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TSS-ConvNet for electrical impedance tomography image reconstruction.

Ayman A Ameen1, Achim Sack2, Thorsten Pöschel2

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Summary

A new truncated spatial-spectral convolutional neural network (TSS-ConvNet) effectively solves ill-posed inverse problems. This data-driven method accurately detects bubble location and size in pipes using simulation and experimental data.

Keywords:
deep neural networkelectrical impedance tomographyill posed inverse problemstruncated spatial-spectral convolutional neural network

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Area of Science:

  • Engineering
  • Applied Mathematics
  • Computer Science

Background:

  • Ill-posed inverse problems are challenging, especially in applications like time-difference electrical impedance tomography.
  • Existing models often struggle with limited receptive fields, relying solely on local Euclidean information.

Purpose of the Study:

  • To propose a novel data-driven method for solving ill-posed inverse problems.
  • To address challenges in detecting bubble location and size within pipes using time-difference electrical impedance tomography.

Main Methods:

  • Introduced a truncated spatial-spectral convolutional neural network (TSS-ConvNet) with interconnected spatial, spectral, and truncated spectral paths.
  • The architecture incorporates a bottleneck design to recover signal information from noisy measurements.
  • Trained the network on a diverse dataset with random configurations for robust generalization.

Main Results:

  • The TSS-ConvNet demonstrated superior accuracy and high resolution on both simulated and experimental data.
  • The model effectively overcomes the receptive field limitations of existing methods.
  • Achieved accurate detection of bubble location and size in complex conditions.

Conclusions:

  • The TSS-ConvNet offers a significant advancement in solving ill-posed inverse problems.
  • This data-driven approach shows strong potential for real-world applications requiring precise measurements.
  • The model's ability to integrate local and global information enhances its performance and applicability.