ventricular activation: A proof-of-concept study.

Current non-invasive electrocardiographic imaging (ECGi) methods are often based on complex body surface potential mapping, limiting the clinical applicability. The aim of this pilot study was to evaluate the ability of a novel non-invasive ECGi method, based on the standard 12-lead ECG, to localize initial site of ventricular activation in right ventricular (RV) paced patients. Validation of the method was performed by comparing the ECGi reconstructed earliest site of activation against the true RV pacing site determined from cardiac computed tomography (CT). This was a retrospective study using data from 34 patients, previously implanted with a dual chamber pacemaker due to advanced atrioventricular block. True RV lead position was determined from analysis of a post-implant cardiac CT scan. The ECGi method was based on an inverse-ECG algorithm applying electrophysiological rules. The algorithm integrated information from an RV paced 12-lead ECG together with a CT-derived patient-specific heart-thorax geometric model to reconstruct a 3D electrical ventricular activation map. The mean geodesic localization error (LE) between the ECGi reconstructed initial site of activation and the RV lead insertion site determined from CT was 13.9 ± 5.6 mm. The mean RV endocardial surface area was 146.0 ± 30.0 cm2 and the mean circular LE area was 7.0 ± 5.2 cm2 resulting in a relative LE of 5.0 ± 4.0%. We demonstrated a novel non-invasive ECGi method, based on the 12-lead ECG, that accurately localized the RV pacing site in relation to the ventricular anatomy.

Abstract 15172: Improving Cardiac Segmentation for Atrial Fibrillation Ablation: A Prospective Trial of Machine Learned Geometric Dissection vs Experts

Introduction: Segmenting cardiac computed tomography (CT) to provide anatomic guidance for Atrial Fibrillation (AF) ablation is routinely applied, but is time-consuming and prone to error. Machine learning (ML) is a powerful approach that could automate this approach, but is hindered by the small size of available labeled datasets. Hypothesis: We hypothesized that a new computational pipeline, in which an ML model is trained mathematically in a small cohort (N=20) using geometrical heart avatars derived from computer graphics imaging (CGI), rather than on manually-segmented data, would enable rapid expert-level segmentation of raw cardiac CT scans. Methods: We first encoded anatomical knowledge with generic geometrical avatars and derived a “virtual dissection” method to geometrically parse the heart (Fig A). An ML model trained by virtual dissection using 20 cases was able to rapidly and accurately segment the pulmonary veins (PVs), left atrial appendage (LAA), and left atrium (LA) from cardiac CT scans (Fig B), which we tested in a retrospective study of N=100 patients (30% women, 64.7±10.1Y) and in a prospective clinical trial of N=42 patients (42.9% women, 65.2±10.8Y) undergoing AF ablation, against a panel of 3 experts (Fig C). Results: In a retrospective study (N=100), ML achieved median Dice scores of 96.6% (IQR: 95.1% to 97.5%), similar to experts (p&lt;0.05). In a prospective study (N=42), this pipeline reduced segmentation time (2.3±0.8 vs 15.0±6.9 minutes; p &lt; 0.00001; Fig D), but achieved similar Dice scores (93.9% (IQR: 93.0% to 94.6%) vs 94.4% (IQR: 92.8% to 95.7%); p&lt;0.05; Fig E). Conclusions: In our prospective trial, virtual dissection (machine learning of cardiac structures based on mathematical cardiac geometry) accelerated cardiac segmentation prior to AF ablation. In general, this approach may reduce the dependence on large training datasets for machine learning, and could be applied to other organ systems for diverse therapeutic strategies.

Prediction of recurrence after catheter ablation for atrial fibrillation using left atrial morphology on preprocedural computed tomography: application of radiomics

Abstract Background Radiomics is a comprehensive analysis methodology of medical image and involves the extraction of numerous features from standard imaging. Its usefulness has been reported mainly in the field of cancer for diagnosis and prediction of prognosis. In the territory of cardiac imaging, several reports have investigated the utility of radiomics for classifying the risk of prognosis in coronary artery disease, and few practical applications have been reported for patients with atrial fibrillation (AF) who underwent pulmonary vein isolation (PVI). Although the left atrial morphology can affect the clinical course after the PVI procedure, it is unclear whether the radiomics feature values of the left atrial morphology on cardiac computed tomography (CT) is useful for predicting the AF recurrence after PVI. Purpose To predict the recurrence of AF after PVI using the radiomics feature values of the left atrial morphology on cardiac computed tomography (CT). Methods We analyzed 525 consecutive three-dimensional cardiac CT in patients with atrial fibrillation who underwent PVI from 2018 to 2019 in our institute. After marking the region of interest on left atrium (including the root of pulmonary veins) semiautomatically, 107 radiomics feature values were obtained by Python program. After excluding the parameters having collinearity or with low predictive capability for the recurrence of AF after PVI, 42 parameters were applied to the final prediction model. Two prediction models were constructed by multivariate Cox regression analysis and machine learning model by support vector machine algorithm. Results The area under the curve (AUC) for predicting the recurrence of AF was 0.815 for the multivariate Cox regression model and 0.826 for the machine learning model by support vector machine. Conclusion The radiomics feature values on preprocedural cardiac CT could be helpful for predicting the recurrence of AF after PVI. Since radiomics feature analysis yields a huge number of numerical values representing the left atrial morphology in a reproducible manner, it would provide a new direction to construct a good prediction model using machine learning including artificial intelligence out of a routine cardiac CT scan. Funding Acknowledgement Type of funding sources: None.

Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions

We aimed to determine the effects of deep learning-based reconstruction (DLR) on radiomic features obtained from cardiac computed tomography (CT) by comparing with iterative reconstruction (IR), and filtered back projection (FBP). A total of 284 consecutive patients with 285 cardiac CT scans that were reconstructed with DLR, IR, and FBP, were retrospectively enrolled. Radiomic features were extracted from the left ventricular (LV) myocardium, and from the periprosthetic mass if patients had cardiac valve replacement. Radiomic features of LV myocardium from each reconstruction were compared using a fitting linear mixed model. Radiomics models were developed to diagnose periprosthetic abnormality, and the performance was evaluated using the area under the receiver characteristics curve (AUC). Most radiomic features of LV myocardium (73 of 88) were significantly different in pairwise comparisons between all three reconstruction methods (P < 0.05). The radiomics model on IR exhibited the best diagnostic performance (AUC 0.948, 95% CI 0.880–1), relative to DLR (AUC 0.873, 95% CI 0.735–1) and FBP (AUC 0.875, 95% CI 0.731–1), but these differences did not reach significance (P > 0.05). In conclusion, applying DLR to cardiac CT scans yields radiomic features distinct from those obtained with IR and FBP, implying that feature robustness is not guaranteed when applying DLR.

Is the Calcium Score Useful for Rheumatoid Arthritis Patients at Low or Intermediate Cardiovascular Risk?

Cardiovascular disease, particularly myocardial infarction, is the leading cause of death of rheumatoid arthritis (RA) patients. The usefulness of the coronary artery calcification score (CACS), determined using cardiac computed-tomography (CT)-scan images, was assessed as a part of a cardiovascular work-up of RA patients at low or intermediate cardiovascular disease risk. This descriptive, cross-sectional, single-center study was conducted on patients with stable RA or that which is in remission. Each patient’s work-up included a collection of cardiovascular risk factors, laboratory analyses, an electrocardiogram, a supra-aortic trunks (SATs) echo-Doppler test and a cardiac CT scan. The primary endpoint was to determine the frequency of patients with a CACS > 100, indicating notable atherosclerosis. Fifty patients were analyzed: mean ± standard deviation age was 53.7 ± 7.5 years, 82% women. The CACS exceeded 100 in 12 (24%) patients (11 were at intermediate risk) and 2 of them underwent angioplasty for silent myocardial ischemia. Cardiovascular risk was reclassified from intermediate to high for 5 patients. Age according to sex and smoking status were significantly associated with that increase; no association was found with RA characteristics or treatments.

Left atrial anatomical variations correlate with atrial fibrillation sources near the left atrial ridge

