{"id":"https://openalex.org/W3171760114","doi":"https://doi.org/10.1109/access.2021.3087424","title":"CT-Scan Denoising Using a Charbonnier Loss Generative Adversarial Network","display_name":"CT-Scan Denoising Using a Charbonnier Loss Generative Adversarial Network","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3171760114","doi":"https://doi.org/10.1109/access.2021.3087424","mag":"3171760114"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3087424","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3087424","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09448108.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/ielx7/6287639/9312710/09448108.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066933751","display_name":"Binit Vasant Gajera","orcid":"https://orcid.org/0000-0003-0127-5199"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Binit Vasant Gajera","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-0127-5199","affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008493607","display_name":"Siddhant Raj Kapil","orcid":"https://orcid.org/0000-0002-0575-5088"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siddhant Raj Kapil","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-0575-5088","affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077903462","display_name":"Dorsa Ziaei","orcid":"https://orcid.org/0000-0003-1854-878X"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dorsa Ziaei","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-1854-878X","affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022345541","display_name":"Jayalakshmi Mangalagiri","orcid":null},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jayalakshmi Mangalagiri","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017221717","display_name":"Eliot L. Siegel","orcid":"https://orcid.org/0000-0002-7458-6281"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]},{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eliot Siegel","raw_affiliation_strings":["University of Maryland School of Medicine, Baltimore, MD, USA","University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland School of Medicine, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011098359","display_name":"David Chapman","orcid":"https://orcid.org/0000-0003-1722-9883"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Chapman","raw_affiliation_strings":["University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-1722-9883","affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.4487,"has_fulltext":true,"cited_by_count":61,"citation_normalized_percentile":{"value":0.90134827,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"84093","last_page":"84109"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9991999864578247,"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"}},{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9988999962806702,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9955999851226807,"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/noise-reduction","display_name":"Noise reduction","score":0.7848981022834778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7104489803314209},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7019160389900208},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.590721070766449},{"id":"https://openalex.org/keywords/imaging-phantom","display_name":"Imaging phantom","score":0.5431255102157593},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5305139422416687},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5273500084877014},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5014448165893555},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.49610385298728943},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.4873571991920471},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4605751037597656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4501974582672119},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32269084453582764},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19630780816078186},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.19125527143478394},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.08232781291007996},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.07521262764930725}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7848981022834778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7104489803314209},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7019160389900208},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.590721070766449},{"id":"https://openalex.org/C104293457","wikidata":"https://www.wikidata.org/wiki/Q28324852","display_name":"Imaging phantom","level":2,"score":0.5431255102157593},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5305139422416687},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5273500084877014},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5014448165893555},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.49610385298728943},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.4873571991920471},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4605751037597656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4501974582672119},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32269084453582764},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19630780816078186},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.19125527143478394},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.08232781291007996},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.07521262764930725},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3087424","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3087424","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09448108.