{"id":"https://openalex.org/W4224306590","doi":"https://doi.org/10.1109/tcbb.2022.3168189","title":"Deep Transfer Learning-Based Multi-Modal Digital Twins for Enhancement and Diagnostic Analysis of Brain MRI Image","display_name":"Deep Transfer Learning-Based Multi-Modal Digital Twins for Enhancement and Diagnostic Analysis of Brain MRI Image","publication_year":2022,"publication_date":"2022-04-19","ids":{"openalex":"https://openalex.org/W4224306590","doi":"https://doi.org/10.1109/tcbb.2022.3168189","pmid":"https://pubmed.ncbi.nlm.nih.gov/35439137"},"language":"en","primary_location":{"id":"doi:10.1109/tcbb.2022.3168189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcbb.2022.3168189","pdf_url":null,"source":{"id":"https://openalex.org/S36029991","display_name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","issn_l":"1545-5963","issn":["1545-5963","1557-9964","2374-0043"],"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/ACM Transactions on Computational Biology and Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jinxia Wang","orcid":"https://orcid.org/0000-0003-3555-1147"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinxia Wang","raw_affiliation_strings":["School of Art and Design, Shaanxi Fashion Engineering University, Xi&#x0027;an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0003-3555-1147","affiliations":[{"raw_affiliation_string":"School of Art and Design, Shaanxi Fashion Engineering University, Xi&#x0027;an, Shaanxi, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Liang Qiao","orcid":"https://orcid.org/0000-0002-8188-886X"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Qiao","raw_affiliation_strings":["College of Computer Science &#x0026; Technology, Qingdao University, Qingdao, Shandong, China"],"raw_orcid":"https://orcid.org/0000-0002-8188-886X","affiliations":[{"raw_affiliation_string":"College of Computer Science &#x0026; Technology, Qingdao University, Qingdao, Shandong, China","institution_ids":["https://openalex.org/I108688024"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haibin Lv","orcid":"https://orcid.org/0000-0003-1059-4765"},"institutions":[{"id":"https://openalex.org/I1331374319","display_name":"National Bureau of Statistics of China","ror":"https://ror.org/008zm7t63","country_code":"CN","type":"other","lineage":["https://openalex.org/I1331374319","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibin Lv","raw_affiliation_strings":["North China Sea Offshore Engineering Survey Institute, Ministry of Natural Resources North Sea Bureau, Qingdao, Shandong, China"],"raw_orcid":"https://orcid.org/0000-0003-1059-4765","affiliations":[{"raw_affiliation_string":"North China Sea Offshore Engineering Survey Institute, Ministry of Natural Resources North Sea Bureau, Qingdao, Shandong, China","institution_ids":["https://openalex.org/I1331374319","https://openalex.org/I211433327"]}]},{"author_position":"last","author":{"id":null,"display_name":"Zhihan Lv","orcid":"https://orcid.org/0000-0003-2525-3074"},"institutions":[{"id":"https://openalex.org/I123387679","display_name":"Uppsala University","ror":"https://ror.org/048a87296","country_code":"SE","type":"education","lineage":["https://openalex.org/I123387679"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Zhihan Lv","raw_affiliation_strings":["Faculty of Arts, Uppsala University, Uppsala, Sweden"],"raw_orcid":"https://orcid.org/0000-0003-2525-3074","affiliations":[{"raw_affiliation_string":"Faculty of Arts, Uppsala University, Uppsala, Sweden","institution_ids":["https://openalex.org/I123387679"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6247,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.90812741,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"20","issue":"4","first_page":"2407","last_page":"2419"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.19020000100135803,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.19020000100135803,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.12280000001192093,"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/T14413","display_name":"Advanced Technologies in Various Fields","score":0.04989999905228615,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/image","display_name":"Image (mathematics)","score":0.450300008058548},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41370001435279846},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.38179999589920044},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.3625999987125397},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.36250001192092896},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.3467999994754791},{"id":"https://openalex.org/keywords/digital-image","display_name":"Digital image","score":0.32269999384880066},{"id":"https://openalex.org/keywords/image-enhancement","display_name":"Image enhancement","score":0.30550000071525574}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6929000020027161},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6424999833106995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5508999824523926},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.450300008058548},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41370001435279846},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.36250001192092896},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C42781572","wikidata":"https://www.wikidata.org/wiki/Q1250322","display_name":"Digital image","level":4,"score":0.32269999384880066},{"id":"https://openalex.org/C3017601658","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image