{"id":"https://openalex.org/W4388820124","doi":"https://doi.org/10.1109/tci.2023.3334488","title":"Improving Spectral CT Image Quality Based on Channel Correlation and Self-Supervised Learning","display_name":"Improving Spectral CT Image Quality Based on Channel Correlation and Self-Supervised Learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4388820124","doi":"https://doi.org/10.1109/tci.2023.3334488"},"language":"en","primary_location":{"id":"doi:10.1109/tci.2023.3334488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2023.3334488","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2334-0118","2573-0436"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Imaging","raw_type":"journal-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/A5100330017","display_name":"Xi Chen","orcid":"https://orcid.org/0000-0003-0944-8437"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Chen","raw_affiliation_strings":["School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0003-0944-8437","affiliations":[{"raw_affiliation_string":"School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100603449","display_name":"Chaoyang Zhang","orcid":"https://orcid.org/0009-0007-5341-2657"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoyang Zhang","raw_affiliation_strings":["School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0007-5341-2657","affiliations":[{"raw_affiliation_string":"School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060593385","display_name":"Ti Bai","orcid":"https://orcid.org/0000-0002-6697-7434"},"institutions":[{"id":"https://openalex.org/I867280407","display_name":"The University of Texas Southwestern Medical Center","ror":"https://ror.org/05byvp690","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I867280407"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ti Bai","raw_affiliation_strings":["Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-6697-7434","affiliations":[{"raw_affiliation_string":"Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA","institution_ids":["https://openalex.org/I867280407"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033662215","display_name":"Shaojie Chang","orcid":"https://orcid.org/0000-0003-0550-9650"},"institutions":[{"id":"https://openalex.org/I4210125099","display_name":"Mayo Clinic in Arizona","ror":"https://ror.org/03jp40720","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210125099"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaojie Chang","raw_affiliation_strings":["Department of Radiology, Mayo Clinic, Rochester, MN, USA"],"raw_orcid":"https://orcid.org/0000-0003-0550-9650","affiliations":[{"raw_affiliation_string":"Department of Radiology, Mayo Clinic, Rochester, MN, USA","institution_ids":["https://openalex.org/I4210125099"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3888,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55940794,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"9","issue":null,"first_page":"1084","last_page":"1097"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":1.0,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9993000030517578,"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/T10844","display_name":"Radiation Dose and Imaging","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6862967610359192},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6652196645736694},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6555317640304565},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.6192055344581604},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.6097967028617859},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5914208889007568},{"id":"https://openalex.org/keywords/photon-counting","display_name":"Photon counting","score":0.5178744792938232},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4817890524864197},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.46641385555267334},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4566461443901062},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4278523623943329},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4266241490840912},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42574137449264526},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.42226871848106384},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15339428186416626},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07797658443450928},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07141900062561035}],"concepts":[{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6862967610359192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6652196645736694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6555317640304565},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.6192055344581604},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.6097967028617859},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5914208889007568},{"id":"https://openalex.org/C2781402376","wikidata":"https://www.wikidata.org/wiki/Q17126172","display_name":"Photon