{"id":"https://openalex.org/W4313527280","doi":"https://doi.org/10.1109/bibm55620.2022.9995219","title":"Multi-contrast High Quality MR Image Super-Resolution with Dual Domain Knowledge Fusion","display_name":"Multi-contrast High Quality MR Image Super-Resolution with Dual Domain Knowledge Fusion","publication_year":2022,"publication_date":"2022-12-06","ids":{"openalex":"https://openalex.org/W4313527280","doi":"https://doi.org/10.1109/bibm55620.2022.9995219"},"language":"en","primary_location":{"id":"doi:10.1109/bibm55620.2022.9995219","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm55620.2022.9995219","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5089903460","display_name":"Runhan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runhan Wang","raw_affiliation_strings":["Fudan University,School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing,Shanghai,200433"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing,Shanghai,200433","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100626035","display_name":"Rui-Wei Zhao","orcid":"https://orcid.org/0000-0002-8498-5761"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiwei Zhao","raw_affiliation_strings":["Fudan University,Academy for Engineering and Technology,Shanghai","Academy for Engineering and Technology, Fudan University, Shanghai"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Academy for Engineering and Technology,Shanghai","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053911342","display_name":"Weijia Fu","orcid":"https://orcid.org/0009-0002-5955-3534"},"institutions":[{"id":"https://openalex.org/I4210159329","display_name":"Children's Hospital of Fudan University","ror":"https://ror.org/05n13be63","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210159329"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijia Fu","raw_affiliation_strings":["Children&#x2019;s Hospital of Fudan University,National Children&#x2019;s Medical Center,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Children&#x2019;s Hospital of Fudan University,National Children&#x2019;s Medical Center,Shanghai,China","institution_ids":["https://openalex.org/I4210159329"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115592812","display_name":"Xiaobo Zhang","orcid":"https://orcid.org/0009-0005-0532-6100"},"institutions":[{"id":"https://openalex.org/I4210159329","display_name":"Children's Hospital of Fudan University","ror":"https://ror.org/05n13be63","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210159329"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaobo Zhang","raw_affiliation_strings":["Children&#x2019;s Hospital of Fudan University,National Children&#x2019;s Medical Center,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Children&#x2019;s Hospital of Fudan University,National Children&#x2019;s Medical Center,Shanghai,China","institution_ids":["https://openalex.org/I4210159329"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070073283","display_name":"Yuejie Zhang","orcid":"https://orcid.org/0000-0001-7993-7223"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuejie Zhang","raw_affiliation_strings":["Fudan University,School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing,Shanghai,200433"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing,Shanghai,200433","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101860531","display_name":"Rui Feng","orcid":"https://orcid.org/0000-0001-6648-953X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]},{"id":"https://openalex.org/I4210159329","display_name":"Children's Hospital of Fudan University","ror":"https://ror.org/05n13be63","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210159329"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Feng","raw_affiliation_strings":["Fudan University,School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing,Shanghai,200433","Academy for Engineering and Technology, Fudan University, Shanghai","National Children's Medical Center, Children's Hospital of Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing,Shanghai,200433","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"National Children's Medical Center, Children's Hospital of Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I4210159329"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3539,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.68595159,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2127","last_page":"2134"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998000264167786,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998000264167786,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/computer-science","display_name":"Computer