enhancement","level":3,"score":0.30550000071525574},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C157787499","wikidata":"https://www.wikidata.org/wiki/Q13479657","display_name":"Real-time MRI","level":3,"score":0.28929999470710754},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.26589998602867126},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.2651999890804291},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C2781305912","wikidata":"https://www.wikidata.org/wiki/Q1225030","display_name":"Digital radiography","level":3,"score":0.25920000672340393},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tcbb.2022.3168189","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcbb.2022.3168189","pdf_url":null,"source":{"id":"https://openalex.org/S36029991","display_name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","issn_l":"1545-5963","issn":["1545-5963","1557-9964","2374-0043"],"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/ACM Transactions on Computational Biology and Bioinformatics","raw_type":"journal-article"},{"id":"pmid:35439137","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35439137","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM transactions on computational biology and bioinformatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2898048761","https://openalex.org/W2941876953","https://openalex.org/W2955446216","https://openalex.org/W2957375862","https://openalex.org/W2963470017","https://openalex.org/W2963787510","https://openalex.org/W2965022726","https://openalex.org/W2984424697","https://openalex.org/W2985755379","https://openalex.org/W2989497143","https://openalex.org/W2991356914","https://openalex.org/W2991534950","https://openalex.org/W2995272337","https://openalex.org/W2998746225","https://openalex.org/W2999778662","https://openalex.org/W3000314771","https://openalex.org/W3001554227","https://openalex.org/W3007473102","https://openalex.org/W3012084003","https://openalex.org/W3021895818","https://openalex.org/W3024560037","https://openalex.org/W3033864100","https://openalex.org/W3036600965","https://openalex.org/W3040726928","https://openalex.org/W3049131298","https://openalex.org/W3115004926","https://openalex.org/W3118545455","https://openalex.org/W3154029201","https://openalex.org/W6770649082","https://openalex.org/W6785706520"],"related_works":[],"abstract_inverted_index":{"OBJECTIVE:":[0],"it":[1],"aims":[2],"to":[3,98,191],"adopt":[4],"deep":[5,53,58,116],"transfer":[6,54,117],"learning":[7,118],"combined":[8],"with":[9,185],"Digital":[10],"Twins":[11],"(DTs)":[12],"in":[13,40,156,174,202,257],"Magnetic":[14,175,206],"Resonance":[15,176,207],"Imaging":[16],"(MRI)":[17],"medical":[18,41,70,79,147],"image":[19,23,80,148,166,183],"enhancement.":[20],"METHODS:":[21],"MRI":[22,47,56,133,262],"enhancement":[24],"method":[25],"based":[26,83],"on":[27,84],"metamaterial":[28],"composite":[29,140],"technology":[30],"is":[31,62,87,127,245],"proposed":[32,88,146,249],"by":[33,91,107,138],"analyzing":[34],"the":[35,44,50,66,94,132,152,157,161,171,192,199,216,233,248,272],"application":[36],"status":[37],"of":[38,46,52,102,115,160,164,188,195,205,261,268],"DTs":[39],"direction":[42,267],"and":[43,75,89,112,135,227,239,264,271],"principle":[45],"imaging.":[48],"On":[49],"basis":[51],"learning,":[55],"super-resolution":[57],"neural":[59,119],"network":[60,120],"structure":[61],"established.":[63],"To":[64],"address":[65],"problem":[67],"that":[68,131],"different":[69],"imaging":[71],"methods":[72],"have":[73,276],"advantages":[74],"disadvantages,":[76],"a":[77,186],"multi-mode":[78],"fusion":[81,149,165],"algorithm":[82,150,250],"adaptive":[85],"decomposition":[86],"verified":[90],"experiments.":[92],"RESULTS:":[93],"optimal":[95],"Peak":[96],"Signal":[97],"Noise":[99],"Ratio":[100],"(PSNR)":[101],"34.11dB":[103],"can":[104],"be":[105],"obtained":[106,137],"introducing":[108],"modified":[109],"linear":[110],"element":[111],"loss":[113],"function":[114],"structure.":[121],"The":[122,145],"Structural":[123],"Similarity":[124],"Coefficient":[125],"(SSIM)":[126],"85.24%.":[128],"It":[129],"indicates":[130],"truthfulness":[134],"sharpness":[136],"adding":[139],"metasurface":[141],"are":[142,232,236],"improved":[143],"greatly.":[144],"has":[151,251],"highest":[153,172],"overall":[154],"score":[155,173,187,194],"subjective":[158],"evaluation":[159,201],"six":[162],"groups":[163,275],"results.":[167,279],"Group":[168],"III":[169],"had":[170],"Imaging-":[177,208],"Positron":[178],"Emission":[179,211],"Computed":[180,212],"Tomography":[181,213],"(MRI-PET)":[182],"fusion,":[184],"4.67,":[189],"close":[190],"full":[193],"5.":[196],"As":[197],"for":[198],"objective":[200],"group":[203],"I":[204],"Single":[209],"Photon":[210],"(MRI-SPECT)":[214],"images,":[215,270],"Root":[217],"Mean":[218],"Square":[219],"Error":[220,225],"(RMSE),":[221],"Relative":[222],"Average":[223],"Spectral":[224,228],"(RASE)":[226],"Angle":[229],"Mapper":[230],"(SAM)":[231],"highest,":[234],"which":[235],"39.2075,":[237],"116.688,":[238],"0.594,":[240],"respectively.":[241],"Mutual":[242],"Information":[243],"(MI)":[244],"5.8822.":[246],"CONCLUSION:":[247],"better":[252],"performance":[253],"than":[254],"other":[255,273],"algorithms":[256],"preserving":[258],"spatial":[259],"details":[260],"images":[263],"color":[265],"information":[266],"SPECT":[269],"five":[274],"achieved":[277],"similar":[278]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-17T08:01:34.144755","created_date":"2022-04-26T00:00:00"}
