Research on inversion method of intravascular blood flow velocity based on convolutional neural network_Journal of Biomedical Engineering

Authors：

WANG Yuchen ^1,2 ,  YANG Dan ^1,2 , XU Bin ³ , ZHANG Xinyu ² , WANG Xu ²

1. Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education, Northeastern University, Shenyang 110819, P. R. China;
2. School of Information Science and Engineering, Northeastern University, Shenyang 110819, P. R. China;
3. School of Computer Science and Engineering, Northeastern University, Shenyang 110169, P. R. China;

Corresponding author：

YANG Dan, Email: yangdan@mail.neu.edu.cn

Keywords：

Magnetoelectric effect; Vascular stenosis; Convolutional neural network; Inversion of blood flow velocity; Unsupervised learning

DOI：

10.7507/1001-5515.202112038

Video：

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Blood velocity inversion based on magnetoelectric effect is helpful for the development of daily monitoring of vascular stenosis, but the accuracy of blood velocity inversion and imaging resolution still need to be improved. Therefore, a convolutional neural network (CNN) based inversion imaging method for intravascular blood flow velocity was proposed in this paper. Firstly, unsupervised learning CNN is constructed to extract weight matrix representation information to preprocess voltage data. Then the preprocessing results are input to supervised learning CNN, and the blood flow velocity value is output by nonlinear mapping. Finally, angiographic images are obtained. In this paper, the validity of the proposed method is verified by constructing data set. The results show that the correlation coefficients of blood velocity inversion in vessel location and stenosis test are 0.884 4 and 0.972 1, respectively. The above research shows that the proposed method can effectively reduce the information loss during the inversion process and improve the inversion accuracy and imaging resolution, which is expected to assist clinical diagnosis.

Citation： WANG Yuchen, YANG Dan, XU Bin, ZHANG Xinyu, WANG Xu. Research on inversion method of intravascular blood flow velocity based on convolutional neural network. Journal of Biomedical Engineering, 2022, 39(3): 561-569. doi: 10.7507/1001-5515.202112038 Copy

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23.	Guo Liang, Liu Guoqiang, Xia Hui. Magneto-acousto-electrical tomography with magnetic induction for conductivity reconstruction. IEEE Trans Biomed Eng, 2015, 62(9): 2114-2124.
24.	徐文臻, 沈悦, 冯坚强, 等. 一种用于多电极电磁流量计的速度重构设计. 测控技术, 2021, 40(6): 57-60.
25.	濮玉, 朱俊江, 张德涛, 等. 基于改进卷积神经网络的房颤筛查算法. 生物医学工程学杂志, 2021, 38(4): 686-694.
26.	续宝红, 丁冲, 徐桂芝. 卷积神经网络在阿尔茨海默病诊断中的应用研究. 生物医学工程学杂志, 2021, 38(1): 169-177, 184.
27.	Gu J X, Wang Z H, Kuen J, et al. Recent advances in convolutional neural networks. Pattern Recognit, 2018, 77: 354-377.
28.	Lecun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 1989, 1(4): 541-551.
29.	Italian national research council institute for applied physics/ Andreuccetti D, Fossi R, Petrucci C, et al. An internet resource for the calculation of the dielectric properties of body tissues in the frequency range 10 Hz-100 GHz. (1997) [2021-12-31]. http: // niremf.ifac.cnr.it/tissprop/.