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:b2d7b3aeabef466cbe77631a33f2a79a","is_oa":true,"landing_page_url":"https://doaj.org/article/b2d7b3aeabef466cbe77631a33f2a79a","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 9, Pp 84093-84109 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3087424","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3087424","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09448108.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":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3171760114.pdf","grobid_xml":"https://content.openalex.org/works/W3171760114.grobid-xml"},"referenced_works_count":60,"referenced_works":["https://openalex.org/W562660536","https://openalex.org/W1522301498","https://openalex.org/W1665214252","https://openalex.org/W1686810756","https://openalex.org/W1964231221","https://openalex.org/W1972150100","https://openalex.org/W1980003430","https://openalex.org/W1990919278","https://openalex.org/W1999320095","https://openalex.org/W2064526599","https://openalex.org/W2094366314","https://openalex.org/W2098092211","https://openalex.org/W2099305408","https://openalex.org/W2099471712","https://openalex.org/W2101061092","https://openalex.org/W2123169298","https://openalex.org/W2126926806","https://openalex.org/W2133665775","https://openalex.org/W2171641626","https://openalex.org/W2194775991","https://openalex.org/W2221873563","https://openalex.org/W2281620867","https://openalex.org/W2331128040","https://openalex.org/W2484874824","https://openalex.org/W2542870090","https://openalex.org/W2556016755","https://openalex.org/W2574952845","https://openalex.org/W2584483805","https://openalex.org/W2617128058","https://openalex.org/W2739748921","https://openalex.org/W2743780012","https://openalex.org/W2748739903","https://openalex.org/W2787564202","https://openalex.org/W2793419304","https://openalex.org/W2798401174","https://openalex.org/W2808109260","https://openalex.org/W2887746098","https://openalex.org/W2962879692","https://openalex.org/W2964121744","https://openalex.org/W2972788736","https://openalex.org/W3101406715","https://openalex.org/W3101569186","https://openalex.org/W3103261259","https://openalex.org/W3104324122","https://openalex.org/W4233762729","https://openalex.org/W4295521014","https://openalex.org/W4320013936","https://openalex.org/W6615672858","https://openalex.org/W6631190155","https://openalex.org/W6637242042","https://openalex.org/W6637373629","https://openalex.org/W6687483927","https://openalex.org/W6688965711","https://openalex.org/W6702130928","https://openalex.org/W6722193357","https://openalex.org/W6729710357","https://openalex.org/W6735913928","https://openalex.org/W6741832134","https://openalex.org/W6756762481","https://openalex.org/W6922652424"],"related_works":["https://openalex.org/W2417440389","https://openalex.org/W4244157427","https://openalex.org/W2015071354","https://openalex.org/W2062195871","https://openalex.org/W2073031339","https://openalex.org/W4391915433","https://openalex.org/W2144778520","https://openalex.org/W2061271245","https://openalex.org/W2365285804","https://openalex.org/W2620851791"],"abstract_inverted_index":{"We":[0,112],"propose":[1],"a":[2,26,47,54,70,95,99,107],"Generative":[3],"Adversarial":[4],"Network":[5],"(GAN)":[6],"optimized":[7],"for":[8,82],"noise":[9,120,182,223],"reduction":[10],"in":[11,35,49,122],"CT-scans.":[12],"The":[13],"objective":[14],"of":[15,43,53,60,69,85,94,156,165,171,194,205],"CT":[16,86,127],"scan":[17,87],"denoising":[18,175,207],"is":[19],"to":[20,30,124,147,224,227],"obtain":[21],"higher":[22],"quality":[23],"imagery":[24],"using":[25,116,132],"lower":[27],"radiation":[28],"exposure":[29,139],"the":[31,41,50,58,67,83,157,163,172,192,195,199,203,206,211,217,228],"patient.":[32],"Recent":[33],"work":[34],"computer":[36],"vision":[37],"has":[38,75],"shown":[39],"that":[40,191,216],"use":[42,68,93],"Charbonnier":[44,71,108,196],"distance":[45,109],"as":[46,104,106,129,131,150,152,208],"term":[48,74],"perceptual":[51,102],"loss":[52,73],"GAN":[55,91],"can":[56],"improve":[57],"performance":[59,204],"image":[61],"reconstruction":[62],"and":[63,160,213,215],"video":[64],"super-resolution.":[65],"However,":[66],"structural":[72,110],"not":[76],"yet":[77],"been":[78],"applied":[79,118],"or":[80],"evaluated":[81],"purpose":[84],"denoising.":[88],"Our":[89,141,188],"proposed":[90],"makes":[92],"Wasserstein":[96],"adversarial":[97],"loss,":[98,103],"pretrained":[100],"VGG19":[101],"well":[105,130,151],"loss.":[111],"evaluate":[113],"our":[114,166],"approach":[115],"both":[117],"Poisson":[119],"distribution":[121],"order":[123],"simulate":[125],"low-dose":[126],"imagery,":[128],"an":[133],"anthropomorphic":[134],"thoracic":[135],"phantom":[136],"at":[137],"different":[138],"levels.":[140],"evaluation":[142],"criteria":[143],"are":[144],"Peek":[145],"Signal":[146],"Noise":[148],"(PSNR)":[149],"Structured":[153],"Similarity":[154],"(SSIM)":[155],"denoised":[158],"images,":[159],"we":[161,179],"compare":[162],"results":[164],"method":[167,218],"versus":[168],"recent":[169],"state":[170],"art":[173],"deep":[174],"GANs.":[176],"In":[177],"addition,":[178],"report":[180],"global":[181],"through":[183],"uniform":[184],"soft":[185,221],"tissue":[186,222],"mediums.":[187],"findings":[189],"show":[190],"incorporation":[193],"Loss":[197],"with":[198,210],"VGG-19":[200],"network":[201],"improves":[202],"measured":[209],"PSNR":[212],"SSIM,":[214],"greatly":[219],"reduces":[220],"levels":[225],"comparable":[226],"NDCT":[229],"scan.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4}],"updated_date":"2026-08-15T07:11:24.734988","created_date":"2025-10-10T00:00:00"}
