{"id":"https://openalex.org/W2921595773","doi":"https://doi.org/10.1117/12.2513158","title":"Deep residual dense U-Net for resolution enhancement in accelerated MRI acquisition","display_name":"Deep residual dense U-Net for resolution enhancement in accelerated MRI acquisition","publication_year":2019,"publication_date":"2019-03-14","ids":{"openalex":"https://openalex.org/W2921595773","doi":"https://doi.org/10.1117/12.2513158","mag":"2921595773"},"language":"en","primary_location":{"id":"doi:10.1117/12.2513158","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2513158","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Processing","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/A5025773749","display_name":"Pak Lun Kevin Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pak Lun Kevin Ding","raw_affiliation_strings":["Arizona State Univ. (United States)"],"affiliations":[{"raw_affiliation_string":"Arizona State Univ. (United States)","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100640908","display_name":"Zhiqiang Li","orcid":"https://orcid.org/0000-0003-0348-9801"},"institutions":[{"id":"https://openalex.org/I1324766304","display_name":"Barrow Neurological Institute","ror":"https://ror.org/01fwrsq33","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1324766304","https://openalex.org/I1325481341","https://openalex.org/I2802827176"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiqiang Li","raw_affiliation_strings":["Barrow Neurological Institute (United States)"],"affiliations":[{"raw_affiliation_string":"Barrow Neurological Institute (United States)","institution_ids":["https://openalex.org/I1324766304"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107953281","display_name":"Yuxiang Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I2802423016","display_name":"WinnMed","ror":"https://ror.org/02s47w807","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2802423016"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuxiang Zhou","raw_affiliation_strings":["Mayo Clinic (United States)"],"affiliations":[{"raw_affiliation_string":"Mayo Clinic (United States)","institution_ids":["https://openalex.org/I2802423016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032615847","display_name":"Baoxin Li","orcid":"https://orcid.org/0000-0002-9294-4572"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Baoxin Li","raw_affiliation_strings":["Arizona State Univ. (United States)"],"affiliations":[{"raw_affiliation_string":"Arizona State Univ. (United States)","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5025773749"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":1.9929,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.85402232,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"14","last_page":"14"},"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":0.9976000189781189,"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":0.9976000189781189,"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":0.9966999888420105,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9962999820709229,"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/computer-science","display_name":"Computer science","score":0.7809153199195862},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.743245005607605},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.656531810760498},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.645824134349823},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5520039200782776},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5182802081108093},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4795360565185547},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4771401584148407},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4477258026599884},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4359545111656189},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.43485477566719055},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.4246370494365692},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18036073446273804},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15876254439353943},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09994480013847351}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7809153199195862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.743245005607605},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.656531810760498},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.645824134349823},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5520039200782776},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5182802081108093},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4795360565185547},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4771401584148407},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4477258026599884},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4359545111656189},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.43485477566719055},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.4246370494365692},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18036073446273804},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15876254439353943},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09994480013847351},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2513158","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2513158","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2019: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W212537071","https://openalex.org/W639708223","https://openalex.org/W1497904071","https://openalex.org/W2118330267","https://openalex.org/W2138863404","https://openalex.org/W2163605009","https://openalex.org/W2442117232","https://openalex.org/W2631883531","https://openalex.org/W2757509933","https://openalex.org/W2777662428","https://openalex.org/W2780544323","https://openalex.org/W2786811008","https://openalex.org/W2791216858","https://openalex.org/W2807567802","https://openalex.org/W2963150697","https://openalex.org/W2963446712","https://openalex.org/W4200283578","https://openalex.org/W4231200252","https://openalex.org/W4234552385","https://openalex.org/W4238964153","https://openalex.org/W4239726661","https://openalex.org/W4254347343","https://openalex.org/W6620707391","https://openalex.org/W6639824700","https://openalex.org/W6657892929","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6697563226","https://openalex.org/W6725739302","https://openalex.org/W6747381837","https://openalex.org/W6748585158"],"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":{"Typical":[0],"Magnetic":[1],"Resonance":[2],"Imaging":[3],"(MRI)":[4],"scan":[5,14,27],"may":[6,28],"take":[7],"20":[8],"to":[9,45,62,136,185,200,237],"60":[10],"minutes.":[11],"Reducing":[12],"MRI":[13,26,110,127],"time":[15],"is":[16,160,235],"beneficial":[17],"for":[18,51,60,83,162,244],"both":[19,224],"patient":[20],"experience":[21],"and":[22,48,73,86,144,227],"cost":[23],"considerations.":[24],"Accelerated":[25],"be":[29],"achieved":[30],"by":[31],"acquiring":[32],"less":[33],"amount":[34],"of":[35,99,175,207],"k-space":[36,68,178,196],"data":[37,197],"(down-sampling":[38],"in":[39,104,189],"the":[40,52,93,100,138,141,145,169,173,176,186,194,205,208,213,232],"k-space).":[41],"However,":[42],"this":[43,84,113],"leads":[44],"lower":[46],"resolution":[47],"aliasing":[49],"artifacts":[50],"reconstructed":[53,109],"images.":[54,111],"There":[55],"are":[56],"many":[57],"existing":[58],"approaches":[59,79],"attempting":[61],"reconstruct":[63],"high-quality":[64,123],"images":[65,124,143],"from":[66,125],"down-sampled":[67],"data,":[69,179],"with":[70,219],"varying":[71],"complexity":[72],"performance.":[74],"In":[75,112,223],"recent":[76],"years,":[77],"deep-learning":[78,118],"have":[80,89],"been":[81,90],"proposed":[82,214,233],"task,":[85],"promising":[87],"results":[88],"reported.":[91],"Still,":[92],"problem":[94],"remains":[95],"challenging":[96],"especially":[97],"because":[98],"high":[101],"fidelity":[102],"requirement":[103],"most":[105],"medical":[106],"applications":[107],"employing":[108,148],"work,":[114],"we":[115,130,180,216],"propose":[116],"a":[117,149,153,164,182],"approach,":[119,215],"aiming":[120],"at":[121],"reconstructing":[122],"accelerated":[126],"acquisition.":[128],"Specifically,":[129],"use":[131],"Convolutional":[132],"Neural":[133],"Network":[134],"(CNN)":[135],"learn":[137],"differences":[139],"between":[140],"aliased":[142],"original":[146],"images,":[147],"U-Net-like":[150],"architecture.":[151],"Further,":[152],"micro-architecture":[154],"termed":[155],"Residual":[156],"Dense":[157],"Block":[158],"(RDB)":[159],"introduced":[161],"learning":[163],"better":[165],"feature":[166],"representation":[167],"than":[168],"plain":[170],"U-Net.":[171],"Considering":[172],"peculiarity":[174],"downsampled":[177],"introduce":[181],"new":[183],"term":[184],"loss":[187],"function":[188],"learning,":[190],"which":[191],"effectively":[192],"employs":[193],"given":[195],"during":[198],"training":[199],"provide":[201],"additional":[202],"regularization":[203],"on":[204],"update":[206],"network":[209],"weights.":[210],"To":[211],"evaluate":[212],"compare":[217],"it":[218],"other":[220],"state-of-the-art":[221],"methods.":[222],"visual":[225],"inspection":[226],"evaluation":[228],"using":[229],"standard":[230],"metrics,":[231],"approach":[234],"able":[236],"deliver":[238],"improved":[239],"performance,":[240],"demonstrating":[241],"its":[242],"potential":[243],"providing":[245],"an":[246],"effective":[247],"solution.":[248]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}