Journal of Northeastern University(Natural Science) ›› 2023, Vol. 44 ›› Issue (7): 931-937.DOI: 10.12068/j.issn.1005-3026.2023.07.003

• Information & Control • Previous Articles     Next Articles

A Reconstruction Algorithm for Electromagnetic Vascular Tomography Image Based on Fractal-DenseNet

YANG Dan1,2, WANG Yu-jia1, XIN Cai-ning1, XU Bin3   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. Key Laboratory of Data Analytics & Optimization for Smart Industry, Northeastern University, Shenyang 110819, China; 3. School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China.
  • Published:2023-07-13
  • Contact: XU Bin
  • About author:-
  • Supported by:
    -

Abstract: In view of the lack of underdetermination and pathology in electromagnetic vascular tomography image reconstruction, a Fractal-DenseNet based vascular tomography image reconstruction algorithm is proposed. With the principle of vascular tomography image reconstruction based on the magneto-electric coupling effect of blood flow, combining FractalNet’s fractal idea with DenseNet’s dense connection idea, a Fractal-DenseNet network model suitable for the inversion of blood flow velocity distribution was built for the reconstruction of vascular tomography image. Based on the magneto-electric coupling measurement model of human forearm ulnar artery flow, the corresponding data pairs of vessel section velocity and voltage signal caused by magneto-electric effect of blood flow were established, which were respectively used as the input and the output of the network model. Through supervised learning, the image reconstruction based on the flow velocity distribution of the vessel is realized. The results show that the root mean square error and correlation coefficients of Fractal-DenseNet reconstruction are 0.0078 and 99.28%, respectively. The proposed model has good anti-noise performance and can be used to reconstruct vascular tomography images under complex boundary conditions.

Key words: vascular tomography image reconstruction; Fractal-DenseNet; magneto-electric coupling effect of blood flow; ulnar artery of human forearm; supervised learning

CLC Number: