{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T16:00:18Z","timestamp":1784044818591,"version":"3.55.0"},"reference-count":73,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"YOUNG PW grant under the Initiative of Excellence\u2014Research University program by the Ministry of Education and Science (PL)","award":["504\/04496\/1034\/45.010003\u20141820\/100\/Z01\/2023"],"award-info":[{"award-number":["504\/04496\/1034\/45.010003\u20141820\/100\/Z01\/2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23187774","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T10:42:49Z","timestamp":1694428969000},"page":"7774","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8682-2751","authenticated-orcid":false,"given":"Mikhail","family":"Ivanenko","sequence":"first","affiliation":[{"name":"Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1524-5049","authenticated-orcid":false,"given":"Waldemar T.","family":"Smolik","sequence":"additional","affiliation":[{"name":"Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1596-6524","authenticated-orcid":false,"given":"Damian","family":"Wanta","sequence":"additional","affiliation":[{"name":"Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2449-0652","authenticated-orcid":false,"given":"Mateusz","family":"Midura","sequence":"additional","affiliation":[{"name":"Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6713-9088","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"Wr\u00f3blewski","sequence":"additional","affiliation":[{"name":"Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohan","family":"Hou","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Control Engineering, Liaoning Technical University, No. 188 Longwan Street, Huludao 125105, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9048-9035","authenticated-orcid":false,"given":"Xiaoheng","family":"Yan","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Control Engineering, Liaoning Technical University, No. 188 Longwan Street, Huludao 125105, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Holder, D.S. (2005). Electrical Impedance Tomography: Methods, History and Applications (Series in Medical Physics and Biomedical Engineering), Institute of Physics Publishing.","DOI":"10.1201\/9781420034462"},{"key":"ref_2","first-page":"69","article-title":"Electrical Impedance Tomography; the Construction and Application to Physiological Measurement of Electrical Impedance Images","volume":"13","author":"Brown","year":"1987","journal-title":"Med. Prog. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Adler, A., Arnold, J.H., Bayford, R., Borsic, A., Brown, B., Dixon, P., Faes, T.J.C., Frerichs, I., Gagnon, H., and G\u00e4rber, Y. (2009). GREIT: A unified approach to 2D linear EIT reconstruction of lung images. Physiol. Meas., 30.","DOI":"10.1088\/0967-3334\/30\/6\/S03"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sapuan, I., Yasin, M., Ain, K., and Apsari, R. (2020). Anomaly Detection Using Electric Impedance Tomography Based on Real and Imaginary Images. Sensors, 20.","DOI":"10.3390\/s20071907"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1088\/0031-9155\/54\/2\/004","article-title":"Variation of the dielectric properties of tissues with age: The effect on the values of SAR in children when exposed to walkie\u2013talkie devices","volume":"54","author":"Peyman","year":"2008","journal-title":"Phys. Med. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1161\/01.RES.4.6.664","article-title":"Specific Resistance of Body Tissues","volume":"4","author":"Kay","year":"1956","journal-title":"Circ. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fbioe.2021.726652","article-title":"The Research Progress of Electrical Impedance Tomography for Lung Monitoring","volume":"9","author":"Shi","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"372","DOI":"10.55175\/cdk.v48i9.132","article-title":"The Role of Electrical Impedance Tomography in Lung Imaging","volume":"48","author":"Christanto","year":"2021","journal-title":"Cermin Dunia Kedokt."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"19273","DOI":"10.1038\/s41598-021-98793-0","article-title":"System Introduction and Evaluation of the First Chinese Chest EIT Device for ICU Applications","volume":"11","author":"Qu","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1088\/0967-3334\/33\/5\/679","article-title":"Whither Lung EIT: Where Are We, Where Do We Want to Go and What Do We Need to Get There?","volume":"33","author":"Adler","year":"2012","journal-title":"Physiol. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1109\/TMI.2019.2895035","article-title":"Capacitively Coupled Electrical Impedance Tomography for Brain Imaging","volume":"38","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"015034","DOI":"10.1088\/2632-2153\/acc637","article-title":"Machine Learning-Based Signal Quality Assessment for Cardiac Volume Monitoring in Electrical Impedance Tomography","volume":"4","author":"Nam","year":"2023","journal-title":"Mach. Learn. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10","DOI":"10.4291\/wjgp.v3.i1.10","article-title":"Electrical Bioimpedance and Other Techniques for Gastric Emptying and Motility Evaluation","volume":"3","year":"2012","journal-title":"World J. Gastrointest. Pathophysiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"50","DOI":"10.2478\/joeb-2021-0007","article-title":"Electrical Impedance Tomography\u2014Recent Applications and Developments","volume":"12","author":"Mansouri","year":"2021","journal-title":"J. Electr. Bioimpedance"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pennati, F., Angelucci, A., Morelli, L., Bardini, S., Barzanti, E., Cavallini, F., Conelli, A., Di Federico, G., Paganelli, C., and Aliverti, A. (2023). Electrical Impedance Tomography: From the Traditional Design to the Novel Frontier of Wearables. Sensors, 23.","DOI":"10.3390\/s23031182"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3810","DOI":"10.1109\/TCSI.2018.2858148","article-title":"A High Frame Rate Wearable EIT System Using Active Electrode ASICs for Lung Respiration and Heart Rate Monitoring","volume":"65","author":"Wu","year":"2018","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/TMI.2004.827482","article-title":"Reconstructions of Chest Phantoms by the D-Bar Method for Electrical Impedance Tomography","volume":"23","author":"Isaacson","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1137\/060656930","article-title":"D-Bar Method for Electrical Impedance Tomography with Discontinuous Conductivities","volume":"67","author":"Knudsen","year":"2007","journal-title":"SIAM J. Appl. Math."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1109\/42.700740","article-title":"Tikhonov Regularization and Prior Information in Electrical Impedance Tomography","volume":"17","author":"Vauhkonen","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5151","DOI":"10.1088\/0031-9155\/53\/18\/020","article-title":"Two Hybrid Regularization Frameworks for Solving the Electrocardiography Inverse Problem","volume":"53","author":"Jiang","year":"2008","journal-title":"Phys. Med. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1088\/0967-3334\/29\/11\/007","article-title":"Comparison of Methods for Optimal Choice of the Regularization Parameter for Linear Electrical Impedance Tomography of Brain Function","volume":"29","author":"Abascal","year":"2008","journal-title":"Physiol. Meas."},{"key":"ref_22","unstructured":"Latourette, K. (2008). 2.1. The Steepest Descent 4. Final Remarks Appendix A. Quadratic Convergence Rate of the Levenberg-Marquard, University of Arizona."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1007\/s10915-021-01716-4","article-title":"Learning Nonlinear Electrical Impedance Tomography","volume":"90","author":"Colibazzi","year":"2022","journal-title":"J. Sci. Comput."},{"key":"ref_24","first-page":"265","article-title":"Electrical Capacitance Tomography Two-Phase Oil-Gas Pipe Flow Imaging by the Linear Back-Projection Algorithm","volume":"44","author":"Gamio","year":"2005","journal-title":"Geof\u00edsic. Int."},{"key":"ref_25","unstructured":"Smolik, W.T. (2013). Rekonstrukcja Obraz\u00f3w w Elektrycznej Tomografii Pojemno\u015bciowej, Oficyna Wydawnicza Politechniki Warszawskiej."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5465","DOI":"10.1007\/s00521-022-07988-7","article-title":"Study and Comparison of Different Machine Learning-Based Approaches to Solve the Inverse Problem in Electrical Impedance Tomographies","volume":"35","author":"Aller","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TMI.2018.2833635","article-title":"Image Reconstruction Is a New Frontier of Machine Learning","volume":"37","author":"Wang","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/JSAIT.2020.2991563","article-title":"Deep Learning Techniques for Inverse Problems in Imaging","volume":"1","author":"Ongie","year":"2020","journal-title":"IEEE J. Sel. Areas Inf. Theory"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/42.363109","article-title":"A Neural Network Image Reconstruction Technique for Electrical Impedance Tomography","volume":"13","author":"Adler","year":"1994","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"124007","DOI":"10.1088\/1361-6420\/aa9581","article-title":"Solving Ill-Posed Inverse Problems Using Iterative Deep Neural Networks","volume":"33","author":"Adler","year":"2017","journal-title":"Inverse Probl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"88","DOI":"10.3390\/a12050088","article-title":"Review on Electrical Impedance Tomography: Artificial Intelligence Methods and Its Applications","volume":"12","author":"Khan","year":"2019","journal-title":"Algorithms"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1019531","DOI":"10.3389\/fbioe.2022.1019531","article-title":"Advances of Deep Learning in Electrical Impedance Tomography Image Reconstruction","volume":"10","author":"Zhang","year":"2022","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhao, C., Chen, B., Zhang, M., and