science","score":0.7696021795272827},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6927440762519836},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.6894785761833191},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5595332980155945},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.5544905066490173},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5200223922729492},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.4999241828918457},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49175432324409485},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4746207296848297},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4362373650074005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3440212905406952}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7696021795272827},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6927440762519836},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.6894785761833191},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5595332980155945},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.5544905066490173},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5200223922729492},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.4999241828918457},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49175432324409485},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4746207296848297},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4362373650074005},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3440212905406952}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm55620.2022.9995219","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm55620.2022.9995219","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321885","display_name":"Science and Technology Commission of Shanghai Municipality","ror":"https://ror.org/03kt66j61"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2117539524","https://openalex.org/W2133665775","https://openalex.org/W2134584543","https://openalex.org/W2194775991","https://openalex.org/W2604388535","https://openalex.org/W2745006834","https://openalex.org/W2789713147","https://openalex.org/W2800433434","https://openalex.org/W2890139949","https://openalex.org/W2891888042","https://openalex.org/W2907750714","https://openalex.org/W2914057844","https://openalex.org/W2963073614","https://openalex.org/W2963091558","https://openalex.org/W2963446712","https://openalex.org/W2963768110","https://openalex.org/W2964297772","https://openalex.org/W2966737464","https://openalex.org/W3034546843","https://openalex.org/W3035022492","https://openalex.org/W3035596626","https://openalex.org/W3102018640","https://openalex.org/W3138516171","https://openalex.org/W3183434090","https://openalex.org/W3202542211","https://openalex.org/W3202624106","https://openalex.org/W3203480968","https://openalex.org/W4321232185"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2607795551","https://openalex.org/W3155117723","https://openalex.org/W1991429770","https://openalex.org/W1983892167","https://openalex.org/W2281134365","https://openalex.org/W4310746709","https://openalex.org/W4385574037","https://openalex.org/W4386075645","https://openalex.org/W4306309518"],"abstract_inverted_index":{"Multi-contrast":[0],"high":[1,67],"quality":[2],"high-resolution":[3],"(HR)":[4],"Magnetic":[5],"Resonance":[6],"(MR)":[7],"images":[8,38,51,100],"enrich":[9],"available":[10],"information":[11,69,126,181],"for":[12,25,97,185],"diagnosis":[13],"and":[14,112,130,139,175,203],"analysis.":[15],"Deep":[16],"convolutional":[17],"neural":[18],"network":[19,157],"methods":[20,199],"have":[21,42],"shown":[22],"promising":[23],"ability":[24],"MR":[26,33,50,56,99],"image":[27,57,72,83,132,162],"super-resolution":[28],"(SR)":[29],"given":[30],"low-resolution":[31],"(LR)":[32],"images.":[34],"Methods":[35],"taking":[36],"HR":[37],"as":[39],"references":[40],"(Ref)":[41],"made":[43],"progress":[44],"to":[45,78,108,143,161,177,209],"enhance":[46],"the":[47,128,145,151,156,190,210],"effect":[48],"of":[49,70,81,115,127,133,182,192],"SR.":[52,101],"However,":[53],"existing":[54],"multi-contrast":[55],"SR":[58],"approaches":[59],"are":[60],"based":[61],"on":[62,200],"contrasting-expanding":[63],"backbones,":[64],"which":[65,196],"lose":[66],"frequency":[68],"Ref":[71,82],"during":[73],"downsampling.":[74],"They":[75],"also":[76,166],"failed":[77],"transfer":[79],"textures":[80],"into":[84],"target":[85,116,134],"domain.":[86,117,135],"In":[87],"this":[88],"paper,":[89],"we":[90,137],"propose":[91,103],"Edge":[92,104],"Mask":[93,105],"Transformer":[94,106],"UNet":[95,123],"(EMFU)":[96],"accelerating":[98],"We":[102,165],"(EMF)":[107],"generate":[109],"global":[110],"details":[111],"texture":[113],"representation":[114,129],"Dual":[118],"domain":[119,170],"fusion":[120,171],"module":[121,172],"in":[122,147],"aggregates":[124],"semantic":[125,180],"LR":[131],"Specifically,":[136],"extract":[138],"encode":[140],"edge":[141,163],"masks":[142],"guide":[144],"attention":[146,160],"EMF":[148],"by":[149],"re-distributing":[150],"embedding":[152],"tensors,":[153],"so":[154],"that":[155],"allocates":[158],"more":[159],"area.":[164],"design":[167],"a":[168],"dual":[169],"with":[173],"self-attention":[174],"cross-attention":[176],"deeply":[178],"fuse":[179],"multiple":[183],"protocols":[184],"MRI.":[186],"Extensive":[187],"experiments":[188],"show":[189],"effectiveness":[191],"our":[193],"proposed":[194],"EMFU,":[195],"surpasses":[197],"state-of-the-art":[198],"benchmarks":[201],"quantitatively":[202],"visually.":[204],"Codes":[205],"will":[206],"be":[207],"released":[208],"community.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
