Animal model experimental protocols
Male SD rats (220–250g) were supplied by the Experimental Animal Center at the Second Affiliated Hospital of Harbin Medical University. Animal protocols were approved by the Institutional Animal Care Committee of Harbin Medical University, Heilongjiang Province, China. All experiments followed the Guide for the Care and Use of Laboratory Animals published by the U.S. National Institutes of Health (NIH Publication 85-23, revised 1996).
All SD rats were randomly divided into either the MI model group (n=15) or the sham group (n=10). Myocardial infarction was induced in MI model group rats via permanent ligature of the left anterior descending coronary artery, while sham group rats underwent the same operational procedure without artery ligation. All rats were anesthetized with 10% chloral hydrate and fixed on an operating table. After anesthetic induction, the rats were mechanically ventilated with a positive pressure ventilator. Chest hair was then removed and a thoracotomy was performed between the third and fourth intercostal spaces to expose the whole heart. The left anterior descending artery was isolated and ligated at about 2 mm on the lower edge of the left auricle using a 7.0 silk snare. Whitening of the cardiac apex provided a visual indication of successful ligation. The chest was then sutured and closed. After resuscitation, the trachea cannula was removed, and all rats were returned to the animal housing facility with free access to food and water. MCE videos were collected one week post-surgery.
MCE data acquisition
MCE was performed using an ultrasound scanner (Vivid 7, GE Healthcare, Milwaukee, WI, USA) equipped with a 10S transducer (8-10 MHz). Rats were anesthetized using the above method and placed on the examination bed. The ultrasound mechanical index setting was set to 0.2, and time gain compensation and 2-D gain settings were adjusted to suppress any nonlinear signals from the tissue before contrast agent infusion. SonoVue (Bracco, Milan, Italy) was then infused into the tail vein at a rate of 1.2 mL/h. MCE images were digitally acquired, starting at peak myocardial opacity until the disappearance of contrast from the myocardium. After imaging, rats were euthanized under deep anesthesia. Electrocardiogram changes were continuously monitored using an electrocardiograph throughout the image acquisition process. The LV short-axis view MCE videos were obtained at the basal and midpapillary muscle levels, and digital data of 6-10 consecutive heart cycles were recorded on magneto-optical disks. The images showing the most favorable myocardial perfusion for each rat were selected analysis In this study, the LV basal and midpapillary muscle levels were divided into six segments according to the guidelines[32].
Pathological staining
Rat hearts were excised after death and fixed in 4% paraformaldehyde for histopathologic analysis. Each heart was cut into three thick slices along the long axis from the apex to base. After standard paraffin embedding, two 5-μm-thick sections were obtained from each slice for Masson’s trichrome staining. Sections were then imaged using light microscopy and abnormal staining segments were counted. Blue-stained myocardial regions were considered to be infarcted, while red staining indicated the presence of viable myocardium.
Deep polar residual network
In MCE short-axis images, myocardium radial characteristics are important indicators of myocardial properties. Therefore, it was better to utilize this characteristic and transfer MCE images from a Descartes system into a polar system for observing myocardial regions and comparing them with radial neighboring regions. This study proposes a novel approach, the use of a polar layer in a deep learning network. After transfering myocardium regions into the polar system, the myocardium ring was converted into a band which is suitable for deep learning network to analyze. The CAD system firstly created a region of interest (ROI) on the original MCE image shaped like a disk including only the myocardium and chamber interior. The radius of this disk was set at 250 pixels in order to cover all myocardial regions. The ROI image is mapped onto the polar coordinates using a polar layer to obtain a polar ROI (PROI) image. Because the infarction region may be present at different angles and locations, the myocardium is usually divided into six equal segments. The PROI image was thus divided into six patches accordingly (Fig. 1).
After the polar layer, the segment patches are transferred from sector shape to a rectangular shape, which is then fed into subsequent convolutional layers for further processing. Here, infarction detection was converted to a task to categorize the segment patches within normal and abnormal groups. A deep residual network was designed to classify PROI image section patch images. The structure of the deep polar residual network (PResNet) consists of the polar layer, convolutional layers, softmax layer, residual component, and classification component. The connection between the polar layer and the following layers was not trainable.
Rather than building a model from scratch, we improved the architecture of a pre-designed ResNet model and refined its weighting for identifying new images. Known as transfer learning, this decreases the necessary time investment for training and enhances the generalization ability of the network. The proposed model adds the polar layer on a pre-trained ResNet-50 network, and a binary cross-entropy function was used as the loss function for classification. With the pre-train ResNet-50 model as a backbone, different layers were added to categorize segment images into normal and abnormal groups. The whole system and related image results are illustrated in Figure 2. The PResNet model analyzed all MCE images three times consecutively.
Observer study
Six radiologists with varying levels of experience in echocardiography (1 to 15 years), blinded to the experimental process and pathology results, analyzed all MCE images independently. Three of the six radiologists were junior individuals with less echocardiography experience (1, 2, and 3 years, respectively), while the other three were senior members (10, 13, and 15 years of experience, respectively). All radiologists graded each segment according to the wall motion score (WMS) and the degree of myocardial perfusion in the MCE. Semiquantitative-WMS scoring system is as follows: WMS=1, normal or hyperkinetic; WMS=2, hypokinetic (reduced thickening); WMS=3, akinetic (absent or negligible thickening, e.g., scar); WMS=4, dyskinetic (systolic thinning or stretching, e.g., aneurysm) [32]. The degree of myocardial perfusion was also scored similarly: 1=normal perfusion, 2= subendocardial perfusion defect, and 3=transmural perfusion defect [33]. A segment was deemed to be infarcted when the sum of its two scores was equal to or greater than six. Segments that did not meet these criteria were deemed to be non-infarcted.
Statistical analysis
A receiver operating characteristic (ROC) analysis was applied to assess the diagnostic accuracy of both the radiologists and the deep learning model. The empirical ROC curve was formed by connecting the set of data points generated from the different cut points. The area under the ROC curve (AUC) was also calculated as a metric to evaluate the method’s performance. Operator classification sensitivity and specificity were calculated, as well as the analysis duration for junior radiologists, senior radiologists, and the proposed deep learning model per rat. Analysis time medians were compared using Friedman analysis with multiple comparison post-hoc testing (Bonferroni). Box and whisker plots were obtained to present analysis time distributions A p-value less than 0.05 was considered to indicate a statistically significant difference. Interobserver variability was assessed with Cohen’s kappa coefficient (Table 1). The Medcalc statistical software (Version 19.0.4 Schoonjans, Frank) was used to take AUC comparison and k statistics. Other statistical analyses were performed with the SPSS software (version 23, Chicago, IL, USA).