Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator.

The minor copper (Cu) particles among major aluminum (Al) particles have been detected by means of an integration of a generative adversarial network and electrical impedance tomography (GAN-EIT) for a wet-type gravity vibration separator (WGS). This study solves the problem of blurred EIT reconstructed images by proposing a GAN-EIT integration system for Cu detection in WGS. GAN-EIT produces two types of images of various Cu positions among major Al particles, which are (1) the photo-based GAN-EIT images, where blurred EIT reconstructed images are enhanced by GAN based on a full set of photo images, and (2) the simulation-based GAN-EIT images. The proposed metal particle detection by GAN-EIT is applied in experiments under static conditions to investigate the performance of the metal detection method under single-layer conditions with the variation of the position of Cu particles. As a quantitative result, the images of detected Cu by GAN-EIT ψÌ¿GAN in different positions have higher accuracy as compared to σ*EIT. In the region of interest (ROI) covered by the developed linear sensor, GAN-EIT successfully reduces the Cu detection error of conventional EIT by 40% while maintaining a minimum signal-to-noise ratio (SNR) of 60 [dB]. In conclusion, GAN-EIT is capable of improving the detailed features of the reconstructed images to visualize the detected Cu effectively.

On-Line Multi-Frequency Electrical Resistance Tomography (mfERT) Device for Crystalline Phase Imaging in High-Temperature Molten Oxide.

An on-line multi-frequency electrical resistance tomography (mfERT) device with a melt-resistive sensor and noise reduction hardware has been proposed for crystalline phase imaging in high-temperature molten oxide. The melt-resistive sensor consists of eight electrodes made of platinum-rhodium (Pt-20mass%Rh) alloy covered by non-conductive aluminum oxide (Al2O3) to prevent an electrical short. The noise reduction hardware has been designed by two approaches: (1) total harmonic distortion (THD) for the robust multiplexer, and (2) a current injection frequency pair: low fL and high fH, for thermal noise compensation. THD is determined by a percentage evaluation of k-th harmonic distortions of ZnO at f=0.1~10,000 Hz. The fL and fH are determined by the thermal noise behavior estimation at different temperatures. At  f <100 Hz, the THD percentage is relatively high and fluctuates; otherwise, THD dramatically declines, nearly reaching zero. At the determined fL≥ 10,000 Hz and fH≈ 1,000,000 Hz, thermal noise is significantly compensated. The on-line mfERT was tested in the experiments of a non-conductive Al2O3 rod dipped into conductive molten zinc-borate (60ZnO-40B2O3) at 1000~1200 °C. As a result, the on-line mfERT is able to reconstruct the Al2O3 rod inclusion images in the high-temperature fields with low error, ςfL, T = 5.99%, at 1000 °C, and an average error ⟨ςfL⟩ = 9.2%.

Long-term phase angle muscle imaging under electrical muscle stimulation (EMS) by phase angle electrical impedance tomography.

Objectives. Phase angle muscle imaging has been proposed by phase angle electrical impedance tomography (ΦEIT) under electrical muscle stimulation (EMS) for long-term monitoring of muscle quality improvement, especially focusing on calf muscles.Approach. In the experiments, twenty-four subjects are randomly assigned either to three groups: control group (CG,n= 8), low voltage intensity of EMS training group (LG,n= 8), and optimal voltage intensity of EMS training group (OG,n= 8).Main results. From the experimental results, phase angle distribution imagesФare cleared reconstructed by ФEIT as four muscle compartments over five weeks experiments, which are called theM1muscle compartments composed of gastrocnemius muscle,M2muscle compartments composed of soleus muscle,M3muscle compartments composed of tibialis-posterior muscle, flexor digitorum longus muscle, and flexor pollicis longus muscle, andM4muscle compartment composed of the tibialis anterior muscle, extensor digitorum longus muscle, and peroneus longus muscle.Фis inversely correlated with age, namely theФdecreases with increasing age. A paired samplest-test was conducted to elucidate the statistical significance of spatial-mean phase angle in all domain <Ф>Ωand in each muscle compartment <Ф>Mwith reference to the conventional phase angle Ф by bioelectrical impedance analysis, muscle grey-scaleGmuscleby ultrasound, and maximal dynamic strengthSMaxby one-repetition maximum test.Significance. From thet-test results, <Ф>Ωhave good correlation with Ф andSMax. In the OG, <ФW5>Ω,ФW5, and (SMax)W5were significantly higher than in the first week (n= 8,p< 0.05). A significant increase in the phase angle of bothM1andM4muscle compartments is observed after five weeks in LG and OG groups. Only the OG group shows a significant increase in the phase angle ofM2muscle compartment after five weeks. However, no significant changes in the spatial-mean phase angle ofM3compartment are observed in each group. In conclusion, ФEIT satisfactorily monitors the response of each compartment in calf muscle to long-term EMS training.

Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD).

The comparison between breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation time distribution (EIT-GRTD) and conventional EIT has been conducted to evaluate the optimal frequency for cancer detection f cancer. The EIT-GRTD has two steps, which are 1) the determination of the f cancer and 2) the refinement of breast reconstruction through time-constant enhancement. This paper employs two-dimensional numerical simulations by a finite element method (FEM) software to replicate the process of breast cancer recognition. The simulation is constructed based on two distinct electrical properties, which are conductivity σ and permitivitty ε, inherent to two major breast tissues: adipose tissues, and breast cancer tissues. In this case, the σ and ε of breast cancer σ cancer, ε cancer are higher than adipose tissues σ adipose, ε adipose. The simulation results indicate that the most effective frequency for breast cancer detection based on EIT-GRTD is f cancer = 56,234 Hz. Meanwhile, conventional EIT requires more processing to determine the f cancer based on image results or spatial conductivity analysis. Quantitatively, both EIT-GRTD and conventional EIT can clearly show the position of the cancer in layers 1 and 2 for EIT-GRTD and only layer 1 for conventional EIT.