1. Anxionnat R, Bracard S, Ducrocq X, et al. Intracranial aneurysms: clinical value of 3D digital subtraction angiography in the therapeutic decision and endovascular treatment. Radiology, 2001, 218(3): 799-808.
2. Sun Z, Choo G H, Ng K H. Coronary CT angiography: current status and continuing challenges. Br J Radiol, 2012, 85(113): 495-510.
3. Uchino A, Kato A, Takase Y, et al. Middle cerebral artery variations detected by magnetic resonance angiography. Eur Radiol, 2000, 10(4): 560-563.
4. Maythem A. Development of an electromagnetic induction method for non-invasive blood flow measurement. West Yorkshire: University of Huddersfield, 2016.
5. Yang D, Liu Y J, Xu B, et al. A blood flow volume linear inversion model based on electromagnetic sensor for predicting the rate of arterial stenosis. Sensors, 2019, 19(13): 3006.
6. 杨静芬. 多电极电磁血液流速仪建模与成像算法研究. 石家庄: 河北科技大学, 2015.
7. 赵民. 基于多电极电磁测量的血液流速仪研究. 石家庄: 河北科技大学, 2017.
8. 章伟睿, 张涛, 史学涛, 等. 基于差分迭代的电阻抗成像算法研究. 电工技术学报, 2021, 36(4): 747-755.
9. Yang D, Liu J H, Wang Y C, et al. Application of a generative adversarial network in image reconstruction of magnetic induction tomography. Sensors, 2021, 21(11): 3869.
10. 田文旭, 杨丹, 魏竹林, 等. 基于改进栈式自编码器的扩散光学层析成像逆问题求解方法研究. 生物医学工程学杂志, 2021, 38(4): 774-782.
11. Wang J, Han B. Application of a class of iterative algorithms and their accelerations to Jacobian-based linearized EIT image reconstruction. Inverse Probl Sci Eng, 2021, 29(8): 1108-1126.
12. Wei H Y, Soleimani M. Four dimensional reconstruction using magnetic induction tomography: experimental study. Electromagn Waves (Camb), 2012, 129: 17-32.
13. Yang K Y, Borijindargoon N, Ng B P, et al. Learning sparsifying transforms for image reconstruction in electrical impedance tomography//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto: IEEE, 2021: 1405-1409.
14. Liu Y J, Yang D, Duo Y H, et al. Numerical model and finite element simulation of arterial blood flow profile Reconstruction in a uniform magnetic field. J Phys D, 2020, 53(19): 195402.
15. Li Feng, Tan Chao, Dong Feng, et al. V-net deep imaging method for electrical resistance tomography. IEEE Sens J, 2020, 20(12): 6460-6469.
16. 叶明, 李晓丞, 刘凯, 等. 一种基于U2-Net模型的电阻抗成像方法. 仪器仪表学报, 2021, 42(2): 235-243.
17. Ren Shangjie, Sun Kai, Tan Chao, et al. A two-stage deep learning method for robust shape reconstruction with electrical impedance tomography. IEEE Trans Instrum Meas, 2020, 69(7): 4887-4897.
18. Tan Chao, Lv Shuhua, Dong Feng, et al. Image reconstruction based on convolutional neural network for electrical resistance tomography. IEEE Sens J, 2019, 19(1): 196-204.
19. Wei Z, Liu D, Chen X D. Dominant-current deep learning scheme for electrical impedance tomography. IEEE Trans Biomed Eng, 2019, 66(9): 2546-2555.
20. 姚健. 多电极电磁肢体血液流速仿真与流速公布重建研究. 石家庄: 河北科技大学, 2020.
21. Galili I, Kaplan D, Lehavi Y. Teaching faraday's law of electromagnetic induction in an introductory physics course. Am J Phys, 2006, 74(4): 337-343.
22. Chen S, Chew W C. Discrete electromagnetic theory with exterior calculus//2016 Progress in Electromagnetic Research Symposium (PIERS), Shanghai: IEEE, 2016: 896-897.
23. Guo Liang, Liu Guoqiang, Xia Hui. Magneto-acousto-electrical tomography with magnetic induction for conductivity reconstruction. IEEE Trans Biomed Eng, 2015, 62(9): 2114-2124.
24. 徐文臻, 沈悦, 冯坚强, 等. 一种用于多电极电磁流量计的速度重构设计. 测控技术, 2021, 40(6): 57-60.
25. 濮玉, 朱俊江, 张德涛, 等. 基于改进卷积神经网络的房颤筛查算法. 生物医学工程学杂志, 2021, 38(4): 686-694.
26. 续宝红, 丁冲, 徐桂芝. 卷积神经网络在阿尔茨海默病诊断中的应用研究. 生物医学工程学杂志, 2021, 38(1): 169-177, 184.
27. Gu J X, Wang Z H, Kuen J, et al. Recent advances in convolutional neural networks. Pattern Recognit, 2018, 77: 354-377.
28. Lecun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 1989, 1(4): 541-551.
29. Italian national research council institute for applied physics/ Andreuccetti D, Fossi R, Petrucci C, et al. An internet resource for the calculation of the dielectric properties of body tissues in the frequency range 10 Hz-100 GHz. (1997) [2021-12-31]. http: // niremf.ifac.cnr.it/tissprop/.

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