Enhanced Multi-Scale Feature Cross-Fusion Network for Impedance-optical Dual-modal Imaging

Zhe Liu, Renjie Zhao, Graham Anderson, Pierre Bagnaninchi, Yunjie Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The intrinsic issue of low spatial resolution of Electrical Impedance Tomography (EIT) is a long-standing challenge that hinders the capability of performing quantitative analysis based on EIT image. Our recent work demonstrates an impedance-optical dual-modal imaging framework and a deep learning model named Multi-Scale Feature Cross Fusion Network (MSFCF-Net) to realize information fusion and high-quality EIT image reconstruction. However, this framework’s performance is limited by the accuracy of the mask image obtained from an auxiliary imaging modality. This paper further proposes a two-stage deep neural network, which is the enhanced version of the MSFCF-Net (named En-MSFCF-Net), to automatically improve mask image and conduct information fusion and image reconstruction. Compared to MSFCF-Net, En-MSFCF-Net demonstrates the superior ability to correct the inaccurate mask image, leading to a more accurate conductivity estimation. Furthermore, the En-MSFCF-Net also maintains the best shape preservation and conductivity prediction accuracy among given learning-based and model-based algorithms. Both qualitative and quantitative results indicate that En-MSFCF-Net could make dual-modal imaging more robust in real-world situations.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Sensors Journal
Early online date29 Aug 2022
Publication statusE-pub ahead of print - 29 Aug 2022


  • Conductivity
  • deep learning
  • Dual-modal imaging
  • Electrical impedance tomography
  • electrical impedance tomography
  • Feature extraction
  • image reconstruction
  • Image reconstruction
  • Imaging
  • information fusion
  • mask image correction
  • Sensors
  • Voltage


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