Abstract Funding Acknowledgements Type of funding sources: None. Background Anatomical variations and characteristics of the left atrium (LA) may have a previously undescribed effect on source locations in atrial fibrillation (AF). Electrographic flow (EGF) mapping is a novel method used to estimate cardiac action potential flow in the atria that can detect AF sources in patients with persistent AF. The EGF technology offers a revolutionary mapping possibility for AF, however, it is not commercially available yet. Purpose This is the first study aiming to investigate the relationship between anatomical characteristics of the LA and non-PV sources detected by EGF in patients with persistent AF. Methods We collected cardiac computed tomography (CT) and EGF data from patients who underwent radiofrequency catheter ablation (CA) for persistent AF. EGF mapping creates a spatial and temporal reconstruction of electric potentials derived from endocardial unipolar electrograms. By analyzing EGF maps obtained during CA procedures, we localized non-PV sources in the LA. Patients with individual stable sources with a source activity above threshold (leading source present in more than &amp;gt; 26% of the time) were classified as having an S-Type EGF signature with source-dependent AF. Patients with no stable active source pattern and no leading source with a source activity above threshold were characterized as having a C-Type EGF signature consistent with source-independent AF. We assessed LA anatomical characteristics including size, LAA length, LAA ostial diameter, trajectory of the left superior pulmonary vein (LSPV) and its relation to the LAA on cardiac CT scans. Abutting LAA-LSPV was defined as cases when the LSPV touched the posterior aspect of LAA, and the maximal distance between the two structures was less than 2 mm. Those cases where the distance between LAA and LSPV was more than 2 mm were defined as non-abutting LAA-LSPV. Results Thirty patients were included in this study (mean age 62.4±6.8 years):23 patients had an S-Type EGF signature (77%), and 7 patients had a C-Type EGF signature (23%). We identified 10 patients with AF sources near the LA ridge, while twenty patients had no leading source near the LA ridge. LA anatomical characteristics, LAA length and ostial diameter showed no correlation with the presence of a leading source near the ridge. We described 19 patients with abutting LAA-LSPV, and 11 patients with non-abutting LAA-LSPV. Three out of 19 patients presented with a leading source near ridge in the abutting LAA-LSPV group, while 7 out of 11 patients presented with a leading source near ridge in the non-abutting LAA-LSPV group (p=0.01). Conclusion Our data suggests that non-abutting LAA-LSPV is associated with the presence of AF sources near the LA ridge.

Machine Learning for the Prevalence and Severity of Coronary Artery Calcification in Nondialysis Chronic Kidney Disease Patients: A Chinese Large Cohort Study.

Purpose:This study sought to determine whether machine learning (ML) can be used to better identify the risk factors and establish the prediction models for the prevalence and severity of coronary artery calcification (CAC) in nondialysis chronic kidney disease (CKD) patients and compare the performance of distinctive ML models with conventional logistic regression (LR) model.Materials and Methods:In all, 3701 Chinese nondialysis CKD patients undergoing noncontrast cardiac computed tomography (CT) scanning were enrolled from November 2013 to December 2017. CAC score derived from the cardiac CT was calculated with the calcium scoring software and was used to assess and stratify the prevalence and severity of CAC. Four ML models (LR, random forest, support vector machine, and k-nearest neighbor) and the corresponding feature ranks were conducted. The model that incorporated the independent predictors was shown as the receiver-operating characteristic (ROC) curve. Area under the curve (AUC) was used to present the prediction value. ML model performance was compared with the traditional LR model using pairwise comparisons of AUCs.Results:Of the 3701 patients, 943 (25.5%) patients had CAC. Of the 943 patients with CAC, 764 patients (20.6%) and 179 patients (4.8%) had an Agatston CAC score of 1 to 300 and ≥300, respectively. The primary cohort and the independent validation cohort comprised 2957 patients and 744 patients, respectively. For the prevalence of CAC, the AUCs of ML models were from 0.78 to 0.82 in the training data set and the internal validation cohort. For the severity of CAC, the AUCs of the 4 ML models were from 0.67 to 0.70 in the training data set and from 0.53 to 0.70 in the internal validation cohort. For the prevalence of CAC, the AUC was 0.80 (95% confidence interval [CI]: 0.77-0.83) for ML (LR) versus 0.80 (95% CI: 0.77-0.83) for the traditional LR model (P=0.2533). For the severity of CAC, the AUC was 0.70 (95% CI: 0.63-0.77) for ML (LR) versus 0.70 (95% CI: 0.63-0.77) for traditional LR model (P=0.982).Conclusions:This study constructed prediction models for the presence and severity of CAC based on Agatston scores derived from noncontrast cardiac CT scanning in nondialysis CKD patients using ML, and showed ML LR had the best performance.

Pre- and post-operative cardiovascular CT in Stage I single ventricle palliation.

The aim of this study is to describe clinical utility of low dose cardiac computed tomography (CT) in the evaluation of single ventricle physiology before and after Stage I palliation. Despite the increased utilization of CT imaging and advancement of CT technology, there are limited studies describing the routine clinical use of cardiac CT and radiation dose parameters in the single ventricle Stage I palliation. This single center, retrospective study included 57 infants with single ventricle physiology who underwent cardiac CT scans between January 1, 2016 and November 30, 2020. Patients' demographic information, diagnosis, indication, total dose length product (DLP), computed tomographic dose index volume (CTDIvol), cardiac CT findings and intraoperative or intraprocedural findings were reviewed. Estimated effective radiation dose was calculated using a previously published conversion rate. The studies were performed using different generations of CT scanners over the 4 years period: Somatom AS 128, Somatom definition edge, Somatom Force (Siemens Medical Solutions). The studies performed with dual source scanner with prospective gated technique have lower radiation dose exposure with median effective radiation dose of 0.32 mSv. Pre- and post-operative cardiovascular CT in Stage I single ventricle palliation using newer generation scanners with prospective gated technique can be done with minimal radiation exposure and good image quality. Cardiac CT is a powerful imaging modality for better management planning in this group of patients.

