+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Functional Evaluation of Percutaneous Coronary Intervention Based on CT Images of Three-Dimensional Reconstructed Coronary Artery Model


      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          In order to explore the computerized tomography (CT) based on three-dimensional reconstruction of coronary artery model, the functional evaluation was made after percutaneous coronary intervention (PCI). In this study, 90 patients with coronary heart disease who received elective PCI were selected. The blood flow reserve fraction (FFR) and SYNTAX score were calculated by three-dimensional reconstruction of CT images, followed up for 2–4 years. According to the SYNTAX score, 0–22 points were defined as the low group (28 cases), 23–32 points as the medium group (33 cases), and 33 points as the high group (29 cases). In this paper, the accuracy, sensitivity, and specificity of CT images of three-dimensional reconstructed coronary artery model are 91%, 73%, and 62%, respectively. The follow-up results showed that the incidence of major adverse cerebrovascular events in the high group was significantly higher than that in the low group and the middle group, and the difference was statistically significant ( P < 0.05). Pearson correlation analysis showed that SYNTAX score was related to serum total cholesterol ( r = 0.234, P=0.003), triglyceride ( r = 0.237, P=0.014), low-density lipoprotein cholesterol ( r = 0.285, P=0.004), and ApoB/ApoA1 ( R = 0.004). In this study, FFR is calculated by CT images based on three-dimensional reconstruction of coronary artery model, which can provide support for the diagnosis and treatment of coronary heart disease. SYNTAX score can be used as a risk predictor for PCI patients with coronary heart disease.

          Related collections

          Most cited references24

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Fuzzy System Based Medical Image Processing for Brain Disease Prediction

          The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

            The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.
              • Record: found
              • Abstract: found
              • Article: not found

              Blinded Physiological Assessment of Residual Ischemia After Successful Angiographic Percutaneous Coronary Intervention

              This study sought to evaluate the incidence and causes of an abnormal instantaneous wave-free ratio (iFR) after angiographically successful percutaneous coronary intervention (PCI).

                Author and article information

                Contrast Media Mol Imaging
                Contrast Media Mol Imaging
                Contrast Media & Molecular Imaging
                7 April 2023
                : 2023
                : 6761830
                1Department of Cardiology, Integrated Traditional Chinese and Western Medicine, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing 100029, China
                2Graduate School, Peking Union Medical College, Beijing 100730, China
                3Beijing Escope Tech Co Ltd, Beijing, China
                Author notes

                Academic Editor: Mohammad Farukh Hashmi

                Author information
                Copyright © 2023 Dongliang Fu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 2 July 2022
                : 29 July 2022
                : 30 July 2022
                Funded by: Capital Health Research and Development of Special
                Award ID: 2018-2-4064
                Research Article


                Comment on this article