Purpose Epicardial fat is the adipose tissue between the serosal pericardial wall layer and the visceral layer. It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans. Methods A set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi‐slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from −175 Hounsfield units (HU) to −15 HU for the segmentation of EAT. Results The Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1% ± 0.7% and 96.9% ± 0.6%, respectively. The inter‐observer variability was also assessed, resulting in a Dice index of 97.0% ± 0.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4% ± 1.5% and 93.3% ± 1.3%, respectively, and the same measurement between the experts themselves was 93.6% ± 1.9%. The Pearson’s correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99. Conclusions This work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. The proposed method is also packaged with a graphical user interface which can be found at https://github.com/MountainAndMorning/EATSeg.

Background: This study was conducted to explore the risk factors of anterior circulation intracranial aneurysm rupture based on extracranial carotid artery (ECA) tortuosity.Methods: This retrospective study, conducted from January 1, 2017, to March 1, 2021, collected and reviewed the clinical and imaging data of 308 patients with anterior circulation intracranial aneurysm [133 (43.2%) patients in the ruptured aneurysm group; 175 (56.8%) patients in the unruptured aneurysm group]. Computed tomography angiography (CTA) of the head and neck was used to determine the ECA tortuosity (normal, simple tortuosity, kink, coil) and the morphologic parameters of the aneurysms. The relationship of aneurysm rupture to ECA tortuosity and the morphologic parameters were analyzed.Results: After univariate analysis, kink, angle of flow inflow (FA), aspect ratio (AR), aneurysm length (L), the distance from the tortuosity to the aneurysm (distance), and size ratio (SR) were significantly correlated with anterior circulation intracranial aneurysm rupture (p < 0.05). Spearman correlation analysis showed that ECA tortuosity was correlated with FA and SR (p < 0.05). Multiple logistic analyses showed that FA [odds ratio (OR), 1.013; 95% CI, 1.002–1.025], SR (OR, 1.521; 95% CI, 1.054–2.195), and kink (OR, 1.823; 95% CI, 1.074–3.096) were independently associated with aneurysm rupture.Conclusion: Study results suggest that FA, SR, and ECA kink were independent risk factors associated with anterior circulation intracranial aneurysm rupture.

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Background The ideal treatment strategy for stable three-vessel coronary artery disease (CAD) patients are difficult to determine and for patients undergoing conservative treatment, imaging evidence of coronary atherosclerotic severity progression remains limited. Epicardial fat volume (EFV) on coronary CT angiography (CCTA) has been considered to be associated with coronary atherosclerosis. Therefore, this study aims to evaluate the relationship between EFV level and coronary atherosclerosis severity in three-vessel CAD. Methods This retrospective study enrolled 252 consecutive patients with three-vessel CAD and 252 normal control group participants who underwent CCTA between January 2018 and December 2019. A semi-automatic method was developed for EFV quantification on CCTA images, standardized by body surface area. Coronary atherosclerosis severity was evaluated and scored by the number of coronary arteries with ≥ 50% stenosis on coronary angiography. Patients were subdivided into groups on the basis of lesion severity: mild (score = 3 vessels, n = 85), moderate (3.5 vessels ≤ score < 4 vessels, n = 82), and severe (4 vessels ≤ score ≤ 7 vessels, n = 85). The independent sample t-test, analysis of variance, and logistic regression analysis were used to evaluate the associations between EFV level and severity of coronary atherosclerosis. Results Compared with normal controls, three-vessel CAD patients had significantly higher EFV level (65 ± 22 mL/m2 vs. 48 ± 19 mL/m2; P < 0.001). In patients with three-vessel CAD, there was a progressive decline in EFV level as the score of coronary atherosclerosis severity increased, especially in those patients with a body mass index (BMI) ≥ 25 kg/m2 (75 ± 21 mL/m2 vs. 72 ± 22 mL/m2 vs. 62 ± 17 mL/m2; P < 0.05). Multivariable regression analysis showed that both BMI (OR 3.40, 95% CI 2.00–5.78, P < 0.001) and the score of coronary atherosclerosis severity (OR 0.49, 95% CI 0.26–0.93, P < 0.05) were independently related to the change of EFV level. Conclusion Three-vessel CAD patients do have higher EFV level than the normal controls. While, there may be an inverse relationship between EFV level and the severity of coronary atherosclerosis in patients with three-vessel CAD.