{"id":"https://openalex.org/W2887467581","doi":"https://doi.org/10.1109/prni.2018.8423948","title":"Automatic detection of motion artifacts on MRI using Deep CNN","display_name":"Automatic detection of motion artifacts on MRI using Deep CNN","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2887467581","doi":"https://doi.org/10.1109/prni.2018.8423948","mag":"2887467581"},"language":"en","primary_location":{"id":"doi:10.1109/prni.2018.8423948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/prni.2018.8423948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","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/A5043850157","display_name":"Irene Fantini","orcid":null},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Irene Fantini","raw_affiliation_strings":["School of Electrical and Computer Engineering (FEEC), University of Campinas, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering (FEEC), University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017698959","display_name":"Let\u00edcia Rittner","orcid":"https://orcid.org/0000-0001-8182-5554"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leticia Rittner","raw_affiliation_strings":["School of Electrical and Computer Engineering (FEEC), University of Campinas, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering (FEEC), University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080241120","display_name":"Clarissa Lin Yasuda","orcid":"https://orcid.org/0000-0001-9084-7173"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Clarissa Yasuda","raw_affiliation_strings":["Faculty of Medical Sciences (FCM), University of Campinas, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"Faculty of Medical Sciences (FCM), University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087970571","display_name":"Roberto Lotufo","orcid":"https://orcid.org/0000-0002-5652-0852"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Roberto Lotufo","raw_affiliation_strings":["School of Electrical and Computer Engineering (FEEC), University of Campinas, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering (FEEC), University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5043850157"],"corresponding_institution_ids":["https://openalex.org/I181391015"],"apc_list":null,"apc_paid":null,"fwci":2.1218,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.87812622,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9988999962806702,"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":0.9988999962806702,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9977999925613403,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9977999925613403,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8611705899238586},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7944225072860718},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7348093390464783},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6306913495063782},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6171174645423889},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6052244901657104},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5302321910858154},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5052452683448792},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49666839838027954},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.46371152997016907},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.27660274505615234}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8611705899238586},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7944225072860718},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7348093390464783},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6306913495063782},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6171174645423889},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6052244901657104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5302321910858154},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5052452683448792},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49666839838027954},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.46371152997016907},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27660274505615234}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/prni.2018.8423948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/prni.2018.8423948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8199999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1812490466","https://openalex.org/W1995059177","https://openalex.org/W2030309005","https://openalex.org/W2094045772","https://openalex.org/W2117539524","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2265592961","https://openalex.org/W2274287116","https://openalex.org/W2531409750","https://openalex.org/W2752819445","https://openalex.org/W2754887662","https://openalex.org/W2949296127","https://openalex.org/W2964350391","https://openalex.org/W3102796228","https://openalex.org/W6684191040","https://openalex.org/W6686164453","https://openalex.org/W6694260854"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Motion":[0],"artifacts":[1,85,118],"on":[2,22,59,97,120,261,297],"brain":[3],"Magnetic":[4],"Resonance":[5],"Images":[6],"(MRI)":[7],"constitute":[8],"an":[9,48,174,235],"important":[10],"factor":[11],"that":[12],"degrades":[13],"the":[14,18,27,35,38,44,81,94,116,121,136,148,154,163,190,209,218,221,227,232,244,248,254,262,284,288,294],"image":[15,28,36,98,146],"quality,":[16],"impacting":[17],"quantitative":[19],"analysis":[20],"based":[21],"structural":[23],"segmentation.":[24],"Thus,":[25],"assessing":[26],"quality":[29,40,53,70,285,299],"is":[30,66,72,142],"essential":[31],"to":[32,43,78,106,114,132,143,152,162,225,279,283],"determine":[33],"if":[34],"fulfills":[37],"minimal":[39],"level":[41,159],"necessary":[42],"research":[45],"analysis.":[46],"Nowadays":[47],"MR":[49],"expert,":[50,287],"responsible":[51],"for":[52],"control,":[54],"performs":[55],"a":[56],"visual":[57],"check":[58],"every":[60],"acquired":[61],"image.":[62],"The":[63,166,183,196,258,273],"MRI":[64,179,211],"database":[65],"huge,":[67],"and":[68,74,109,139,171,215,253,269],"this":[69],"screening":[71],"time-consuming":[73],"fatiguing.":[75],"We":[76],"propose":[77],"automatically":[79],"detect":[80,144],"images":[82],"containing":[83],"motion":[84,117,289],"using":[86,173,206],"Deep":[87,125,251],"Convolutional":[88],"Neural":[89,237],"Networks":[90],"(CNN),":[91],"currently":[92],"presenting":[93],"best":[95],"performance":[96,260],"classification":[99],"contests.":[100],"Four":[101],"renowned":[102],"architectures":[103],"were":[104,150,169,204],"chosen":[105],"be":[107,277],"fine-tuned,":[108],"have":[110],"their":[111],"results":[112,219,246],"combined":[113,224],"report":[115],"presence":[119,291],"acquisition.":[122,230,272],"Besides,":[123],"as":[124,160],"CNN":[126],"filters":[127],"from":[128,156,208,220,247],"lower":[129,157],"layers":[130],"map":[131],"smaller":[133],"regions":[134],"in":[135],"original":[137],"input":[138],"our":[140],"goal":[141],"fine-grained":[145],"corruption,":[147],"CNNs":[149,168,203,252],"adapted":[151,167],"use":[153],"output":[155],"intermediate":[158],"features":[161],"binary":[164],"classifier.":[165],"trained":[170,205,242],"tested":[172],"annotated":[175],"dataset":[176],"composed":[177],"of":[178,199],"T1-weighted":[180],"volumetric":[181],"acquisitions.":[182],"training":[184],"subset":[185,192],"contains":[186],"48":[187],"images,":[188],"while":[189],"testing":[191],"has":[193],"20":[194],"images.":[195],"method":[197],"consists":[198],"two":[200],"steps.":[201],"Firstly":[202],"patches":[207,222,245],"three":[210],"planes":[212],"(sagittal,":[213],"axial":[214],"coronal).":[216],"Secondly,":[217],"are":[223],"provide":[226],"result":[228],"per":[229,267,271],"On":[231],"second":[233],"step,":[234],"Artificial":[236],"Network":[238],"(ANN)":[239],"classifier":[240],"was":[241,265],"combining":[243],"four":[249],"modified":[250],"patch":[255,268],"location":[256],"information.":[257],"overall":[259],"test":[263],"set":[264],"88.27%":[266],"100%":[270],"proposed":[274],"technique":[275],"can":[276],"applied":[278],"large":[280],"datasets,":[281],"providing,":[282],"control":[286],"artifact":[290],"probability,":[292],"minimizing":[293],"time":[295],"spent":[296],"manual":[298],"control.":[300]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}