武居 昌宏

Spatio-temporal void fraction in air-water two-phase flow regime transitions has been visualized by combination of convolutional neural network and long short-term memory implemented into multiple -current-voltage (MCV-CNN_LSTM). The MCV-ML is composed of two components, which are MCV-CNN_LSTM training and evaluation. In the first component, four steps are carried out as 1) true void fraction α measurement was conducted by wire-mesh sensor (WMS) as objective variables, 2) simulated MCV voltage U calculation by simulation of MCV measurement for explanatory variable, 3) CNN_LSTM training instance generation, and 4) CNN_LSTM model training and testing. In the second component, two additional steps are experimentally conducted which are 5) actual MCV voltage V measurement by MCV for input of trained ML, and 6) spatio-temporal void fraction α prediction. For the dataset generation, the experiment was conducted on air-water two phase flow regime transitions in vertical pipe with an inner diameter of 25 mm and a length of 350 mm from the elbow. The superficial velocity of liquid and gas phase are varied to present the flow regime transition of bubbly to slug flow. As a result, the estimated spatio-temporal void fraction αˆ by MCV-CNN_LSTM enables the visualization of detailed gas distribution in vertical upward two-phase flow. The spatial averaged instantaneous void fraction measured by WMS and estimated by MCV-CNN_LSTM show qualitative agreement in normalized cross correlation (INCC) of 0.364 on its temporal variation.

Non-invasive hERG channel screening is achieved by integrating electrical impedance tomography (EIT) and extracellular voltage activation (EVA) into a PCB sensor.

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 ψ̿GAN 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.

Abstract Image reconstruction in electrical impedance tomography (EIT) is a typical ill-posed inverse problem, from which the stability of conductivity reconstruction affects the reliability of physiological parameters evaluation. In order to improve the stability, the effect of boundary voltage noise on conductivity reconstruction should be controlled. A noise-controlling method based on hybrid current-stimulation and voltage-measurement for EIT (HCSVM-EIT) is proposed for stable conductivity reconstruction. In HCSVM-EIT, the boundary voltage is measured by one current-stimulation and voltage-measurement pattern (high-SNR pattern) with a higher signal-to-noise ratio (SNR); the sensitivity matrix is calculated by another current-stimulation and voltage-measurement pattern (low-cond pattern) with a lower condition number; the boundary voltage is then transformed from the high-SNR pattern into the low-cond pattern by multiplying by an optimized transformation matrix for image reconstruction. The stability of conductivity reconstruction is improved by combining the advantages of the high-SNR pattern for boundary voltage measurement and the low-cond pattern for sensitivity matrix calculation. The simulation results show that the HCSVM-EIT increases the correlation coefficient (CC) of conductivity reconstruction. The experiment results show that the CC of conductivity reconstruction of the human lower limb is increased from 0.3424 to 0.5580 by 62.97% compared to the quasi-adjacent pattern, and from 0.4942 to 0.5580 by 12.91% compared to the adjacent pattern. In conclusion, the stable conductivity reconstruction with higher CC in HCSVM-EIT improves the reliability of physiological parameters evaluation for disease detection.

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

In this study, sodium concentration in the dermis layer is imaged by the square wave open electrical impedance tomography (SW-oEIT) with spatial voltage thresholding (SVT). The SW-oEIT with SVT consists of three steps which are (1) voltage measurement, (2) spatial voltage thresholding, and (3) sodium concentration imaging. In the 1st step, the root mean square voltageṽis calculated based on the measured voltagevunder the square wave currentIthrough the planar electrodes on the skin domain Ω. In the 2nd step, them-th measured voltagevis converted to a compensated voltagev*based on the voltage electrodes distancedvand threshold distancedΓin order to highlight the region of interest of the dermis layerΩd.In the 3rd step, sodium concentration is imaged by the Gauss-Newton reconstruction method. The SW-oEIT with SVT was applied to multi-layer skin simulation andex-vivoexperiments under various dermis sodium concentrationscin the range of 5-50 mM. As an image evaluation result, the spatial mean conductivity distributionσ*inΩdis successfully determined as increasingcon both simulations and experiments. The relationship between〈σ*〉andcwas evaluated by the determination coefficientR2and the normalized sensitivity〈S〉.The optimizeddΓwith the highest evaluation values ofR2=0.84 and〈S〉=0.83 is under the condition ofdΓ= 2 mm. Based on the signal evaluation, the SW-oEIT with SVT has a 15.32% higher correlation coefficientCCcompared to the conventionaloEIT based on sinewave injection.

