Experimental evaluation of some current injection-voltage reading patterns in electrical impedance tomography (EIT) and comparison to simulation results - Case study: Large scales

Tarabi, Nazilla; Mousazadeh, Hossein; Jafari, Ali; Taghizadeh-Tameh, Jalil; Kiapey, Ali

doi:10.1016/j.flowmeasinst.2021.102087

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…In another phantom study, configurations of even values of electrode skipping patterns were found better than those of odd values (Luppi Silva et al 2017). Recently, Tarabi et al recommended that in industrial application of EIT for conductive carrier-phases and in large domains, the patterns of opposite current injection and adjacent voltage measurement should be used because the highest correlation of image reconstruction of simulation with experimental data could be obtained with this pattern (Tarabi et al 2022). Russo et al argued that arrangement of the adjacent current injection and the opposite voltage measurement was preferable in the optimization of EIT-based stretchable sensors (Russo et al 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Several groups have studied the influence of current injection on EIT reconstruction (e.g. Seagar et al 1987, Xu et al 2008, Zhang et al 2010, Adler et al 2011, Bera and Nagaraju 2012, Luppi Silva et al 2017, Russo et al 2017, Tarabi et al 2022. Most of the previous studies employed simulation or phantoms with homogeneous impedance distribution, which is far different from the actual human chest with highly inhomogeneous impedance distribution.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system

Yang,

Gao,

Wang

et al. 2024

Physiol. Meas.

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BackgroundWearable electrical impedance tomography (EIT) can be used to monitor regional lung ventilation and perfusion at the bedside. Due to its special system architecture, the amplitude of the injected current is usually limited compared to stationary EIT system. This study aims to evaluate the performance of current injection patterns with various low-amplitude currents in healthy volunteers.MethodsA total of 96 test sets of EIT measurement was recorded in 12 healthy subjects by employing adjacent and opposite current injection patterns with four amplitudes of small current (i.e. 1 mA, 500 uA, 250 uA and 125 uA). The performance of the two injection patterns with various currents was evaluated in terms of signal-to-noise ratio (SNR) of thorax impedance, EIT image metrics and EIT-based clinical parameters.ResultsCompared with adjacent injection, opposite injection had higher SNR (p<0.01), less inverse artifacts (p<0.01), and less boundary artifacts (p<0.01) with the same current amplitude. In addition, opposite injection exhibited more stable EIT-based clinical parameters (p<0.01) across the current range. For adjacent injection, significant differences were found for three EIT image metrics (p<0.05) and four EIT-based clinical parameters (p<0.01) between the group of 125 uA and the other groups.ConclusionFor better performance of wearable pulmonary EIT, currents greater than 250 uA should be used in opposite injection, 500 uA in adjacent one, to ensure a high level of SNR, a high quality of reconstructed image as well as a high reliability of clinical parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system

Yang,

Gao,

Wang

et al. 2024

Physiol. Meas.

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“…Considering the electrode placement shown in figure 2, the adjacent pattern is used for injection and measurement, the BIM current source is initially connected to the electrodes 1 and 2, and the voltages are measured across each pair of consecutive electrodes, provided they are different from those used for injection [35].…”

Section: Injection and Measurement Patternsmentioning

confidence: 99%

Machine learning-based bioimpedance assessment of knee osteoarthritis severity

Muñoz,

Mosquera,

Rengifo

et al. 2024

Biomed. Phys. Eng. Express

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This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA. All participants performed 20 repetitions of each of the following five tasks: (i) sitting withthe knee bent, (ii) sitting with the knee extended, (iii) sitting and performing successive extensions and flexions of the knee, (iv) standing, and (v) walking. Data from the 15 experimental setups (3 types of measurements × 5 exercises) were used to train a multiclass random forest. The training and validation cycle was repeated 100 times using random undersampling. At each of the 100 cycles, 80% of the data were used for training and the rest for testing. The results showed that the proposed approach achieved average sensitivities and specificities of 100% for the four KAO severity grades in the extensión, cyclic, and gait tasks. This suggests that the proposed method can serve as a screening tool to determine which individuals should undergo X-rays or magnetic resonance imaging for further evaluation of KOA.

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“…12 In addition, image reconstruction is also a hot topic which refers to calculation of internal conductivity distribution from boundary voltage measurements. 13,14 However, such a process is an inherently nonlinear and seriously ill-posed problem. 15 The nonlinearity is caused by soft field characteristic of the electric field which makes the distribution of sensitive field easily affected by medium distribution.…”

Section: Introductionmentioning

confidence: 99%

“…In EIT, a variety of studies have been conducted, such as quantization error and electrode position errors in the modeling 12 . In addition, image reconstruction is also a hot topic which refers to calculation of internal conductivity distribution from boundary voltage measurements 13 , 14 . However, such a process is an inherently nonlinear and seriously ill-posed problem 15 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography

et al. 2022

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Purpose: Electrical impedance tomography (EIT) has shown its potential in the field of medical imaging. Physiological or pathological variation would cause the change of conductivity. EIT is favorable in reconstructing conductivity distribution inside the detected area. However, due to its ill-posed and nonlinear characteristics, reconstructed images suffer from low spatial resolution.Approach: Tikhonov regularization method is a popular and effective approach for image reconstruction in EIT. Nevertheless, excessive smoothness is observed when reconstruction is conducted based on Tikhonov method. To improve Tikhonov-based reconstruction quality in EIT, an innovative hybrid iterative optimization method is proposed. An efficient alternating minimization algorithm is introduced to solve the optimization problem.Results: To verify image reconstruction performance and anti-noise robustness of the proposed method, a series of simulation work and phantom experiments is carried out. Meanwhile, comparison is made with reconstruction results based on Landweber, Newton-Raphson, and Tikhonov methods. The reconstruction performance is also verified by quantitative comparison of blur radius and structural similarity values which further demonstrates the excellent performance of the proposed method.Conclusions: In contrast to Landweber, Newton-Raphson, and Tikhonov methods, it is found that images reconstructed by the proposed method are more accurate. Even under the impact of noise, the proposed method outperforms comparison methods.

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Experimental evaluation of some current injection-voltage reading patterns in electrical impedance tomography (EIT) and comparison to simulation results - Case study: Large scales

Cited by 7 publications

References 23 publications

Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system

Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system

Machine learning-based bioimpedance assessment of knee osteoarthritis severity

Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography

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