{"id":"https://openalex.org/W3005191255","doi":"https://doi.org/10.1109/bibm47256.2019.8983233","title":"Brain MRI Super-resolution Reconstruction using a Multi-level and Parallel Conv-Deconv Network","display_name":"Brain MRI Super-resolution Reconstruction using a Multi-level and Parallel Conv-Deconv Network","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3005191255","doi":"https://doi.org/10.1109/bibm47256.2019.8983233","mag":"3005191255"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm47256.2019.8983233","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5100444623","display_name":"Lulu Wang","orcid":"https://orcid.org/0000-0001-7466-9522"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lulu Wang","raw_affiliation_strings":["College of Computer Science, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003865319","display_name":"Jinglong Du","orcid":"https://orcid.org/0000-0002-4225-0425"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinglong Du","raw_affiliation_strings":["College of Computer Science, Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058812679","display_name":"Ali Gholipour","orcid":"https://orcid.org/0000-0001-7699-4564"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]},{"id":"https://openalex.org/I1288882113","display_name":"Boston Children's Hospital","ror":"https://ror.org/00dvg7y05","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1288882113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Gholipour","raw_affiliation_strings":["Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, USA","institution_ids":["https://openalex.org/I136199984","https://openalex.org/I1288882113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100676074","display_name":"Zhongshi He","orcid":"https://orcid.org/0009-0007-3682-0464"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]},{"id":"https://openalex.org/I1288882113","display_name":"Boston Children's Hospital","ror":"https://ror.org/00dvg7y05","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1288882113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhongshi He","raw_affiliation_strings":["Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, USA","institution_ids":["https://openalex.org/I136199984","https://openalex.org/I1288882113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100669028","display_name":"Yuanyuan Jia","orcid":"https://orcid.org/0000-0002-5155-2185"},"institutions":[{"id":"https://openalex.org/I87780372","display_name":"Chongqing Medical University","ror":"https://ror.org/017z00e58","country_code":"CN","type":"education","lineage":["https://openalex.org/I87780372"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Jia","raw_affiliation_strings":["College of Medical Informatics, Chongqing Medical University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"College of Medical Informatics, Chongqing Medical University, Chongqing, China","institution_ids":["https://openalex.org/I87780372"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.135,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.560633,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":77,"max":79},"biblio":{"volume":null,"issue":null,"first_page":"885","last_page":"891"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998,"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":0.9998,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9935,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9911,"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/convolution","display_name":"Convolution (computer science)","score":0.62160397},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5590886},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43780917},{"id":"https://openalex.org/keywords/temporal-resolution","display_name":"Temporal resolution","score":0.43090492}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7889542},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.7458341},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.62160397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.60295254},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6007249},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.56262594},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5590886},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5432071},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5140961},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.51315933},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46444395},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46045333},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.44517902},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43780917},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43472517},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4322141},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.43090492},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42149532},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4179019},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25957647},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm47256.2019.8983233","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":28,"referenced_works":["https://openalex.org/W1502698477","https://openalex.org/W1522301498","https://openalex.org/W1641498739","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W1849277567","https://openalex.org/W1965378579","https://openalex.org/W1999327491","https://openalex.org/W2003863798","https://openalex.org/W2009530659","https://openalex.org/W2058881601","https://openalex.org/W2133665775","https://openalex.org/W2140854449","https://openalex.org/W2141200610","https://openalex.org/W2155893237","https://openalex.org/W2187351272","https://openalex.org/W2242218935","https://openalex.org/W2474584297","https://openalex.org/W2503339013","https://openalex.org/W2709402577","https://openalex.org/W2807184855","https://openalex.org/W2808495892","https://openalex.org/W2906473494","https://openalex.org/W2912226037","https://openalex.org/W2942080485","https://openalex.org/W2964121744","https://openalex.org/W2964125708","https://openalex.org/W54257720"],"related_works":["https://openalex.org/W2383495548","https://openalex.org/W2370645350","https://openalex.org/W2369061952","https://openalex.org/W2367122702","https://openalex.org/W2132989621","https://openalex.org/W2085978486","https://openalex.org/W2015447694","https://openalex.org/W1983978385","https://openalex.org/W1969590113","https://openalex.org/W1601492201"],"abstract_inverted_index":{"High":[0],"resolution":[1,50,73],"(HR)":[2],"magnetic":[3],"resonance":[4],"images":[5],"(MRI)":[6],"provide":[7],"rich":[8],"tissue":[9],"anatomical":[10],"information":[11,98],"that":[12,160],"enables":[13],"accurate":[14],"diagnostics":[15],"and":[16,32,60,92,99,115,170],"pathological":[17],"analysis.":[18],"However,":[19],"the":[20,48,121,128,133,137,150],"acquisition":[21],"of":[22,51,110],"HR":[23,69,122],"MRI":[24,70,167],"is":[25],"limited":[26],"by":[27],"hardware":[28],"restrictions,":[29],"scanning":[30],"time,":[31],"signal-to-noise":[33],"ratio":[34],"(SNR)":[35],"in":[36],"clinical":[37],"applications.":[38],"Recently,":[39],"deep":[40],"learning":[41,146],"has":[42],"shown":[43],"promising":[44],"power":[45],"for":[46],"improving":[47],"spatial":[49],"MRI.":[52],"In":[53,124],"this":[54],"study,":[55],"we":[56,87,131],"propose":[57],"a":[58,143,172],"multilevel":[59],"parallel":[61,89],"Conv-Deconv":[62],"super-resolution":[63],"(CDSR)":[64],"network":[65],"to":[66,95,119,126,136],"reconstruct":[67],"high-quality":[68],"from":[71,77],"low":[72],"(LR)":[74],"inputs.":[75],"Different":[76],"current":[78,165],"SR":[79,168],"methods":[80,169],"based":[81],"on":[82,155],"convolutional":[83],"neural":[84],"networks":[85],"(CNNs),":[86],"connect":[88],"3D":[90],"convolution":[91],"deconvolution":[93,113],"filters":[94],"capture":[96],"context":[97],"extract":[100],"multi-level":[101],"features.":[102],"Hierarchical":[103],"features":[104],"are":[105],"adaptively":[106],"upsampled":[107],"using":[108],"each":[109],"their":[111],"following":[112],"layers":[114],"then":[116],"fused":[117,138],"together":[118],"recover":[120],"details.":[123],"order":[125],"alleviate":[127],"optimization":[129],"difficulty,":[130],"introduce":[132],"interpolated":[134],"input":[135],"output,":[139],"which":[140],"performs":[141],"like":[142],"cross-scale":[144],"residual":[145],"strategy,":[147],"hence":[148],"accelerates":[149],"convergence.":[151],"Extensive":[152],"experimental":[153],"results":[154],"three":[156],"benchmark":[157],"datasets":[158],"show":[159],"our":[161],"proposed":[162],"method":[163],"outperforms":[164],"reported":[166],"sets":[171],"new":[173],"state-of-the-art":[174],"performance.":[175]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3005191255","counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2024-12-07T04:57:38.646676","created_date":"2020-02-14"}