本論文は,エンドオブライフ期にある患者の浮腫/リンパ浮腫治療やケアを導くエビデンスを見出すことを目指し,関連文献の体系的マッピングレビューを行った.医中誌Web,PubMed等の文献データベース等から抽出した文献336件において,2008-2019年に発刊された対象文献11件を特定した.主な治療やケア内容は,複合的治療や利尿剤投与等,アウトカム指標は四肢状態や機能,QOL等であったが,評価指標は統一されていないことが明示された.(著者抄録)

Abstract The assessment method of anisotropic transmembrane transport coefficient vector P of a cell-spheroid under inhomogeneous ion concentration fields has been proposed by combining electrical impedance tomography (EIT) with an ion transport model to evaluate the anisotropic transmembrane transport of ions. An element P_i of P represents the transmembrane transport coefficient of the ith part of the cell membrane, which is assessed by the ion transport model from the average conductivity σ̃_i of the ith extracellular sector reconstructed by EIT. Anisotropic factor H obtained from P_i is introduced, which represents the anisotropic transmembrane transport. To validate our methodology, the inhomogeneous ion concentration fields are generated by injecting two tonicity-different sucrose solutions (isotonic, hypotonic or hypertonic) from both sides of the cell-spheroid. As a result, the inhomogeneous ion concentration distribution due to the anisotropic transmembrane transport is successfully observed from the reconstructed image by EIT. The anisotropic factor H shows that H = 0.34 ± 0.24 in isotonic and hypertonic combination, H = 0.58 ± 0.15 in isotonic and hypotonic combination and H = 0.23 ± 0.06 in hypertonic and hypotonic combination, respectively. To verify the results obtained by our methodology, the fluorescence ratio F [-] of potassium ions around the cell-spheroid is observed under three combinations as same as the EIT measurement. F shows the anisotropic transmembrane transport with the same trend with the EIT results.

<jats:title>Abstract</jats:title><jats:p>Gastric Function has been successfully estimated by gastric electrical impedance tomography (<jats:italic>g</jats:italic>EIT) Suit with dual-step fuzzy clustering. The <jats:italic>g</jats:italic>EIT Suit which are made of elastic cloth with dual-planar electrodes and compact data acquisition (DAQ) system measures gastric impedance Z to visualize the gastric conductivity distribution σ. The dual-step fuzzy clustering extracts the clustered gastric conductivity distribution <jats:sup><jats:italic>k</jats:italic></jats:sup>σ, which accurately estimates the gastric function<jats:italic>.</jats:italic> The <jats:italic>g</jats:italic>EIT Suit with dual-step fuzzy clustering are applied to eight healthy persons during liquid meal consumption to estimate the gastric function under gastric accommodation phase of 200, 400 and 600 mL based on the gastric emptying phase. As the results, the <jats:italic>g</jats:italic>EIT Suit successfully estimate the gastric function. By the measured impedance Z, the subjects have a mean temporal impedance <jats:inline-formula><jats:alternatives><jats:tex-math>$$\overline{\Delta \mathbf{Z} }$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mover> <mml:mrow> <mml:mi>Δ</mml:mi> <mml:mi>Z</mml:mi> </mml:mrow> <mml:mo>¯</mml:mo> </mml:mover> </mml:math></jats:alternatives></jats:inline-formula>= − 9.27 [Ohm] and <jats:italic>p</jats:italic>-value of that <jats:inline-formula><jats:alternatives><jats:tex-math>$$\overline{\mathbf{Z} }$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mover> <mml:mi>Z</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> </mml:math></jats:alternatives></jats:inline-formula><jats:italic>p</jats:italic>(<jats:italic>Z</jats:italic>) = 0.0013[–]as the t-test result. In the case of gastric conductivity distribution σ, the subjects have a value of spatial mean conductivity distribution ⟨σ⟩ = 0.23[–] and <jats:italic>p</jats:italic>-value of that ⟨σ⟩ <jats:italic>p</jats:italic>(σ) = 0.0140[–]. Lastly, in the case gastric volume <jats:italic>V</jats:italic>, subjects have a gastric volume <jats:italic>V</jats:italic> = 12.44 [%] and <jats:italic>p</jats:italic>-value <jats:italic>p</jats:italic>(<jats:italic>V</jats:italic>) = 0.0664[–].</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The image reconstruction in electrical impedance tomography (EIT) has low accuracy due to the approximation error between the measured voltage change and the approximated voltage change, from which the object cannot be accurately reconstructed and quantitatively evaluated. A voltage approximation model based on object-oriented sensitivity matrix estimation (OO-SME model) is proposed to reconstruct the image with high accuracy. In the OO-SME model, a sensitivity matrix of the object-field is estimated, and the sensitivity matrix change from the background-field to the object-field is estimated to optimize the approximated voltage change, from which the approximation error is eliminated to improve the reconstruction accuracy. Against the existing linear and nonlinear models, the approximation error in the OO-SME model is eliminated, thus an image with higher accuracy is reconstructed. The simulation shows that the OO-SME model reconstructs a more accurate image than the existing models for quantitative evaluation. The relative accuracy (RA) of reconstructed conductivity is increased up to 83.98% on average. The experiment of lean meat mass evaluation shows that the RA of lean meat mass is increased from 7.70% with the linear model to 54.60% with the OO-SME model. It is concluded that the OO-SME model reconstructs a more accurate image to evaluate the object quantitatively than the existing models.</jats:p>

