{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T11:03:48Z","timestamp":1768647828062,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T00:00:00Z","timestamp":1540944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571253"],"award-info":[{"award-number":["61571253"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.<\/jats:p>","DOI":"10.3390\/s18113701","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T11:55:41Z","timestamp":1540986941000},"page":"3701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5833-5610","authenticated-orcid":false,"given":"Jin","family":"Zheng","sequence":"first","affiliation":[{"name":"Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China"}]},{"given":"Jinku","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China"}]},{"given":"Lihui","family":"Peng","sequence":"additional","affiliation":[{"name":"Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1088\/0022-3735\/22\/3\/009","article-title":"Tomographic imaging of two-component flow using capacitance sensors","volume":"22","author":"Huang","year":"1989","journal-title":"J. Phys. E Sci. Instrum."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1063\/1.1142343","article-title":"A capacitive system for 3-dimensional imaging of fluidized-beds","volume":"62","author":"Fasching","year":"1991","journal-title":"Rev. Sci. Instrum."},{"key":"ref_3","first-page":"89","article-title":"Electrical capacitance tomography for flow imaging system model for development of image reconstruction algorithms and design of primary sensors","volume":"139","author":"Xie","year":"1992","journal-title":"IEEE Proc. G"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1109\/JSEN.2006.881409","article-title":"Electrical capacitance tomography\u2014Sensor models, design, simulations, and experimental verification","volume":"6","author":"Alme","year":"2006","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1049\/iet-smt:20060108","article-title":"Simulation design of electrical capacitance tomography sensors","volume":"1","author":"AOlmos","year":"2007","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"042001","DOI":"10.1088\/0957-0233\/21\/4\/042001","article-title":"Design of electrical capacitance tomography sensors","volume":"21","author":"Yang","year":"2010","journal-title":"Meas. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.flowmeasinst.2005.02.015","article-title":"Determination of the optimal axial length of the electrode in an electrical capacitance tomography sensor","volume":"16","author":"Peng","year":"2005","journal-title":"Flow Meas. Instrum."},{"key":"ref_8","first-page":"1554","article-title":"Evaluation of effect of number of electrodes in ECT sensors on image quality","volume":"12","author":"Peng","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1088\/0957-0233\/7\/3\/003","article-title":"Hardware design of electrical capacitance tomography systems","volume":"7","author":"Yang","year":"1996","journal-title":"Meas. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1049\/ip-smt:19990008","article-title":"A new AC-based capacitance tomography system","volume":"146","author":"Yang","year":"1999","journal-title":"IEE Proc. Sci. Meas. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0957-0233\/22\/5\/055503","article-title":"A high-performance digital system for electrical capacitance tomography","volume":"22","author":"Cui","year":"2011","journal-title":"Meas. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.flowmeasinst.2016.05.005","article-title":"A novel multi-electrode sensing strategy for electrical capacitance tomography with ultra-low dynamic range","volume":"53","author":"Yang","year":"2017","journal-title":"Flow Meas. Instrum."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1088\/0957-0233\/7\/3\/013","article-title":"A review of reconstruction techniques for capacitance tomography","volume":"7","author":"Isaksen","year":"1996","journal-title":"Meas. Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1002\/1521-4117(200010)17:3<96::AID-PPSC96>3.0.CO;2-8","article-title":"Using regularization methods for image reconstruction of electrical capacitance tomography","volume":"17","author":"Peng","year":"2000","journal-title":"Part. Part. Syst. Charact."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0957-0233\/14\/1\/201","article-title":"Image reconstruction algorithms for electrical capacitance tomography","volume":"14","author":"Yang","year":"2003","journal-title":"Meas. Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2124","DOI":"10.1088\/0957-0233\/15\/10\/023","article-title":"A nonlinear image reconstruction algorithm for electrical capacitance tomography","volume":"15","author":"Fang","year":"2004","journal-title":"Meas. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1088\/0957-0233\/15\/7\/022","article-title":"Reconstruction of permittivity images from capacitance tomography data by using very fast simulated annealing","volume":"15","author":"Martin","year":"2004","journal-title":"Meas. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1088\/0957-0233\/16\/10\/014","article-title":"Nonlinear image reconstruction for electrical capacitance tomography using experimental data","volume":"16","author":"Soleimani","year":"2005","journal-title":"Meas. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3287","DOI":"10.1088\/0957-0233\/18\/11\/004","article-title":"Dynamic imaging in electrical capacitance tomography and electromagnetic induction tomography using a Kalman filter","volume":"18","author":"Soleimani","year":"2007","journal-title":"Meas. Sci. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"094014","DOI":"10.1088\/0957-0233\/19\/9\/094014","article-title":"Image reconstruction by nonlinear Landweber iteration for complicated distributions","volume":"19","author":"Li","year":"2008","journal-title":"Meas. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"052002","DOI":"10.1088\/0957-0233\/20\/5\/052002","article-title":"A review of statistical modelling and inference for electrical capacitance tomography","volume":"20","author":"Watzenig","year":"2009","journal-title":"Meas. Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1088\/0957-0233\/7\/3\/002","article-title":"Process tomography: A European innovation and its applications","volume":"7","author":"Beck","year":"1996","journal-title":"Meas. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1016\/S0009-2509(97)00037-7","article-title":"Application of capacitance tomography to gas-solid flows","volume":"52","author":"Dyakowski","year":"1997","journal-title":"Chem. Eng. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1016\/S0009-2509(97)00038-9","article-title":"Investigation of the two-phase flow in trickle-bed reactors using capacitance tomography","volume":"52","author":"Reinecke","year":"1997","journal-title":"Chem. Eng. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/TIM.2003.809087","article-title":"Application of electrical capacitance tomography to the void fraction measurement of two-phase flow","volume":"52","author":"Huang","year":"2003","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3201","DOI":"10.1016\/j.ces.2004.04.019","article-title":"On the electrostatics of pneumatic conveying of granular materials using electrical capacitance tomography","volume":"59","author":"Zhu","year":"2004","journal-title":"Chem. Eng. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.flowmeasinst.2005.02.017","article-title":"Tomography for multi-phase flow measurement in the oil industry","volume":"16","author":"Ismail","year":"2005","journal-title":"Flow Meas. Instrum."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7022","DOI":"10.1016\/j.ces.2005.06.029","article-title":"Application of electrical capacitance tomography to the fluidized bed drying of pharmaceutical granule","volume":"60","author":"Chaplin","year":"2005","journal-title":"Chem. Eng. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1016\/j.ces.2009.04.008","article-title":"New insights into transient behaviors of local liquid-holdup in periodically operated trickle-bed reactors using electrical capacitance tomography (ECT)","volume":"64","author":"Liu","year":"2009","journal-title":"Chem. Eng. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"015301","DOI":"10.1088\/0957-0233\/23\/1\/015301","article-title":"Investigation of droplet distribution in electrohydrodynamic atomization (EHDA) using an ac-based electrical capacitance tomography (ECT) system with an internal\u2013external electrode sensor","volume":"23","author":"Rezvanpour","year":"2012","journal-title":"Meas. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/TIM.2014.2329738","article-title":"Image reconstruction for electrical capacitance tomography based on sparse representation","volume":"64","author":"Ye","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"085401","DOI":"10.1088\/0957-0233\/25\/8\/085401","article-title":"A fast sparse reconstruction algorithm for electrical capacitance tomography","volume":"25","author":"Zhao","year":"2014","journal-title":"Meas. Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, Y.J., and Peng, L.H. (2013, January 22\u201323). An image reconstruction algorithm for ECT using enhanced model and sparsity regularization. Proceedings of the 2013 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China.","DOI":"10.1109\/IST.2013.6729658"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"165062","DOI":"10.1088\/0957-0233\/25\/10\/105602","article-title":"Level-set shape reconstruction of binary permittivity distributions using near-field focusing capacitance measurements","volume":"25","author":"Taylor","year":"2014","journal-title":"Meas. Sci. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"025403","DOI":"10.1088\/0957-0233\/25\/2\/025403","article-title":"Reconstruction of the three-dimensional inclusion shapes using electrical capacitance tomography","volume":"25","author":"Ren","year":"2014","journal-title":"Meas. Sci. Technol."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.flowmeasinst.2012.05.006","article-title":"Image reconstruction for an Electrical Capacitance Tomography (ECT) system based on a least squares support vector machine and bacterial colony chemotaxis algorithm","volume":"27","author":"Wang","year":"2012","journal-title":"Flow Meas. Instrum."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, J., Yang, X., Wang, Y., and Pan, R. (2012, January 19\u201321). An image reconstruction algorithm based on RBF neural network for electrical capacitance tomography. Proceedings of the 2012 Sixth International Conference on Electromagnetic Field Problems and Applications, Dalian, China.","DOI":"10.1109\/ICEF.2012.6310416"},{"key":"ref_39","unstructured":"LeCun, Y. (2018, September 02). The MNIST Database of Handwritten Digits. Available online: http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fe, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5464","DOI":"10.1109\/JSEN.2018.2836337","article-title":"An Autoencoder Based Image Reconstruction for Electrical Capacitance Tomography","volume":"18","author":"Zheng","year":"2018","journal-title":"Sensors"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zheng, J., and Peng, L. (2017, January 18\u201320). A Platform for Electrical Capacitance Tomography Large-scale Benchmark Dataset Generating and Image Reconstruction. Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China.","DOI":"10.1109\/IST.2017.8261465"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8","DOI":"10.2478\/msr-2014-0002","article-title":"Liquid film thickness estimation using electrical capacitance tomography","volume":"14","author":"Cui","year":"2014","journal-title":"Meas. Sci. Rev."},{"key":"ref_44","unstructured":"Yang, Y.J., and Peng, L.H. (2013, January 2\u20135). An image reconstruction algorithm for high-contrast dielectrics in ECT. Proceedings of the 7th World Congress on Industrial Process Tomography (WCIPT), Krakow, Poland."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1088\/0957-0233\/10\/11\/315","article-title":"An image-reconstruction algorithm based on Landweber\u2019s iteration method for electrical-capacitance tomography","volume":"10","author":"Yang","year":"1999","journal-title":"Meas. Sci. Technol."},{"key":"ref_46","unstructured":"Gamio, J.C., and Ortiz-Aleman, C. (2003, January 2\u20135). An interpretation of the linear back-projection algorithm used in capacitance tomography. Proceedings of the 3rd World Congress on Industrial Process Tomography, Banff, AB, Canada."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.flowmeasinst.2007.07.004","article-title":"An image reconstruction algorithm based on total variation with adaptive mesh refinement for ECT","volume":"18","author":"Wang","year":"2007","journal-title":"Flow Meas. Instrum."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3701\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:27:07Z","timestamp":1760196427000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3701"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,31]]},"references-count":47,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113701"],"URL":"https:\/\/doi.org\/10.3390\/s18113701","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,31]]}}}