Li, Y. (2019, January 9\u201310). Big Data Driven U-Net Based Electrical Capacitance Image Reconstruction Algorithm. Proceedings of the 2019 IEEE International Conference on Imaging Systems and Techniques (IST), Abu Dhabi, United Arab.","DOI":"10.1109\/IST48021.2019.9010423"},{"key":"ref_34","first-page":"1119","article-title":"Solving Inverse Problems With Deep Neural Networks\u2014Robustness Included","volume":"8828","author":"Genzel","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"K\u0142osowski, G., Rymarczyk, T., Cieplak, T., Niderla, K., and Skowron, \u0141. (2020). Quality Assessment of the Neural Algorithms on the Example of EIT-UST Hybrid Tomography. Sensors, 20.","DOI":"10.3390\/s20113324"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rymarczyk, T., Klosowski, G., Kozlowski, E., and Tch\u00f3rzewski, P. (2019). Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography. Sensors, 19.","DOI":"10.3390\/s19071521"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/JSEN.2005.860316","article-title":"Nonlinear Forward Problem Solution for Electrical Capacitance Tomography Using Feed-Forward Neural Network","volume":"6","author":"Marashdeh","year":"2006","journal-title":"IEEE Sens. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1088\/0957-0233\/17\/8\/007","article-title":"A Nonlinear Image Reconstruction Technique for ECT Using a Combined Neural Network Approach","volume":"17","author":"Marashdeh","year":"2006","journal-title":"Meas. Sci. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"012013","DOI":"10.1088\/1742-6596\/1757\/1\/012013","article-title":"EIT-4LDNN: A Novel Neural Network for Electrical Impedance Tomography","volume":"1757","author":"Zhang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8760","DOI":"10.1109\/JSEN.2022.3161025","article-title":"A Regularization-Guided Deep Imaging Method for Electrical Impedance Tomography","volume":"22","author":"Fu","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"085007","DOI":"10.1088\/1361-6420\/ac7743","article-title":"Machine Learning Enhanced Electrical Impedance Tomography for 2D Materials","volume":"38","author":"Coxson","year":"2022","journal-title":"Inverse Probl."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Deabes, W., Abdel-Hakim, A.E., Bouazza, K.E., and Althobaiti, H. (2022). Adversarial Resolution Enhancement for Electrical Capacitance Tomography Image Reconstruction. Sensors, 22.","DOI":"10.3390\/s22093142"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4466","DOI":"10.1109\/JSEN.2022.3197663","article-title":"Image Reconstruction for Electrical Impedance Tomography (EIT) With Improved Wasserstein Generative Adversarial Network (WGAN)","volume":"23","author":"Zhang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1093\/gji\/ggab024","article-title":"Convolutional Neural Networks with SegNet Architecture Applied to Three-Dimensional Tomography of Subsurface Electrical Resistivity: CNN-3D-ERT","volume":"225","author":"Vu","year":"2021","journal-title":"Geophys. J. Int."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ma, G., Jiang, Y., Wang, B., and Soleimani, M. (2021). Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography. Electronics, 10.","DOI":"10.3390\/electronics10091058"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Fuentes, X., Mera, D., G\u00f3mez, A., and Vidal-Franco, I. (2018). Towards a Fast and Accurate EIT Inverse Problem Solver: A Machine Learning Approach. Electronics, 7.","DOI":"10.3390\/electronics7120422"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4505311","DOI":"10.1109\/TIM.2021.3092061","article-title":"RCRC: A Deep Neural Network for Dynamic Image Reconstruction of Electrical Impedance Tomography","volume":"70","author":"Ren","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4506308","DOI":"10.1109\/TIM.2022.3205908","article-title":"R-UNet Deep Learning-Based Damage Detection of CFRP With Electrical Impedance Tomography","volume":"71","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4009309","DOI":"10.1109\/TIM.2023.3298389","article-title":"Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring","volume":"72","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"9627","DOI":"10.1109\/TPAMI.2023.3240565","article-title":"DeepEIT: Deep Image Prior Enabled Electrical Impedance Tomography","volume":"45","author":"Liu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"104421","DOI":"10.1016\/j.bspc.2022.104421","article-title":"SAR-CGAN: Improved Generative Adversarial Network for EIT Reconstruction of Lung Diseases","volume":"81","author":"Li","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3282","DOI":"10.1109\/JBHI.2023.3265385","article-title":"Effective Electrical Impedance Tomography Based on Enhanced Encoder-Decoder Using Atrous Spatial Pyramid Pooling Module","volume":"27","author":"Tian","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TCI.2021.3132190","article-title":"Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems","volume":"7","author":"Herzberg","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1088\/0967-3334\/26\/4\/014","article-title":"Neural Network Based Approach for Anomaly Detection in the Lungs Region by Electrical Impedance Tomography","volume":"26","author":"Minhas","year":"2005","journal-title":"Physiol. Meas."