Significant Stenosis of the Brachiocephalic Trunk and Moderate Stenosis of the Left Circumflex Artery in Computed Tomography Angiography Images

Atherosclerosis, as a civilization disease, is a serious epidemiological problem. Significant carotid disease and significant coronary artery disease result in acute consequences, such as ischemic stroke and myocardial infarction, which are the major causes of cardiovascular mortality. Typically, atherosclerosis of the aortic arch branches involves the bulbs of the common carotid arteries and the proximal segments of the internal carotid arteries, and can be effectively assessed by ultrasonography. Computed tomography angiography enables the identification of patients with less typical clinical manifestations of atherosclerosis, e.g., brachiocephalic trunk stenosis with symptoms of the steal syndrome and moderate stenosis in the coronary arteries. We present examples of computed tomography angiography images of this type of changes.

Pericoronary Fat Attenuation Index Is Associated With Vulnerable Plaque Components and Local Immune‐Inflammatory Activation in Patients With Non‐ST Elevation Acute Coronary Syndrome

Background The pericoronary fat attenuation index (FAI) is assessed using standard coronary computed tomography angiography, and it has emerged as a novel imaging biomarker of coronary inflammation. The present study assessed whether increased pericoronary FAI values on coronary computed tomography angiography were associated with vulnerable plaque components and their intracellular cytokine levels in patients with non‐ST elevation acute coronary syndrome. Methods and Results A total of 195 lesions in 130 patients with non‐ST elevation acute coronary syndrome were prospectively included. Lesion‐specific pericoronary FAI, plaque components and other plaque features were evaluated by coronary computed tomography angiography. Local T cell subsets and their intracellular cytokine levels were detected by flow cytometry. Lesions with pericoronary FAI values >−70.1 Hounsfield units exhibited spotty calcification (43.1% versus 25.0%, P =0.015) and low‐attenuation plaques (17.6% versus 4.2%, P =0.016) more frequently than lesions with lower pericoronary FAI values. Further quantitative plaque compositional analysis showed that increased necrotic core volume (Pearson’s r=0.324, P <0.001) and fibrofatty volume (Pearson’s r=0.270, P <0.001) were positively associated with the pericoronary FAI, and fibrous volume (Pearson’s r=−0.333, P <0.001) showed a negative association. An increasing proinflammatory intracellular cytokine profile was found in lesions with higher pericoronary FAI values. Conclusions The pericoronary FAI may be a reliable indicator of local immune‐inflammatory response activation, which is closely related to plaque vulnerability. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04792047.

Artificial Intelligence Segmentation Model-Based Computed Tomography Angiography Image in the Diagnosis of Congenital Aortic Constriction

This study was to analyze the impacts of the image segmentation model and computed tomography angiography (CTA) on the clinical diagnosis of aortic constriction under the background of artificial intelligence. In this study, 126 patients with congenital aortic constriction (CAC) diagnosed by surgery were selected as the research objects and routine digital subtraction angiography (DSA) and CTA were performed. Then, the traditional active contour model (AC model) was optimized based on the local area information to construct a new image segmentation model for intelligent segmentation and reconstruction of the CTA images of patients. The results revealed that compared with the AC model and the image segmentation model based on region growth (RG model) obtained from angiography segmentation, the algorithm constructed in this study showed a smaller segmentation range for angiography images and more accurate segmentation results. The quantitative data results suggested that the evolution times and running time of the constructed model were less than those of the AC and RG models P < 0.05 . Based on the gold standard of DSA examination results, there were 122 correctly diagnosed cases, 3 missed diagnosed cases, and 1 misdiagnosed by CTA, so the diagnosis coincidence rate was 96.83%. Compared with DSA, the average inner diameter and average pressure difference of patients with precatheter, paracatheter, and postcatheter type were not greatly different in CTA P > 0.05 . The CTA examination suggested there were 154 cases with intracardiac structural abnormalities, with a detection rate of 86.52%; there were 32 cases of cardiac-vascular connection abnormalities, with a detection rate of 100%; and there were 79 extracardiac vascular abnormalities, with the detection rate of 95.18%. It indicated that the optimized image segmentation model based on local area information proposed in this paper has excellent segmentation performance for CT angiography images and has good segmentation effect and efficiency. The CTA based on the artificial intelligence image segmentation model showed a better diagnostic effect on abnormal heart-vascular connection and abnormal extracardiac blood vessels and can be used as an effective examination method for clinical diagnosis of CAC.