Abstract Conductivity change in skin layers has been classified by source indicator o^k (k=1: Stratum corneum, k=2: Epidermis, k=3: Dermis, k=4: Fat, and k=5: Stratum corneum + Epidermis) trained from feedforward neural network (FNN) in bioelectrical impedance spectroscopy (BIS). In BIS studies, treating the skin as a bulk, limits the differentiation of conductivity changes in individual skin layers, however skin layer classification using FNN shows promise in accurately categorizing skin layers, which is essential for predicting source indicators o^k and initiating skin dielectric characteristics diagnosis. The o^k is trained by three main conceptual points which are (i) implementing FNN for predicting k in conductivity change, (ii) profiling four impedance inputs α_ξ consisting of magnitude input α|_z|, phase angle input α_θ, resistance input α_R, and reactance input α_x for filtering nonessential input, and (iii) selecting low and high frequency pair (frlh)$$(f_{r}^{lh})$$ by distribution of relaxation time (DRT) for eliminating parasitic noise effect. The training data set of FNN is generated to obtain the α_ξ ∈ R^10×17×10 by 10,200 cases by simulation under configuration and measurement parameters. The trained skin layer classification is validated through experiments with porcine skin under various sodium chloride (NaCl) solutions C_NaCl = {15, 20, 25, 30, 35}[mM] in the dermis layer. FNN successfully classified conductivity change in the dermis layer from experiment with accuracy of 90.6% for the bipolar set-up at f6lh=10 &amp;100 [kHz]$$f_{6}^{lh}=10\,\And 100\,{\rm{[kHz] } }$$ and with the same accuracy for the tetrapolar at f8lh=35 &amp;100 [kHz]$$f_{8}^{lh}=35\,\And 100\,{\rm{[kHz] } }$$. The measurement noise and systematic error in the experimental results are minimized by the proposed method using the feature extraction based on α_ξ at frlh$$f_{r}^{lh}$$.

<jats:p>The influences of the superficial velocity and particle diameter on gas–solid flow characterizations in a rolling circulation fluidized bed (RCFB) are clarified by using the electrical capacitance tomography method. Gas–solid flow characterizations include the time-space averaged particle volume fraction ᾱz′, the time transition of the cross-sectional average particle volume fraction 〈α〉t, and the radial profile of the particle volume fraction 〈α〉r/R. Two kinds of particles are selected in the case that the particle diameter is 0.22 mm and the superficial velocity is set to 2.0, 3.0, and 4.0 m/s and in the case that the particle diameter is 0.45 mm and the superficial velocity is set to 3.0, 4.0, and 5.0 m/s. After analyzing the results, the main conclusions are able to be summarized as follows: First, when compared with the superficial velocity, ᾱz′ visualized from particle distribution images is much more influenced by the particle diameter. Second, the fluctuations of 〈α〉t and 〈α〉r/R tend to be more serious under the conditions of larger particle and lower riser’s height, which are able to be quantitatively explained by the fast Fourier transform of 〈α〉t and the spatial standard deviation 〈σs〉 of 〈α〉r/R. Consequently, a strategy to guarantee both particle distribution stability and particle distribution uniformity within the RCFB is proposed.</jats:p>

Abstract A low-power and handy gastric data acquisition (g-DAQ) system has been proposed to identify the gastric processes in the epigastric region with sectorial electrical impedance tomography (s-EIT) and K-means sectorial clustering algorithm. The g-DAQ with a wearable abdominal sensor investigates gastric retention levels in the epigastric region during the emptying process. A C-runtime engine with Secure Shell protocol optimized an ARM microprocessor and field programmable gate array-based system with bidirectional channels to perform real-time data acquisition. The s-EIT algorithm projects the gastric conductivity distribution in the epigastric region into a cross-sectional image. K-means clustering method quantitatively identifies the gastric content images on the epigastric region to monitor the different clustered conductivity ${\boldsymbol{\unicode{x03B1 } }_k}$. The phantom experiments evaluated the s-EIT using liver-shaped, bone-shaped, and gastric-shaped phantoms in an abdominal-shaped vessel to distinguish gastric phantom conductivity. In human experiments, the proposed method was applied to measure 15 samples of the emptying process to evaluate the retention level of liquid gastric content. As a result, the proposed g-DAQ successfully performed a rapid acquisition at least 50 times faster than the conventional method in terms of the data acquisition rate. The developed g-DAQ with 110 mm × 66 mm × 51 mm of dimensions and 0.16 kg of weight took 0.32 sec to measure 208 points per frame and consumed 3.67 Watt of average power operation. By drinking 500 ml of rehydration water with 0.69 S m⁻¹ of conductivity, In subjects 1–4 and phantoms, the maximum R(t) was identified on t₁ as the gastric volume is fully bloated. During 30 min, the emptying liquid content was well-indicated because the minimum R(t) was identified on t₁₅ as an empty state. The mean of the measurement results is −0.0387 with the linear equation R(t) = −0.0387t + 0.8105. In conclusion, the body mass index did not significantly affect the trendline.

