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Abstract Background Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor’s selection point is subjective, and difficult to accurately locate the edge, which will bring errors to the measurement results. Methods In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions Therefore, the measurement method proposed in this paper has the advantages of less manual intervention, and the processing method is reasonable and has practical value.

Background: Previous studies suggested that myocardial work (MW) may identify abnormalities in the left ventricular (LV) function and establish a more sensitive index for LV dysfunction at the early stage. This study aimed to explore the value of global and regional MW parameters in predicting high-risk stable coronary artery disease (SCAD) patients with normal wall motion and preserved LV function.Patients and Methods: A total of 131 patients, who were clinically diagnosed as SCAD with normal wall motion and LV function, were finally included in this study. Global MW parameters, including global work index (GWI), global constructive work (GCW), global waste work (GWW), and global work efficiency (GWE) were measured with non-invasive LV pressure-strain loops constructed from speckle-tracking echocardiography. Regional myocardial work index (RWI) and work efficiency (RWE) were also calculated according to the perfusion territory of each major coronary artery. All patients underwent coronary angiography and were divided into the high-risk SCAD group, the non-high-risk SCAD group, and the No SCAD group according to the range and degrees of coronary arteries stenosis.Results: The global longitudinal strain (GLS), GWI and GCW were statistically different (P < 0.001) among the three groups. In the high-risk SCAD group, GLS, GWI, and GCW were significantly lower than the other two groups (P < 0.05). Receiver operating characteristic analysis demonstrated GWI and GCW could predict high-risk SCAD at a cutoff value of 1,808 mm Hg% (sensitivity, 52.6%; specificity, 87.8%; predictive positive value, 76.3%; predictive negative value, 69.9%) and 2,308 mm Hg% (sensitivity, 80.7%; specificity, 64.9%; predictive positive value, 63.3%; predictive negative value, 80.0%), respectively. Multivariate analyses showed that carotid plaque, decreased GWI, and GCW was independently related to high-risk SCAD. The cutoff values of RWILAD, RWILCX, and RWIRCA were 2,156, 1,929, and 1,983 mm Hg% in predicting high-risk SCAD, respectively (P < 0.001). When we combined RWI in two or three perfusion regions, the diagnostic performance of SCAD was improved (P < 0.001).Conclusions: Both global and regional MW parameters have great potential in non-invasively predicting high-risk SCAD patients with normal wall motion and preserved LV function, contributing to the early identification of high-risk patients who may benefit from revascularization therapy.

To assess the value of echocardiography for detection of the flow-dependent epicardial coronary vasodilation, the changes in internal diameter of the left anterior descending coronary arteries (LAD) induced by reactive hyperemia were studied by echocardiography in 12 health anesthetized open-chest dogs. Reactive hyperemia was induced by brief occlusion of the left anterior descending coronary artery for 30 s followed by rapid release. The two- dimensional images of the left anterior descending coronary artery before and after reactive hyperemia with and without intracoronary infusion of NG-nitro-L-arginine methyl ester (L-NAME), an inhibitor of nitric oxide synthase (NOS) were investigated. The internal diameter of LAD was measured and its percent change induced by reactive hyperemia was calculated. Our results showed that the internal diameter of LAD was 2.23±0.19 mm before intracoronary infusion of L-NAME (baseline). The internal diameter of LAD significantly increased to 2.52±0.24 mm ( P<0.01) after reactive hyperemia at baseline, and the percent change in internal diameter of LAD was (13.10±3.59)%. The internal diameter of LAD before and after reactive hyperemia under the condition of intracoronary infusion of L-NAME was not different from that before reactive hyperemia at baseline. The percent change in internal diameter of LAD was (1.07±2.97)%, and it was significantly lower than that at baseline ( P<0.001). We are led to conclude that the change in internal diameter of LAD responding to reactive hyperemia was detected sensitively by echocardiography, and this change was associated with endothelium-derived nitric oxide. [ABSTRACT FROM AUTHOR]