{"id":"https://openalex.org/W1902803334","doi":"https://doi.org/10.1109/isbi.2002.1029370","title":"Movement correction of fMRI time-series using intrinsic statistical properties of images: an independent component analysis approach","display_name":"Movement correction of fMRI time-series using intrinsic statistical properties of images: an independent component analysis approach","publication_year":2003,"publication_date":"2003-06-25","ids":{"openalex":"https://openalex.org/W1902803334","doi":"https://doi.org/10.1109/isbi.2002.1029370","mag":"1902803334"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2002.1029370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2002.1029370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings IEEE International Symposium on Biomedical Imaging","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/A5057068227","display_name":"Rui Liao","orcid":"https://orcid.org/0000-0002-0057-2792"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]},{"id":"https://openalex.org/I59028336","display_name":"Pratt Institute","ror":"https://ror.org/007m3p006","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I59028336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R. Liao","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, USA","institution_ids":["https://openalex.org/I59028336","https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111692284","display_name":"Jeffrey Krolik","orcid":null},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]},{"id":"https://openalex.org/I59028336","display_name":"Pratt Institute","ror":"https://ror.org/007m3p006","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I59028336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Krolik","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, USA","institution_ids":["https://openalex.org/I59028336","https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021255536","display_name":"Martin J. McKeown","orcid":"https://orcid.org/0000-0002-4048-0817"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M.J. McKeown","raw_affiliation_strings":["Brain Imaging and Analysis Center, Department Of Medicine (Neurology), Duke University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brain Imaging and Analysis Center, Department Of Medicine (Neurology), Duke University, USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"765","last_page":"768"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10581","display_name":"Neural dynamics and brain function","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.6541358232498169},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6024268865585327},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5773990154266357},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5691105127334595},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5400870442390442},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5199764370918274},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.49811363220214844},{"id":"https://openalex.org/keywords/joint-entropy","display_name":"Joint entropy","score":0.4249507188796997},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4223959743976593},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4124111533164978},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35674798488616943},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33160531520843506},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.2606886029243469},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20235344767570496},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08816012740135193}],"concepts":[{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.6541358232498169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6024268865585327},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5773990154266357},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5691105127334595},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5400870442390442},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5199764370918274},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.49811363220214844},{"id":"https://openalex.org/C106752470","wikidata":"https://www.wikidata.org/wiki/Q1364826","display_name":"Joint entropy","level":3,"score":0.4249507188796997},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4223959743976593},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4124111533164978},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35674798488616943},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33160531520843506},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.2606886029243469},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20235344767570496},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08816012740135193},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2002.1029370","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2002.1029370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings IEEE International Symposium on Biomedical Imaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6299999952316284,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W50383775","https://openalex.org/W2016444985","https://openalex.org/W2108384452","https://openalex.org/W2119052282","https://openalex.org/W2123649031","https://openalex.org/W2124757684","https://openalex.org/W2129002068","https://openalex.org/W2141224535","https://openalex.org/W6602031078"],"related_works":["https://openalex.org/W3212114011","https://openalex.org/W2036846997","https://openalex.org/W2090882960","https://openalex.org/W2029679244","https://openalex.org/W3149233489","https://openalex.org/W3012976800","https://openalex.org/W2018879631","https://openalex.org/W4362588372","https://openalex.org/W2911322731","https://openalex.org/W2071645312"],"abstract_inverted_index":{"A":[0],"3D":[1],"image":[2,116,125],"registration":[3],"method":[4,131],"for":[5,171],"alignment":[6,121],"of":[7,25,32,44,53,57,87,102],"functional":[8],"magnetic":[9],"resonance":[10],"imaging":[11],"(fMRI)":[12],"time-series,":[13],"based":[14],"on":[15],"independent":[16,59],"component":[17],"analysis":[18],"(ICA),":[19],"is":[20,126,159],"described.":[21],"Movement":[22],"during":[23],"acquisition":[24],"an":[26,38,172],"fMRI":[27,136],"time-series":[28],"corrupts":[29],"the":[30,33,41,45,51,58,73,77,81,84,88,114,149,169],"statistics":[31],"acquired":[34],"data,":[35],"resulting":[36],"in":[37,40,50],"increase":[39],"joint":[42],"entropy":[43,52,78,107],"data":[46,74,82],"and":[47,83,134,163],"a":[48,54,123],"decrease":[49],"nonlinear":[55,85],"function":[56,86],"components":[60,91],"calculated":[61,89],"by":[62,70,112],"ICA.":[63],"Motion":[64],"effects":[65],"can":[66],"therefore":[67],"be":[68],"mitigated":[69],"spatially":[71,103],"adjusting":[72],"to":[75,117,132],"maximize":[76],"difference":[79],"between":[80],"ICA":[90],"without":[92,168],"explicitly":[93],"estimating":[94],"motion":[95,166],"parameters.":[96],"By":[97],"determining":[98],"which":[99],"linear":[100],"combination":[101],"transformed":[104],"images":[105],"maximizes":[106],"difference,":[108],"interpolation":[109,146],"error":[110],"incurred":[111],"resampling":[113],"misaligned":[115],"bring":[118],"it":[119],"into":[120],"with":[122],"reference":[124,174],"minimized.":[127],"We":[128,153],"applied":[129],"this":[130,156],"synthetic":[133],"real":[135],"data.":[137],"The":[138],"proposed":[139],"results":[140],"were":[141],"more":[142],"accurate":[143],"than":[144],"cubic":[145],"even":[147],"when":[148],"displacement":[150],"was":[151],"known.":[152],"conclude":[154],"that":[155],"initial":[157],"approach":[158],"completely":[160],"automatic,":[161],"noniterative":[162],"provides":[164],"nonrigid-body":[165],"correction,":[167],"need":[170],"explicitly-defined":[173],"volume.":[175]},"counts_by_year":[{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
