{"id":"https://openalex.org/W4410537907","doi":"https://doi.org/10.1109/ssd64182.2025.10989815","title":"A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction","display_name":"A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction","publication_year":2025,"publication_date":"2025-02-17","ids":{"openalex":"https://openalex.org/W4410537907","doi":"https://doi.org/10.1109/ssd64182.2025.10989815"},"language":"en","primary_location":{"id":"doi:10.1109/ssd64182.2025.10989815","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssd64182.2025.10989815","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010854493","display_name":"Mariem Hafsa","orcid":"https://orcid.org/0000-0002-1908-0157"},"institutions":[{"id":"https://openalex.org/I8636806","display_name":"University of Sousse","ror":"https://ror.org/00dmpgj58","country_code":"TN","type":"education","lineage":["https://openalex.org/I8636806"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Mariem Hafsa","raw_affiliation_strings":["Université de Sousse,Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems,Sousse,Tunisie,4023"],"affiliations":[{"raw_affiliation_string":"Université de Sousse,Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems,Sousse,Tunisie,4023","institution_ids":["https://openalex.org/I8636806"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058599511","display_name":"Kwun-Nan Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kailun Lin","raw_affiliation_strings":["Technische Universität Chemnitz,Department of Mechanical Engineering,Chemnitz,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universität Chemnitz,Department of Mechanical Engineering,Chemnitz,Germany","institution_ids":["https://openalex.org/I2610724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050773373","display_name":"Najoua Essoukri Ben Amara","orcid":"https://orcid.org/0000-0001-7914-0644"},"institutions":[{"id":"https://openalex.org/I8636806","display_name":"University of Sousse","ror":"https://ror.org/00dmpgj58","country_code":"TN","type":"education","lineage":["https://openalex.org/I8636806"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Najoua Essoukri Ben Amara","raw_affiliation_strings":["Université de Sousse,Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems,Sousse,Tunisie,4023"],"affiliations":[{"raw_affiliation_string":"Université de Sousse,Ecole Nationale d'Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems,Sousse,Tunisie,4023","institution_ids":["https://openalex.org/I8636806"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070689752","display_name":"Olfa Kanoun","orcid":null},"institutions":[{"id":"https://openalex.org/I2610724","display_name":"Chemnitz University of Technology","ror":"https://ror.org/00a208s56","country_code":"DE","type":"education","lineage":["https://openalex.org/I2610724"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Olfa Kanoun","raw_affiliation_strings":["Technische Universität Chemnitz,Professorship of Measurement and Sensor Technology,Chemnitz,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universität Chemnitz,Professorship of Measurement and Sensor Technology,Chemnitz,Germany","institution_ids":["https://openalex.org/I2610724"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010854493"],"corresponding_institution_ids":["https://openalex.org/I8636806"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11345078,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"243","last_page":"248"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9531000256538391,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9531000256538391,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/electrical-impedance-tomography","display_name":"Electrical impedance tomography","score":0.8594480752944946},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7228515148162842},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.6589689254760742},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6335878372192383},{"id":"https://openalex.org/keywords/tomography","display_name":"Tomography","score":0.5962145924568176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5279433131217957},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47809645533561707},{"id":"https://openalex.org/keywords/electrical-impedance","display_name":"Electrical impedance","score":0.4695877134799957},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38313111662864685},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.15491360425949097},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1273258924484253},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.10499116778373718},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.09587770700454712}],"concepts":[{"id":"https://openalex.org/C155175808","wikidata":"https://www.wikidata.org/wiki/Q1326472","display_name":"Electrical impedance tomography","level":3,"score":0.8594480752944946},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7228515148162842},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.6589689254760742},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6335878372192383},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.5962145924568176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5279433131217957},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47809645533561707},{"id":"https://openalex.org/C17829176","wikidata":"https://www.wikidata.org/wiki/Q179043","display_name":"Electrical impedance","level":2,"score":0.4695877134799957},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38313111662864685},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.15491360425949097},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1273258924484253},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.10499116778373718},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.09587770700454712}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssd64182.2025.10989815","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssd64182.2025.10989815","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2113761410","https://openalex.org/W2952571350","https://openalex.org/W3113076290","https://openalex.org/W3133934120","https://openalex.org/W4212800133","https://openalex.org/W4293226502","https://openalex.org/W4312276830","https://openalex.org/W4391585349","https://openalex.org/W4391925051","https://openalex.org/W4396513873","https://openalex.org/W4400728622","https://openalex.org/W4401808239","https://openalex.org/W4406314519"],"related_works":["https://openalex.org/W3211685995","https://openalex.org/W2532315310","https://openalex.org/W2902419700","https://openalex.org/W3160616384","https://openalex.org/W2905540725","https://openalex.org/W4376629886","https://openalex.org/W2000128178","https://openalex.org/W2101428392","https://openalex.org/W2537891456","https://openalex.org/W2215273690"],"abstract_inverted_index":{"Electrical":[0],"Impedance":[1],"Tomography":[2],"(EIT)":[3],"is":[4,135,158],"a":[5,12,70,81,89,109],"widely":[6],"used":[7],"non-invasive":[8],"imaging":[9],"method":[10],"in":[11,53,124,148],"variety":[13],"of":[14,83,114,122,150],"application":[15],"fields.":[16],"Most":[17],"EIT":[18,37,164],"applications":[19],"require":[20],"an":[21,117],"accurate":[22],"and":[23,51,95,98,116,153,160],"high-quality":[24],"reconstructed":[25],"inner":[26],"conductivity":[27],"distribution":[28],"image.":[29],"Several":[30],"solutions":[31],"have":[32],"been":[33],"developed":[34],"for":[35,92,128,163],"reliable":[36],"image":[38,151],"reconstruction,":[39],"but":[40],"they":[41],"still":[42],"suffer":[43],"from":[44],"limitations":[45],"such":[46],"as":[47],"low":[48],"spatial":[49],"resolution":[50],"inconsistency":[52],"real":[54],"measurement":[55],"scenarios.":[56],"In":[57],"this":[58],"paper,":[59],"we":[60],"propose":[61],"using":[62],"physically":[63],"based,":[64],"extensive":[65],"simulation":[66],"data":[67,85],"to":[68],"train":[69],"Convolutional":[71],"Neural":[72],"Network":[73],"(CNN)":[74],"model":[75],"with":[76],"5-fold":[77],"cross-validation.":[78],"We":[79],"generate":[80],"set":[82],"10.000":[84],"samples,":[86],"which":[87],"builds":[88],"comprehensive":[90],"basis":[91],"its":[93],"training":[94],"ensures":[96],"reliability":[97],"generalization":[99],"capability.":[100],"The":[101,132,156],"quantitative":[102],"evaluation":[103],"demonstrates":[104],"good":[105],"reconstruction":[106,147],"performance,":[107],"achieving":[108],"Mean":[110],"Squared":[111],"Error":[112],"(MSE)":[113],"0.0151":[115],"Image":[118],"Correlation":[119],"Coefficient":[120],"(ICC)":[121],"0.97":[123],"the":[125,129],"best-performing":[126],"fold":[127],"simulated":[130],"data.":[131],"method's":[133],"effectiveness":[134],"particularly":[136],"validated":[137],"through":[138],"experimental":[139],"measurements,":[140],"where":[141],"it":[142],"significantly":[143],"outperforms":[144],"traditional":[145],"Gauss-Newton":[146],"terms":[149],"sharpness":[152],"boundary":[154],"definition.":[155],"algorithm":[157],"robust":[159],"easily":[161],"applicable":[162],"imaging.":[165]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}