Aged Garlic Extract Reduces IL-6: A Double-Blind Placebo-Controlled Trial in Females with a Low Risk of Cardiovascular Disease.

Background Chronic inflammation is a risk factor for cardiovascular disease. The aim of the study was to evaluate whether a daily supplementation of aged garlic extract (AGE) could reduce inflammation in females with low risk for cardiovascular disease. The study was conducted at a single center, as a parallel randomized placebo-controlled trial. Method 63 females with a Framingham risk score over 10 underwent cardiac computed tomography (CT) scan. Of those, patients with a coronary artery calcium (CAC) scores less than 5 (n = 31) met the inclusion criteria and were randomized, in a double-blind manner to an intake of placebo or AGE (2400 mg daily) for 1 year. Results Main outcome measure was changes in inflammatory biomarkers, blood pressure, fastening blood glucose, and blood lipids. A total of 29 patients (14 in the AGE group and 15 in the placebo group) completed the study and were analyzed. Females treated with AGE showed lower levels of inflammatory marker IL-6 after 12 months of treatment compared to females receiving placebo (p < 0.05). The blood lipids had a trend towards a lowering effect in females treated with AGE; however, this trend was not significant. Conclusion The present study concludes that AGE lowers IL-6 in females with a risk profile of cardiovascular disease. We could also conclude that risk prediction with cardiac CT scan turned out to be superior in estimating the risk of cardiac disease compared to Framingham risk score. This trial is registered with NCT03860350.

Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning.

AimsTo develop an automated method for bloodpool segmentation and imaging plane re-slicing of cardiac computed tomography (CT) via deep learning (DL) for clinical use in coronary artery disease (CAD) wall motion assessment and reproducible longitudinal imaging.Methods and resultsOne hundred patients who underwent clinically indicated cardiac CT scans with manually segmented left ventricle (LV) and left atrial (LA) chambers were used for training. For each patient, long-axis (LAX) and short-axis planes were manually defined by an imaging expert. A DL model was trained to predict bloodpool segmentations and imaging planes. Deep learning bloodpool segmentations showed close agreement with manual LV [median Dice: 0.91, Hausdorff distance (HD): 6.18 mm] and LA (Dice: 0.93, HD: 7.35 mm) segmentations and a strong correlation with manual ejection fraction (Pearson r: 0.95 LV, 0.92 LA). Predicted planes had low median location (6.96 mm) and angular orientation (7.96) errors which were comparable to inter-reader differences (P > 0.71). 84–97% of DL-prescribed LAX planes correctly intersected American Heart Association segments, which was comparable (P > 0.05) to manual slicing. In a test cohort of 144 patients, we evaluated the ability of the DL approach to provide diagnostic imaging planes. Visual scoring by two blinded experts determined ≥94% of DL-predicted planes to be diagnostically adequate. Further, DL-enabled visualization of LV wall motion abnormalities due to CAD and provided reproducible planes upon repeat imaging.ConclusionA volumetric, DL approach provides multiple chamber segmentations and can re-slice the imaging volume along standardized cardiac imaging planes for reproducible wall motion abnormality and functional assessment.

Usefulness of spectral cardiac computed tomography for the evaluation of thrombotic complications in patients with ischemic stroke

Objective: The aim of this study was to evaluate the usefulness of spectral cardiac computed tomography (CT) angiographyin patients with ischemic stroke. Methods: In the setting of COVID-19 pandemic, we incorporated the use of spectral cardiac CT in patients with ischemicstroke to rule out the presence of cardioembolic sources, thrombotic complications or myocardial damage in a single session.Since July 2020, a delayed-phase image acquisition was incorporated to cardiac CT scans in the context of ischemic stroke.We describe four representative cases of the usefulness of the method and the cardiovascular findings. Results: We present four cases recorded recorded within a 40-day period. Two patients with patients with cardioembolic source(aorta and left atrial appendage) and two with ischemic stroke of undetermined source with evidence of cardiomyopathy(ischemic and non-ischemic). Conclusions: In the setting of ischemic stroke, spectral cardiac CT with delayed acquisition could be useful to rule out thepresence of cardioembolic sources and identify the underlying etiology.