{"id":"https://openalex.org/W3202680281","doi":"https://doi.org/10.1145/3458380.3458381","title":"Adaptive Enhanced Generative Adversarial Network for MRI Reconstruction","display_name":"Adaptive Enhanced Generative Adversarial Network for MRI Reconstruction","publication_year":2021,"publication_date":"2021-02-26","ids":{"openalex":"https://openalex.org/W3202680281","doi":"https://doi.org/10.1145/3458380.3458381","mag":"3202680281"},"language":"en","primary_location":{"id":"doi:10.1145/3458380.3458381","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3458380.3458381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 5th International Conference on Digital Signal Processing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5059310597","display_name":"Wenzhong Zhou","orcid":"https://orcid.org/0000-0002-8368-7834"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenzhong Zhou","raw_affiliation_strings":["Beijing Institute of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101699052","display_name":"Huiqian Du","orcid":"https://orcid.org/0000-0001-5664-0224"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiqian Du","raw_affiliation_strings":["Beijing Institute of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023264151","display_name":"Wenbo Mei","orcid":"https://orcid.org/0000-0002-1041-0666"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbo Mei","raw_affiliation_strings":["Beijing Institute of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001248594","display_name":"Liping Fang","orcid":"https://orcid.org/0000-0001-5849-913X"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liping Fang","raw_affiliation_strings":["Beijing Institute of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17645182,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9990000128746033,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9984999895095825,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.7902383208274841},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6637500524520874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6512376666069031},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6472030878067017},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6201744079589844},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5946661829948425},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5937259197235107},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49522677063941956},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4312882721424103},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.427381306886673},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4229596257209778},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2714187502861023},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19433394074440002},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10741627216339111}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7902383208274841},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6637500524520874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6512376666069031},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6472030878067017},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6201744079589844},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5946661829948425},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5937259197235107},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49522677063941956},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4312882721424103},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.427381306886673},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4229596257209778},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2714187502861023},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19433394074440002},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10741627216339111},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3458380.3458381","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3458380.3458381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 5th International Conference on Digital Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4300000071525574,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2029816571","https://openalex.org/W2093621384","https://openalex.org/W2097117768","https://openalex.org/W2097474301","https://openalex.org/W2099471712","https://openalex.org/W2101675075","https://openalex.org/W2104266187","https://openalex.org/W2114504143","https://openalex.org/W2141168890","https://openalex.org/W2156739854","https://openalex.org/W2163973643","https://openalex.org/W2168668658","https://openalex.org/W2194775991","https://openalex.org/W2304034118","https://openalex.org/W2442117232","https://openalex.org/W2552808051","https://openalex.org/W2594014149","https://openalex.org/W2621182918","https://openalex.org/W2778924750","https://openalex.org/W2791621240","https://openalex.org/W2912147220","https://openalex.org/W2922509574","https://openalex.org/W2962793481","https://openalex.org/W2962903101","https://openalex.org/W2963073614","https://openalex.org/W2963470893","https://openalex.org/W2981609437","https://openalex.org/W4250955649"],"related_works":["https://openalex.org/W4387838477","https://openalex.org/W2067193074","https://openalex.org/W2182785089","https://openalex.org/W3034421924","https://openalex.org/W2982536526","https://openalex.org/W4386858688","https://openalex.org/W4380302312","https://openalex.org/W3008689640","https://openalex.org/W4385338604","https://openalex.org/W3081626085"],"abstract_inverted_index":{"Extracting":[0],"effective":[1],"feature":[2,26,65,128,148],"information":[3,129,149],"is":[4,29],"very":[5],"important":[6],"for":[7,25,84],"deep":[8,13],"learning":[9],"methods.":[10],"However,":[11],"most":[12],"learning-based":[14],"CS-MRI":[15,169],"methods":[16,170],"use":[17],"the":[18,34,48,55,64,69,89,97,103,105,112,116,120,133,139,162,166],"standard":[19],"convolutional":[20],"layers":[21],"with":[22,171],"square":[23,117],"kernels":[24],"extraction,":[27],"which":[28],"difficult":[30],"to":[31,61,110,124],"further":[32,155],"improve":[33],"accuracy":[35],"of":[36,68,100,115,135,141],"MRI":[37,86],"reconstruction":[38],"under":[39],"limited":[40],"computational":[41],"resources.":[42],"In":[43,102,154],"this":[44],"paper,":[45],"we":[46,72],"propose":[47,73],"enhanced":[49,77],"asymmetric":[50,57],"convolution":[51],"blocks":[52,59],"(EACBs)":[53],"and":[54,92,119,143,152],"selective":[56],"kernel":[58],"(SAKBs)":[60],"effectively":[62,151],"enhance":[63,111],"extraction":[66],"ability":[67],"network.":[70],"Further,":[71],"a":[74],"novel":[75],"adaptive":[76],"generative":[78],"adversarial":[79],"network,":[80],"termed":[81],"as":[82],"AEGAN,":[83,104],"high-precision":[85],"reconstruction,":[87],"where":[88],"proposed":[90,106,163],"EACBs":[91,107,142],"SAKBs":[93,121,144],"are":[94,108,122],"embedded":[95],"in":[96],"network":[98],"architecture":[99],"AEGAN.":[101],"used":[109,123],"corresponding":[113],"skeleton":[114],"kernel,":[118],"efficiently":[125],"capture":[126],"useful":[127],"by":[130],"adaptively":[131],"adjusting":[132],"size":[134],"receptive":[136],"fields.":[137],"Therefore,":[138],"combination":[140],"can":[145,158],"extract":[146],"rich":[147],"more":[150],"efficiently.":[153],"experiments,":[156],"it":[157],"be":[159],"demonstrated":[160],"that":[161],"AEGAN":[164],"exceeds":[165],"state-of-the-art":[167],"GAN-based":[168],"fewer":[172],"model":[173],"parameters.":[174]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}