TSS-ConvNet for electrical impedance tomography image reconstruction

Ameen AA, Sack A, Pöschel T (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Physiological Measurement Institute of Physics: Hybrid Open Access

Book Volume: 45

Article Number: 045006

Journal Issue: 4

DOI: 10.1088/1361-6579/ad39c2

Abstract

Objective. The objective of this study was to propose a novel data-driven method for solving ill-posed inverse problems, particularly in certain conditions such as time-difference electrical impedance tomography for detecting the location and size of bubbles inside a pipe. Approach. We introduced a new layer architecture composed of three paths: spatial, spectral, and truncated spectral paths. The spatial path processes information locally, whereas the spectral and truncated spectral paths provide the network with a global receptive field. This unique architecture helps eliminate the ill-posedness and nonlinearity inherent in the inverse problem. The three paths were designed to be interconnected, allowing for an exchange of information on different receptive fields with varied learning abilities. Our network has a bottleneck architecture that enables it to recover signal information from noisy redundant measurements. We named our proposed model truncated spatial-spectral convolutional neural network (TSS-ConvNet). Main results. Our model demonstrated superior accuracy with relatively high resolution on both simulation and experimental data. This indicates that our approach offers significant potential for addressing ill-posed inverse problems in complex conditions effectively and accurately. Significance. The TSS-ConvNet overcomes the receptive field limitation found in most existing models that only utilize local information in Euclidean space. We trained the network on a large dataset covering various configurations with random parameters to ensure generalization over the training samples.

Authors with CRIS profile

Achim Sack Lehrstuhl für Multiscale Simulation of Particulate Systems (MSS) Thorsten Pöschel Lehrstuhl für Multiscale Simulation of Particulate Systems (MSS)

Involved external institutions

Sohag-University EG Egypt (EG)

How to cite

APA:

Ameen, A.A., Sack, A., & Pöschel, T. (2024). TSS-ConvNet for electrical impedance tomography image reconstruction. Physiological Measurement, 45(4). https://doi.org/10.1088/1361-6579/ad39c2

MLA:

Ameen, Ayman A., Achim Sack, and Thorsten Pöschel. "TSS-ConvNet for electrical impedance tomography image reconstruction." Physiological Measurement 45.4 (2024).

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