Reconstruction of Organ Boundaries With Deep Learning in the D-Bar Method for Electrical Impedance Tomography

Objective: Medical electrical impedance tomography is a non-ionizing imaging modality in which low-amplitude, low-frequency currents are applied on electrodes on the body, the resulting voltages are measured, and an inverse problem is solved to determine the conductivity distribution in the region o...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2021-03, Vol.68 (3), p.826-833
Main Authors: Capps, Michael, Mueller, Jennifer L.
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Biological systems
Boundaries
Computation
Conductivity
Deep Learning
Electric Impedance
Electrical impedance
electrical impedance tomography
Electrical resistivity
Image processing
Image Processing, Computer-Assisted
Image reconstruction
Impedance
Inverse problems
Machine learning
Mapping
Neural networks
Organs
Phantoms, Imaging
Scattering
Thorax
Tomography
Transforms
Voltage measurement
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Summary:Objective: Medical electrical impedance tomography is a non-ionizing imaging modality in which low-amplitude, low-frequency currents are applied on electrodes on the body, the resulting voltages are measured, and an inverse problem is solved to determine the conductivity distribution in the region of interest. Due the ill-posedness of the inverse problem, the boundaries of internal organs are typically blurred in the reconstructed image. Methods: A deep learning approach is introduced in the D-bar method for reconstructing a 2-D slice of the thorax to recover the boundaries of organs. This is accomplished by training a deep neural network on labeled pairs of scattering transforms and the boundaries of the organs in the data from which the transforms were computed. This allows the network to "learn" the nonlinear mapping between them by minimizing the error between the output of the network and known actual boundaries. Further, a "sparse" reconstruction is computed by fusing the results of the standard D-bar reconstruction with reconstructed organ boundaries from the neural network. Results: Results are shown on simulated and experimental data collected on a saline-filled tank with agar targets simulating the conductivity of the heart and lungs. Conclusions and Significance: The results demonstrate that deep neural networks can successfully learn the mapping between scattering transforms and the internal boundaries of structures.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2020.3006175