{"id":"https://openalex.org/W4214624592","doi":"https://doi.org/10.1109/access.2022.3155203","title":"Unpaired-Paired Learning for Shading Correction in Cone-Beam Computed Tomography","display_name":"Unpaired-Paired Learning for Shading Correction in Cone-Beam Computed Tomography","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4214624592","doi":"https://doi.org/10.1109/access.2022.3155203"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3155203","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3155203","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09722839.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09722839.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021735255","display_name":"Hyoung Suk Park","orcid":"https://orcid.org/0000-0003-0032-4630"},"institutions":[{"id":"https://openalex.org/I4210158432","display_name":"National Institute for Mathematical Sciences","ror":"https://ror.org/04n7py080","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210158432"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyoung Suk Park","raw_affiliation_strings":["National Institute for Mathematical Sciences, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-0032-4630","affiliations":[{"raw_affiliation_string":"National Institute for Mathematical Sciences, Daejeon, South Korea","institution_ids":["https://openalex.org/I4210158432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089915992","display_name":"Kiwan Jeon","orcid":"https://orcid.org/0000-0002-2460-7478"},"institutions":[{"id":"https://openalex.org/I4210158432","display_name":"National Institute for Mathematical Sciences","ror":"https://ror.org/04n7py080","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210158432"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kiwan Jeon","raw_affiliation_strings":["National Institute for Mathematical Sciences, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-2460-7478","affiliations":[{"raw_affiliation_string":"National Institute for Mathematical Sciences, Daejeon, South Korea","institution_ids":["https://openalex.org/I4210158432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056832997","display_name":"Sang-Hwy Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sang-Hwy Lee","raw_affiliation_strings":["Oral Science Research Center, Yonsei University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oral Science Research Center, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034037522","display_name":"Jin Keun Seo","orcid":"https://orcid.org/0000-0002-6275-4938"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jin Keun Seo","raw_affiliation_strings":["School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-6275-4938","affiliations":[{"raw_affiliation_string":"School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5021735255"],"corresponding_institution_ids":["https://openalex.org/I4210158432"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.178,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.76462785,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"10","issue":null,"first_page":"26140","last_page":"26148"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/shading","display_name":"Shading","score":0.7596945762634277},{"id":"https://openalex.org/keywords/cone-beam-computed-tomography","display_name":"Cone beam computed tomography","score":0.6654538512229919},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.658888578414917},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49767830967903137},{"id":"https://openalex.org/keywords/beam","display_name":"Beam (structure)","score":0.47106072306632996},{"id":"https://openalex.org/keywords/tomography","display_name":"Tomography","score":0.4408777952194214},{"id":"https://openalex.org/keywords/cone","display_name":"Cone (formal languages)","score":0.4286673069000244},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.4070318341255188},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38319647312164307},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3514232337474823},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.2656954824924469},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.17796969413757324},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.15303432941436768},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.14196068048477173},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.0790475606918335}],"concepts":[{"id":"https://openalex.org/C177515723","wikidata":"https://www.wikidata.org/wiki/Q1191981","display_name":"Shading","level":2,"score":0.7596945762634277},{"id":"https://openalex.org/C2779813781","wikidata":"https://www.wikidata.org/wiki/Q1224951","display_name":"Cone beam computed tomography","level":3,"score":0.6654538512229919},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.658888578414917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49767830967903137},{"id":"https://openalex.org/C168834538","wikidata":"https://www.wikidata.org/wiki/Q3705329","display_name":"Beam (structure)","level":2,"score":0.47106072306632996},{"id":"https://openalex.org/C163716698","wikidata":"https://www.wikidata.org/wiki/Q841267","display_name":"Tomography","level":2,"score":0.4408777952194214},{"id":"https://openalex.org/C30014739","wikidata":"https://www.wikidata.org/wiki/Q5159445","display_name":"Cone (formal languages)","level":2,"score":0.4286673069000244},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.4070318341255188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38319647312164307},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3514232337474823},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2656954824924469},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.17796969413757324},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15303432941436768},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.14196068048477173},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0790475606918335}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2022.3155203","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3155203","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09722839.