A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction
Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/9/1114 |
| _version_ | 1797519449301975040 |
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| author | Dan Yang Yuchen Wang Bin Xu Xu Wang Yanjun Liu Tonglei Cheng |
| author_facet | Dan Yang Yuchen Wang Bin Xu Xu Wang Yanjun Liu Tonglei Cheng |
| author_sort | Dan Yang |
| collection | DOAJ |
| description | Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases. |
| first_indexed | 2024-03-10T07:42:56Z |
| format | Article |
| id | doaj.art-d36049cdcf4a434f87defbd868f93d77 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T07:42:56Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-d36049cdcf4a434f87defbd868f93d772023-11-22T12:56:46ZengMDPI AGEntropy1099-43002021-08-01239111410.3390/e23091114A Deep Neural Network Method for Arterial Blood Flow Profile ReconstructionDan Yang0Yuchen Wang1Bin Xu2Xu Wang3Yanjun Liu4Tonglei Cheng5School of Information Science & Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Information Science & Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaSchool of Information Science & Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Information Science & Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Information Science & Engineering, Northeastern University, Shenyang 110819, ChinaArterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.https://www.mdpi.com/1099-4300/23/9/1114arterial blood flow profile reconstructionartery stenosisdeep neural networkelectromagnetic effect |
| spellingShingle | Dan Yang Yuchen Wang Bin Xu Xu Wang Yanjun Liu Tonglei Cheng A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction Entropy arterial blood flow profile reconstruction artery stenosis deep neural network electromagnetic effect |
| title | A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction |
| title_full | A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction |
| title_fullStr | A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction |
| title_full_unstemmed | A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction |
| title_short | A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction |
| title_sort | deep neural network method for arterial blood flow profile reconstruction |
| topic | arterial blood flow profile reconstruction artery stenosis deep neural network electromagnetic effect |
| url | https://www.mdpi.com/1099-4300/23/9/1114 |
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