{"id":"https://openalex.org/W2889970951","doi":"https://doi.org/10.23919/icif.2018.8455399","title":"Fast Multi-Coil Parallel MR Imaging Based on a Combination of the Optimum Interpolation Approximation and Compressed Sensing","display_name":"Fast Multi-Coil Parallel MR Imaging Based on a Combination of the Optimum Interpolation Approximation and Compressed Sensing","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2889970951","doi":"https://doi.org/10.23919/icif.2018.8455399","mag":"2889970951"},"language":"en","primary_location":{"id":"doi:10.23919/icif.2018.8455399","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icif.2018.8455399","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Information Fusion (FUSION)","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/A5103290287","display_name":"Yuichi Kida","orcid":null},"institutions":[{"id":"https://openalex.org/I30181195","display_name":"Ohu University","ror":"https://ror.org/01fe6f215","country_code":"JP","type":"education","lineage":["https://openalex.org/I30181195"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yuichi Kida","raw_affiliation_strings":["The School of Pharmaceutical sciences, Ohu University Koriyama-shi, Japan"],"affiliations":[{"raw_affiliation_string":"The School of Pharmaceutical sciences, Ohu University Koriyama-shi, Japan","institution_ids":["https://openalex.org/I30181195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111065230","display_name":"Takuro Kida","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takuro Kida","raw_affiliation_strings":["Tokyo Institute of Technology, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103290287"],"corresponding_institution_ids":["https://openalex.org/I30181195"],"apc_list":null,"apc_paid":null,"fwci":0.1768,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5613666,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"911","last_page":"918"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":1.0,"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":1.0,"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":0.9998000264167786,"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":0.9994000196456909,"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/interpolation","display_name":"Interpolation (computer graphics)","score":0.6931945085525513},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6842446327209473},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5561312437057495},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5214661359786987},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5118290781974792},{"id":"https://openalex.org/keywords/infimum-and-supremum","display_name":"Infimum and supremum","score":0.47606155276298523},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.43836793303489685},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4155622720718384},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4139414131641388},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38221147656440735},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3222699761390686}],"concepts":[{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6931945085525513},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6842446327209473},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5561312437057495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5214661359786987},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5118290781974792},{"id":"https://openalex.org/C95611797","wikidata":"https://www.wikidata.org/wiki/Q17502105","display_name":"Infimum and supremum","level":2,"score":0.47606155276298523},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.43836793303489685},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4155622720718384},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4139414131641388},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38221147656440735},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3222699761390686},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/icif.2018.8455399","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icif.2018.8455399","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Information Fusion (FUSION)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2014547837","https://openalex.org/W2083042020","https://openalex.org/W2101675075","https://openalex.org/W2117649283","https://openalex.org/W2130320505","https://openalex.org/W2767250947","https://openalex.org/W6745323644"],"related_works":["https://openalex.org/W2953175190","https://openalex.org/W2257286100","https://openalex.org/W2099602951","https://openalex.org/W4288318511","https://openalex.org/W2562705109","https://openalex.org/W2098184873","https://openalex.org/W4283118620","https://openalex.org/W2012159417","https://openalex.org/W3033742928","https://openalex.org/W2896778670"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,10,27,48,65,96,108,127],"new":[4,109],"parallel":[5],"imaging":[6],"method":[7,25,32,67,111,135,167,187],"based":[8,86],"on":[9,87],"combination":[11],"of":[12,29,37,50,58,61,68,74,82,98,112,126,142,148,183,190],"the":[13,51,55,70,75,80,83,88,91,113,116,122,133,139,143,146,149,155,160,164,172,180,184,191],"optimum":[14,71,169],"interpolation":[15,72],"approximation":[16,62],"(OIA)":[17],"and":[18,115],"compressed":[19],"sensing":[20],"(CS)":[21],"method.":[22,118,193],"The":[23],"proposed":[24,165,185],"is":[26,77,85,93,136],"kind":[28],"improved":[30],"SENSE":[31],"because":[33],"all":[34],"sensitivity":[35],"maps":[36],"receiver":[38],"coils":[39],"are":[40],"assumed":[41],"to":[42,152,159],"be":[43],"known.":[44],"Firstly,":[45],"we":[46,106,178],"introduce":[47],"theory":[49],"OIA":[52,76,92,114,150],"that":[53,189],"minimizes":[54],"supremum":[56],"value":[57],"any":[59],"measure":[60],"error.":[63],"Secondly,":[64],"practical":[66],"computing":[69],"functions":[73],"presented.":[78],"Because":[79,145],"proof":[81],"optimality":[84],"set":[89],"theory,":[90],"optimized":[94],"for":[95,102,170],"group":[97],"MR":[99,130,162,175],"images,":[100],"not":[101],"particular":[103,128,173],"ones.":[104],"Thirdly,":[105],"present":[107],"combinational":[110,166,186],"CS":[117,134],"In":[119],"this":[120],"method,":[121],"sufficiently":[123],"accurate":[124],"reconstruction":[125,181],"target":[129,161,174],"image":[131],"by":[132],"used":[137],"as":[138,154],"weighting":[140,156],"function":[141,157],"OIA.":[144],"accuracy":[147,182],"tends":[151],"improve":[153],"approaches":[158],"image,":[163],"becomes":[168],"reconstructing":[171],"image.":[176],"Finally,":[177],"compare":[179],"with":[188],"CS-only":[192]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}