Numerical Evaluation of Complex Capacitance Measurement Using Pulse Excitation in Electrical Capacitance Tomography

Wanta, Damian; Makowiecka, Oliwia; Smolik, Waldemar T.; Kryszyn, Jacek; Domański, Grzegorz; Midura, Mateusz; Wróblewski, Przemysław

doi:10.3390/electronics11121864

Cited by 6 publications

(8 citation statements)

References 32 publications

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“…Our experiment numerically simulated the actual CCEIT device used in our laboratory. The bioimpedance measurement method used by our system is described in detail in [ 16 ]. We simulated a two-wire measurement method with pulse excitation.…”

Section: Methodsmentioning

confidence: 99%

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Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Ivanenko,

Wanta,

Smolik

et al. 2024

Life

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This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was examined. The big data required for supervised network training were generated using a two-dimensional numerical simulation. The phantom of an axial cross-section of the head without and with impact lesions was an average of a three-centimeter-thick layer corresponding to the height of the sensing electrodes. Stroke was modeled using regions with characteristic electrical parameters for tissues with reduced perfusion. The head phantom included skin, skull bone, white matter, gray matter, and cerebrospinal fluid. The coupling capacitance was taken into account in the 16-electrode capacitive sensor model. A dedicated ECTsim toolkit for Matlab was used to solve the forward problem and simulate measurements. A conditional generative adversarial network (cGAN) was trained using a numerically generated dataset containing samples corresponding to healthy patients and patients affected by either hemorrhagic or ischemic stroke. The validation showed that the quality of images obtained using supervised learning and cGAN was promising. It is possible to visually distinguish when the image corresponds to the patient affected by stroke, and changes caused by hemorrhagic stroke are the most visible. The continuation of work towards image reconstruction for measurements of physical phantoms is justified.

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Section: Methodsmentioning

confidence: 99%

“…The coupling capacitance value must be high to make the contact susceptance significant compared to the measured conductivity. The changes in the coupling capacitance value during the measurement also disturb the results, but the monitoring of coupling capacitance during measurement is theoretically possible [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Ivanenko,

Wanta,

Smolik

et al. 2024

Life

Self Cite

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“…A model of a 2D slice of the human thorax, proposed in [ 60 ], was used in the generation of the training dataset. The proposed thorax phantom is based on axial lung CT slice segmentation [ 63 ] and presents the patient lying on his back.…”

Section: Machine Learning Approach In Cceitmentioning

confidence: 99%

“…We propose a modification of the thorax numerical phantom presented in [ 60 ], in which the lung displacement in the pleural cavity and shape deformation are modeled using elliptical regions. We assume individual differences in the shape and position of the organs in the patient’s chest.…”

Section: Introductionmentioning

confidence: 99%

Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax

Ivanenko,

Smolik,

Wanta

et al. 2023

Sensors

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Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results.

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“…Due to the limitations of invasive methods of process monitoring, more and more attempts are being made to use noninvasive tomographic methods, i.e., electrical capacitance tomography (ECT) [24,44,45,47], electrical impedance tomography (EIT) [10,11,22,29,41], magnetoacoustic tomography [48,49], electrical resistance tomography [27,38,46], ultrasonic tomography [19,21,31], or computed tomography using radiation X [3,30].…”

Section: Introductionmentioning

confidence: 99%

Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography – a hybrid approach

Kłosowski¹,

Rymarczyk²,

Niderla³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The article presents a new concept for monitoring industrial tank reactors. The presented concept allows for faster and more reliable monitoring of industrial processes, which increases their reliability and reduces operating costs. The innovative method is based on electrical tomography. At the same time, it is non-invasive and enables the imaging of phase changes inside tanks filled with liquid. In particular, the hybrid tomograph can detect gas bubbles and crystals formed during industrial processes. The main novelty of the described solution is the simultaneous use of two types of electrical tomography: impedance and capacitance. Another novelty is the use of the LSTM network to solve the tomographic inverse problem. It was made possible by taking the measurement vector as a data sequence. Research has shown that the proposed hybrid solution and the LSTM algorithm work better than separate systems based on impedance or capacitance tomography.

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Numerical Evaluation of Complex Capacitance Measurement Using Pulse Excitation in Electrical Capacitance Tomography

Cited by 6 publications

References 32 publications

Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax

Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography – a hybrid approach

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