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1016\/j.bja.2018.02.030","article-title":"Characteristic Pattern of Pleural Effusion in Electrical Impedance Tomography Images of Critically Ill Patients","volume":"120","author":"Becher","year":"2018","journal-title":"Br. J. Anaesth."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TMI.2016.2613511","article-title":"Incorporating a Spatial Prior into Nonlinear D-Bar EIT Imaging for Complex Admittivities","volume":"36","author":"Hamilton","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2546","DOI":"10.1109\/TBME.2019.2891676","article-title":"Dominant-Current Deep Learning Scheme for Electrical Impedance Tomography","volume":"66","author":"Wei","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Shin, K., and Mueller, J.L. (2021). Calder\u00f3n\u2019s Method with a Spatial Prior for 2-d Eit Imaging of Ventilation and Perfusion. Sensors, 21.","DOI":"10.3390\/s21165635"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1515\/cdbme-2017-0108","article-title":"Reconstruction of Conductivity Change in Lung Lobes Utilizing Electrical Impedance Tomography","volume":"3","author":"Schullcke","year":"2017","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wanta, D., Makowiecka, O., Smolik, W.T., Kryszyn, J., Doma\u0144ski, G., Midura, M., and Wr\u00f3blewski, P. (2022). Numerical Evaluation of Complex Capacitance Measurement Using Pulse Excitation in Electrical Capacitance Tomography. Electronics, 11.","DOI":"10.3390\/electronics11121864"},{"key":"ref_61","first-page":"146","article-title":"2D Modelling of a Sensor for Electrical Capacitance Tomography in Ectsim Toolbox","volume":"7","author":"Kryszyn","year":"2017","journal-title":"Inform. Control. Meas. Econ. Environ. Prot."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s40010-021-00748-7","article-title":"A Finite Volume Method Using a Quadtree Non-Uniform Structured Mesh for Modeling in Electrical Capacitance Tomography","volume":"92","author":"Wanta","year":"2022","journal-title":"Proc. Natl. Acad. Sci. India Sect. A Phys. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1080\/21681163.2019.1672210","article-title":"Fast and Automatic Segmentation of Pulmonary Lobes from Chest CT Using a Progressive Dense V-Network","volume":"8","author":"Imran","year":"2020","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1088\/0967-3334\/24\/1\/310","article-title":"Dielectric Properties of Blood: An Investigation of Haematocrit Dependence","volume":"24","author":"Jaspard","year":"2003","journal-title":"Physiol. Meas."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/TBME.2004.843297","article-title":"Comparative Analysis of Hematocrit Measurements by Dielectric and Impedance Techniques","volume":"52","author":"Treo","year":"2005","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"25818","DOI":"10.1109\/JSEN.2021.3116164","article-title":"Image Reconstruction in Electrical Capacitance Tomography Based on Deep Neural Networks","volume":"21","author":"Deabes","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/TIM.2014.2329738","article-title":"Wuqiang Yang Image Reconstruction for Electrical Capacitance Tomography Based on Sparse Representation","volume":"64","author":"Ye","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_68","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 22). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, Diego, CA, USA."},{"key":"ref_69","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lile, France."},{"key":"ref_70","unstructured":"Deabes, W., and Abdel-Hakim, A.E. (2022). CGAN-ECT: Tomography Image Reconstruction from Electrical Capacitance Measurements Using CGANs. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_72","unstructured":"DeVries, T., Romero, A., Pineda, L., Taylor, G.W., and Drozdzal, M. (2019). On the Evaluation of Conditional GANs. arXiv."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1130229","DOI":"10.3389\/fonc.2023.1130229","article-title":"Common Statistical Concepts in the Supervised Machine Learning Arena","volume":"13","author":"Rashidi","year":"2023","journal-title":"Front. Oncol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7774\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:47:57Z","timestamp":1760129277000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7774"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,9]]},"references-count":73,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187774"],"URL":"https:\/\/doi.org\/10.3390\/s23187774","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,9]]}}}