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:1401c0406f734e39af1c7cb28f25a00e","is_oa":true,"landing_page_url":"https://doaj.org/article/1401c0406f734e39af1c7cb28f25a00e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 26140-26148 (2022)","raw_type":"article"},{"id":"pmh:oai:ir.ymlib.yonsei.ac.kr:22282913/188569","is_oa":true,"landing_page_url":"https://ir.ymlib.yonsei.ac.kr/handle/22282913/188569","pdf_url":null,"source":{"id":"https://openalex.org/S4306400691","display_name":"YUHSpace (Yonsei University Medical Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193775966","host_organization_name":"Yonsei University","host_organization_lineage":["https://openalex.org/I193775966"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3155203","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3155203","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09722839.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2698359513","display_name":null,"funder_award_id":"HI20C0127","funder_id":"https://openalex.org/F4320322107","funder_display_name":"Korea Health Industry Development Institute"},{"id":"https://openalex.org/G3969678654","display_name":null,"funder_award_id":"NIMS-B22910000","funder_id":"https://openalex.org/F4320314264","funder_display_name":"Institute of Mathematical Sciences"},{"id":"https://openalex.org/G8089768951","display_name":null,"funder_award_id":"SRFC-IT1902-09","funder_id":"https://openalex.org/F4320328437","funder_display_name":"Samsung Science and Technology Foundation"}],"funders":[{"id":"https://openalex.org/F4320314264","display_name":"Institute of Mathematical Sciences","ror":"https://ror.org/05078rg59"},{"id":"https://openalex.org/F4320322107","display_name":"Korea Health Industry Development Institute","ror":"https://ror.org/00fdzyk40"},{"id":"https://openalex.org/F4320328437","display_name":"Samsung Science and Technology Foundation","ror":null},{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"},{"id":"https://openalex.org/F4320334904","display_name":"National Institute for Materials Science","ror":"https://ror.org/026v1ze26"},{"id":"https://openalex.org/F4320337380","display_name":"Division of Mathematical Sciences","ror":"https://ror.org/051fftw81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4214624592.pdf","grobid_xml":"https://content.openalex.org/works/W4214624592.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W75831814","https://openalex.org/W1511118377","https://openalex.org/W1522301498","https://openalex.org/W1882111414","https://openalex.org/W1967277558","https://openalex.org/W1971253730","https://openalex.org/W1977836277","https://openalex.org/W1980885354","https://openalex.org/W1998112037","https://openalex.org/W1998361352","https://openalex.org/W2002839292","https://openalex.org/W2039574235","https://openalex.org/W2055302232","https://openalex.org/W2060434533","https://openalex.org/W2065873904","https://openalex.org/W2072021306","https://openalex.org/W2086988348","https://openalex.org/W2121293785","https://openalex.org/W2128218423","https://openalex.org/W2130022421","https://openalex.org/W2140972007","https://openalex.org/W2146646931","https://openalex.org/W2165565866","https://openalex.org/W2271840356","https://openalex.org/W2593414223","https://openalex.org/W2617128058","https://openalex.org/W2777802649","https://openalex.org/W2887746098","https://openalex.org/W2962765321","https://openalex.org/W2962793481","https://openalex.org/W2968087827","https://openalex.org/W3040980702","https://openalex.org/W3081551184","https://openalex.org/W3101406715","https://openalex.org/W3121038207","https://openalex.org/W3185953395","https://openalex.org/W3197867302","https://openalex.org/W4320013936","https://openalex.org/W6603134578","https://openalex.org/W6631190155","https://openalex.org/W6694517276","https://openalex.org/W6734564793"],"related_works":["https://openalex.org/W2994336186","https://openalex.org/W2390827063","https://openalex.org/W2587512547","https://openalex.org/W2372305560","https://openalex.org/W2393851740","https://openalex.org/W2103040079","https://openalex.org/W2319567267","https://openalex.org/W4293073787","https://openalex.org/W2768843469","https://openalex.org/W2614950993"],"abstract_inverted_index":{"Cone-beam":[0],"computed":[1,68],"tomography":[2,69],"(CBCT)":[3],"is":[4,98,119],"widely":[5],"used":[6],"in":[7,114,168,186],"dental":[8],"and":[9,26,45,66,88,198,234],"maxillofacial":[10],"imaging":[11,128],"applications.":[12],"However,":[13],"CBCT":[14,65,87,214,236],"suffers":[15],"from":[16,139,164,202],"shading":[17,38],"artifacts":[18,39,108,200],"owing":[19],"to":[20,134,231],"several":[21],"factors,":[22],"including":[23],"photon":[24],"scattering":[25],"data":[27,204],"truncation.":[28],"This":[29,118],"paper":[30],"presents":[31],"a":[32,52,76],"deep-learning-based":[33],"method":[34,50,225],"for":[35,101],"eliminating":[36],"the":[37,43,81,85,93,115,121,126,136,147,165,169,173,179,191,196,208,212,218,223,232,240],"that":[40,109,190],"interfere":[41],"with":[42,80],"diagnostic":[44],"treatment":[46],"processes.":[47],"The":[48,72,143,183],"proposed":[49,148,192,224],"involves":[51],"two-stage":[53],"generative":[54],"adversarial":[55],"network":[56],"(GAN)-based":[57],"image-to-image":[58],"translation,":[59],"where":[60],"it":[61,105,132],"operates":[62],"on":[63],"unpaired":[64,241],"multidetector":[67],"(MDCT)":[70],"images.":[71,117],"first":[73,170],"stage":[74,145],"uses":[75],"generic":[77],"GAN":[78,175],"along":[79],"fidelity":[82,124,205],"difference":[83],"between":[84,125],"original":[86,213,233],"MDCT-like":[89],"images":[90,237],"generated":[91],"by":[92],"network.":[94],"Although":[95],"this":[96,151,154,187],"approach":[97,193],"generally":[99],"effective":[100],"denoising,":[102],"at":[103],"times,":[104],"introduces":[106],"additional":[107],"appear":[110],"as":[111],"bone-like":[112],"structures":[113,138,210],"output":[116],"because":[120],"weak":[122],"input":[123],"two":[127],"modalities":[129],"can":[130],"make":[131],"difficult":[133],"preserve":[135],"morphological":[137,209],"complex":[140],"shadowing":[141,197],"artifacts.":[142],"second":[144],"of":[146,211],"model":[149],"addresses":[150],"problem.":[152],"In":[153,216],"stage,":[155],"paired":[156,181],"training":[157],"data,":[158,161],"excluding":[159],"inappropriate":[160],"were":[162],"collected":[163],"results":[166,184],"obtained":[167,185,221,238],"stage.":[171],"Subsequently,":[172],"fidelity-embedded":[174],"was":[176],"retrained":[177],"using":[178,222,239],"selected":[180],"samples.":[182],"study":[188],"reveal":[189],"substantially":[194],"reduces":[195],"secondary":[199],"arising":[201],"incorrect":[203],"while":[206],"preserving":[207],"image.":[215],"addition,":[217],"corrected":[219,235],"image":[220],"facilitates":[226],"accurate":[227],"bone":[228],"segmentation":[229],"compared":[230],"method.":[242]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2022-03-02T00:00:00"}