Detection of invasive ductal carcinoma by electrical impedance spectroscopy implementing gaussian relaxation-time distribution (EIS-GRTD).

Breast cancer detection and differentiation of breast tissues are critical for accurate diagnosis and treatment planning. This study addresses the challenge of distinguishing between invasive ductal carcinoma (IDC), normal glandular breast tissues (nGBT), and adipose tissue using electrical impedance spectroscopy combined with Gaussian relaxation-time distribution (EIS-GRTD). The primary objective is to investigate the relaxation-time characteristics of these tissues and their potential to differentiate between normal and abnormal breast tissues. We applied a single-point EIS-GRTD measurement to ten mastectomy specimens across a frequency rangef= 4 Hz to 5 MHz. The method calculates the differential ratio of the relaxation-time distribution functionΔγbetween IDC and nGBT, which is denoted byΔγIDC-nGBT,andΔγbetween IDC and adipose tissues, which is denoted byΔγIDC-adipose.As a result, the differential ratio ofΔγbetween IDC and nGBTΔγIDC-nGBTis 0.36, and between IDC and adiposeΔγIDC-adiposeis 0.27, which included in theα-dispersion atτpeak1=0.033±0.001s.In all specimens, the relaxation-time distribution functionγof IDCγIDCis higher, and there is no intersection withγof nGBTγnGBTand adiposeγadipose.The difference inγsuggests potential variations in relaxation properties at the molecular or structural level within each breast tissue that contribute to the overall relaxation response. The average mean percentage errorδfor IDC, nGBT, and adipose tissues are 5.90%, 6.33%, and 8.07%, respectively, demonstrating the model's accuracy and reliability. This study provides novel insights into the use of relaxation-time characteristic for differentiating breast tissue types, offering potential advancements in diagnosis methods. Future research will focus on correlating EIS-GRTD finding with pathological results from the same test sites to further validate the method's efficacy.

Detection of invasive ductal carcinoma in quadrant breast areas by electrical impedance tomography implemented with gaussian relaxation-time distribution (EIT-GRTD).

Invasive ductal carcinoma (IDC) in breast specimens has been detected in the quadrant breast area: (I) upper outer, (II) upper inner, (III) lower inner, and (IV) lower outer areas by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD). The EIT-GRTD consists of two steps which are (1) the optimum frequencyfoptselection and (2) the time constant enhancement of breast imaging reconstruction.foptis characterized by a peak in the majority measurement pair of the relaxation-time distribution functionγ,which indicates the presence of IDC.γrepresents the inverse of conductivity and indicates the response of breast tissues to electrical currents across varying frequencies based on the Voigt circuit model. The EIT-GRTD is quantitatively evaluated by multi-physics simulations using a hemisphere container of mimic breast, consisting of IDC and adipose tissues as normal breast tissue under one condition with known IDC in quadrant breast area II. The simulation results show that EIT-GRTD is able to detect the IDC in four layers atfopt= 30, 170 Hz. EIT-GRTD is applied in the real breast by employed six mastectomy specimens from IDC patients. The placement of the mastectomy specimens in a hemisphere container is an important factor in the success of quadrant breast area reconstruction. In order to perform the evaluation, EIT-GRTD reconstruction images are compared to the CT scan images. The experimental results demonstrate that EIS-GRTD exhibits proficiency in the detection of the IDC in quadrant breast areas while compared qualitatively to CT scan images.

Physiological-induced conductive response evaluation in specific muscle compartments under hybrid of electrical muscle stimulation and voluntary resistance training by electrical impedance tomography.

Objective: The physiological-induced conductive response has been visualised for evaluation in specific muscle compartments under hybrid (hybridEMS) of electrical muscle stimulation (EMS) and voluntary resistance training (VRT) by electrical impedance tomography (EIT). Methods: In the experiments, tendency of conductivity distribution images σ over time was clearly detected for three specific muscle compartments, which are called AM 1 compartment composed of biceps brachii muscle, AM 2 compartment composed of triceps brachii muscle, and AM 3 compartment composed of brachialis muscle, under three training modalities. Results: From the experimental results, the tendency of physiological-induced conductive response are increased in all three training modalities with increasing training time. Correspondingly, the spatial-mean conductivity <σ>AM1,AM2,AM3 increased with the conductance value G and extracellular water ratio ß of right arm by bio-impedance analysis (BIA) method. In addition, hybridEMS has the greatest effect on physiological-induced conductive response in AM 1, AM 2, and AM 3. Under hybridEMS, the spatial-mean conductivity increased from <σ pre > AM1 = 0.154 to <σ 23mins > AM1 = 0.810 in AM 1 muscle compartment (n = 8, p < 0.001); <σ pre > AM2 = 0.040 to <σ 23mins > AM2 = 0.254 in AM 2 muscle compartment (n = 8, p < 0.05); <σ pre > AM3 = 0.078 to <σ 23mins > AM3 = 0.497 in AM 3 muscle compartment (n = 8, p < 0.05). Conclusion: The paired-samples t-test results of <σ>AM1,AM2,AM3 under all three training modalities suggest hybridEMS has the most efficient elicitation on physiological induced conductive response compared to VRT and EMS. The effect of EMS on deep muscle compartment (AM 3) is slower compared to VRT and hybridEMS, with a significant difference after 15 min of training.