Application of cascaded GAN based on CT scan in the diagnosis of aortic dissection.

PURPOSE: Currently, Computed Tomography Angiography (CTA) is the most commonly used clinical method for the diagnosis of aortic dissection, which is much better than plain CT. However, CTA examination has some disadvantages such as time-consuming image processing, complicated procedure and injection of developer. CT plain scanning is widely used in the early diagnosis of arterial dissection because of its convenience, speed and popularity. In order not to delay the optimal diagnosis and treatment time of patients, we use deep learning technology and network model to synthesize plain CT images into CTA images. Patients can be timely professional related departments of clinical diagnosis and treatment, and reduce the rate of missed diagnosis. In this paper, we propose a CTA image synthesis technique for cardiac aortic dissection based on the cascaded generative adjunctive network model. METHOD: Firstly, we registered CT images, and then used nnU-Net segmentation network model to obtain CT and CTA paired images containing only the aorta. Then we proposed a CTA image synthesis method for aortic dissection based on cascaded generative adversarial. The core idea is to build a cascade generator and double discriminator network based on DCT channel attention mechanism to further enhance the synthesis effect of CTA. RESULTS: The model is trained and tested on CT plain scan and CTA image data set of aortic dissection. The results show that the proposed model achieves good results in CTA image synthesis. In the CT data set, the nnU-Net model improves 8.63% and reduces 10.87mm errors in the key index DSC and HD, respectively, compared with the benchmark model U-Net. In CTA data set, nnU-Net model improves 10.27% and reduces 6.56mm error in key index DSC and HD, respectively, compared with benchmark model U-Net. In the synthesis task, the cascaded generative adm network is superior to Pix2pix and Pix2pixHD network models in both PSNR and SSIM, which proves that our proposed model has significant advantages. CONCLUSION: This study provides new possibilities for CTA image synthesis of aortic dissection, and improves the accuracy and efficiency of diagnosis, and hopes to provide substantial help for the diagnosis of aortic dissection.

Fully connected network with multi-scale dilation convolution module in evaluating atrial septal defect based on MRI segmentation.

BACKGROUND AND OBJECTIVE: Atrial septal defect (ASD) is a common congenital heart disease. During embryonic development, abnormal atrial septal development leads to pores between the left and right atria. ASD accounts for the largest proportion of congenital heart disease. Therefore, the design and implementation of an ASD intelligent auxiliary segmentation system based on deep learning segmentation of the atria has very important practical significance, which we aim to achieve in this paper. METHODS: This study proposes a multi-scale dilated convolution module, which is composed of three parallel dilated convolutions with different expansion coefficients. The original FCN network usually adopts bilinear interpolation or deconvolution methods when upsampling, both of which lead to information loss to a certain extent. In order to make up for the loss of information, it is expected that the final segmentation result can be directly connected to the deep features in the cardiac MRI. This study uses a dense upsampling convolution module, and in order to obtain the shallow position information, the original FCN jump connection module is still retained. In this research, a deep convolutional neural network for multi-scale feature extraction is designed through the multi-scale expansion convolution module. At the same time, this paper also implements two traditional machine learning segmentation methods (K-means and Watershed algorithms) and a deep learning algorithm (U-net) for comparison. RESULTS: The intelligent auxiliary segmentation algorithm for atrial images proposed in this framework based on multi-scale expansion convolution and adversarial learning can achieve superior results. Among them, the segmentation algorithm based on multi-scale expansion convolution can extract the associated features of pixels in multiple ranges, and can obtain deeper feature information when using a limited downsampling layer. According to the experimental results of the multi-scale expanded convolutional network on the data set, the Proportion of Greater Contour (PGC) index of the multi-scale expanded convolutional network is 98.78, the value of Average Perpendicular Distance (ADP) is 1.72mm, and the value of Overlapping Dice Metric (ODM) is 0.935, which are higher than other models. CONCLUSION: The experimental results show that compared with other segmentation models, the model based on multi-scale expansion convolution has significantly improved the accuracy of segmentation. Our technique will be able to assist in the segmentation of ASD, evaluation of the extent of the defect and enhance surgical planning via atrial septal occlusion.

Establish a Nomogram of Cardiac Postoperative Cognitive Dysfunction.

OBJECTIVES: Cardiac postoperative cognitive dysfunction (c-POCD) is a common complication. This article established a nomogram by analyzing preoperative and intraoperative data to help identify high-risk patients and take effective management for prevention of c-POCD in early stage. MEASUREMENTS AND MAIN RESULTS: A total of 265 patients were enrolled in this study, 27 of whom were diagnosed as cardiac postoperative cognitive dysfunction (c-POCD). Patients were divided into a control group and c-POCD group. Univariate analysis suggested that gender, smoking, drinking history, hypertension, white blood cell (WBC) count, aspartate aminotransferase (AST), high-sensitivity troponin (hs-CRP), arrhythmia, left atrial diameter (LAD), cardiopulmonary bypass (CPB) time, and the ascending aortic block (AAB) time were correlated with postoperative cognitive dysfunction after cardiac surgery. Multivariate regression analysis indicated that CPB time (P = 0.0015, OR (95% CI) = 6.696 (2.068-21.675), hypertension (P = 0.0098, OR (95%CI) = 3.776 (1.377-10.356), WBC count (P = 0.0227, OR (95%CI ) = 3.358 (1.184-9.522), AST (P = 0.0128, OR (95%CI) = 3.966 (1.340-11.735), and arrhythmia (P = 0.0017, OR (95%CI) = 5.164 (1.855-14.371) were the independent risk factors of cognitive dysfunction after cardiac surgery and used to establish a nomogram for clinical use. The initial C-index of the nomogram was 0.8182 and good calibration. Corrected C-index value of 0.793 was reached after internal validation. The area under ROC curve of this model was 0.8188 (95%CI: 0.7185-0.9190). The positive odds ratio (PLR) was 1.21 (95%CI: 1.1-1.3), and the negative odds ratio (NLR) was 0.18 (95%CI: 0.03-1.3). CONCLUSION: This nomogram incorporating the CPB time, hypertension, WBC count, AST, and arrhythmia to predict the risk of c-POCD. The internal validation shows a good forecasting effect.

Treatment of Giant Left Atrial Diverticulum: A Case Report and Literature Review.

In this paper, we present a giant left atrial diverticulum (LAD) in a 10-year-old girl, whose three-dimensional (3D) image reconstruction was used to help diagnosis and surgical positioning. Previously reported cases were reviewed, and the clinical characteristics of this disease also was summarized to improve the diagnosis and treatment of LAD.