counting","level":3,"score":0.5178744792938232},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4817890524864197},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.46641385555267334},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4566461443901062},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4278523623943329},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4266241490840912},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42574137449264526},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.42226871848106384},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15339428186416626},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07797658443450928},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07141900062561035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tci.2023.3334488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2023.3334488","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2334-0118","2573-0436"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computational Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4354898466","display_name":null,"funder_award_id":"11975250","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1972037630","https://openalex.org/W1972150100","https://openalex.org/W2002504347","https://openalex.org/W2002611249","https://openalex.org/W2026154529","https://openalex.org/W2037521346","https://openalex.org/W2042195023","https://openalex.org/W2094366314","https://openalex.org/W2101768706","https://openalex.org/W2110652437","https://openalex.org/W2133665775","https://openalex.org/W2142884793","https://openalex.org/W2282734976","https://openalex.org/W2469946482","https://openalex.org/W2527841944","https://openalex.org/W2542870090","https://openalex.org/W2584483805","https://openalex.org/W2601814420","https://openalex.org/W2743780012","https://openalex.org/W2948751498","https://openalex.org/W2953419750","https://openalex.org/W2980297198","https://openalex.org/W3013621693","https://openalex.org/W3095556331","https://openalex.org/W3110830082","https://openalex.org/W3120261529","https://openalex.org/W3126582326","https://openalex.org/W3167231702","https://openalex.org/W3185305479","https://openalex.org/W3212800953","https://openalex.org/W4297619556","https://openalex.org/W4309365917","https://openalex.org/W4312773500","https://openalex.org/W4312862840","https://openalex.org/W4319866349","https://openalex.org/W4362693441","https://openalex.org/W4365136356","https://openalex.org/W6631190155","https://openalex.org/W6749271710","https://openalex.org/W6922398385"],"related_works":["https://openalex.org/W2757389719","https://openalex.org/W2951714568","https://openalex.org/W1963814553","https://openalex.org/W2037595954","https://openalex.org/W1988158806","https://openalex.org/W2507293823","https://openalex.org/W2386146599","https://openalex.org/W2018015402","https://openalex.org/W2144778520","https://openalex.org/W2801815023"],"abstract_inverted_index":{"Photon":[0],"counting":[1],"spectral":[2,151],"computed":[3],"tomography":[4],"(PCCT)":[5],"can":[6,147,170],"produce":[7],"reconstructed":[8,39,91],"attenuation":[9],"maps":[10],"in":[11,63,79,191],"different":[12,54],"energy":[13,17,31,123],"channels,":[14],"reflecting":[15],"the":[16,20,25,34,38,46,49,68,85,108,112,144,150,166,184],"properties":[18],"of":[19,29,48,67,88],"scanned":[21],"object.":[22],"Due":[23],"to":[24,142,186],"limited":[26],"photon":[27],"numbers":[28],"each":[30,89],"channel":[32,124,132],"and":[33,114,134,160,173,182],"nonideal":[35],"detector":[36],"response,":[37],"images":[40,133],"usually":[41],"contain":[42],"considerable":[43],"noise.":[44],"With":[45],"development":[47],"deep":[50],"learning":[51,97],"(DL)":[52],"technique,":[53],"DL-based":[55],"models":[56,69],"have":[57],"been":[58],"proposed":[59,94,167],"for":[60,73],"noise":[61,172],"reduction":[62],"CT.":[64],"However,":[65],"most":[66],"require":[70],"paired":[71],"datasets":[72],"training,":[74],"which":[75,146],"are":[76,117],"rarely":[77],"available":[78],"practical":[80],"imaging":[81],"procedures.":[82],"Inspired":[83],"by":[84],"structural":[86],"similarities":[87],"channel's":[90],"image,":[92],"we":[93],"a":[95,135],"self-supervised":[96],"based":[98],"PCCT":[99,188],"image":[100,125,137,189],"enhancement":[101],"framework":[102],"via":[103],"multi-spectral":[104],"channels":[105],"(S2MS).":[106],"In":[107],"S2MS":[109,168],"framework,":[110],"both":[111],"input":[113,141],"output":[115],"labels":[116],"noisy":[118,162],"images.":[119],"Specifically,":[120],"one":[121],"single":[122],"was":[126],"used":[127,139],"as":[128,140],"output.":[129],"The":[130],"other":[131],"full-energy":[136],"were":[138],"train":[143],"network,":[145],"fully":[148],"use":[149],"data":[152,163],"information":[153],"without":[154],"extra":[155],"cost.":[156],"Experiments":[157],"on":[158],"simulated":[159],"real":[161],"demonstrate":[164],"that":[165],"model":[169],"suppress":[171],"preserve":[174],"details":[175],"more":[176],"effectively":[177],"than":[178],"traditional":[179],"DL":[180],"models,":[181],"has":[183],"potential":[185],"improve":[187],"quality":[190],"clinical":[192],"applications":[193]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}