A sensitive frequency f margin SFM has been determined for quantification of aggregated interstitial protein concentrations Фprot by frequency difference electrical impedance tomography fd-EIT. Impedances of increasing Фprot of albumin-globulin mixtures in phantoms were measured and changes in protein's adjacent f point conductivity Δσadj and loss factor Δε''adj plotted against f to determine a Фprot SFM fsen where σadj and ε''adj change significantly with Фprot. Results show that the Δσadj and Δε''adj dropped rapidly with increase in f up to f = 10 kHz then gradually to f = 100 kHz. The σ and ε” were only sensitive to Фprot as f increased from 1 to 10 kHz. For fd-EIT, optimum reference frequency fref was set at 100 kHz. At fsen = 1 kHz against fref, fd-EIT reconstructed σ due to 30% increase in Фprot with area ratio error ARE = 4.1% and a position error PE = 0.1 [-].

The use of 3D cell culture for tissue engineering and regenerative medicine applications often challenges conventional biochemical and optical assays. Impedance‐based cellular assays have shown their potential to retrieve dielectric parameters pertaining to cell behavior such as viability, proliferation, and differentiation for 2D adherent cell culture. Herein, simultaneous 3D impedance imaging and viability measurements of multiple large (&gt;2 mm) 3D cell cultures embedded in collagen gels are demonstrated. The method is facilitated by low‐resistance 3D printed scaffolds that can hold a 3D cell–gel system throughout cell culture while being transparent to impedance imaging. It is shown in silico and in vitro that frequency‐difference electrical impedance tomography (fd‐EIT) can nondestructively and in a label‐free way differentiate a variety of cell concentrations with a single miniature sensor in real time. This study paves the way toward the development of EIT imaging for the quantitative and noninvasive evaluation of tissue engineering products.

Dynamic fields visualization method of carbon-black (CB) volume fraction ΦCB distribution in Lithium-ion battery (LIB) cathode slurry has been proposed based on electrical resistance tomography (ERT) during the manufacturing process. The proposed method consists of an impedance analyzer, a switching circuit, and ΦCB distribution imaging algorism, archiving to the measurement speed of 5 frames per second. In experiments, ΦCB distribution was visualized by the proposed method in lab-scale LIB cathode manufacturing equipment. To qualitatively evaluate the ΦCB distribution images, those images are compared with scanning electron microscope (SEM) images. This comparison shows that the ΦCB distribution images are qualitatively consistent with SEM images. In addition, in order to quantitatively evaluate the proposed method, the accuracy of reconstructed ΦCB distribution is evaluated by electromagnetic field simulations. As a result, the root mean square errors RMSE between the known ΦCB distribution and that obtained by the proposed method was less than 0.56%.

Albumin diffusivity coefficient DaI in imitated porous structure of interstitial space has been estimated by means of the integration of albumin diffusion model (ADM) to electrical impedance tomography (EIT) (iADM-EIT) under five different porosity φ conditions (from φ1 = 0.922 to φ5 = 0.990) for transport phenomena quantification. The iADM-EIT was conducted by applying an iterative curve-fitting between spatio-temporal albumin concentration CaI derived from ADM and spatio-temporal distribution of conductivity difference Δσ reconstructed by EIT. The essential point of the iADM-EIT is the quantification of experimental CIa from Δσ by establishing a constitutive relationship among CIex a, Δσ, and φ. ADM is developed based on Fick's second law implemented in Krogh tissue cylinder. EIT is performed to image the Δσ caused by increase of CIa due to albumin diffusion phenomenon from a capillary to the imitated porous structure. The imitated porous structure is manufactured with agarose gel in a dynamic phantom including a capillary. As a result, the relative albumin diffusivity coefficient (D0a: free diffusivity of albumin coefficient) is increased with the increase of φ in the range from 0.271 to 0.694, which are correspondent to the literature data with percent average relative error δ = 6.83 ± 2.72%.