{"id":"https://openalex.org/W2885594885","doi":"https://doi.org/10.1109/memea.2018.8438755","title":"Effect Size Comparison for Gaussian and Rician Modelling within fMRI Data","display_name":"Effect Size Comparison for Gaussian and Rician Modelling within fMRI Data","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2885594885","doi":"https://doi.org/10.1109/memea.2018.8438755","mag":"2885594885"},"language":"en","primary_location":{"id":"doi:10.1109/memea.2018.8438755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/memea.2018.8438755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","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/A5090638743","display_name":"Susanne Blotwijk","orcid":"https://orcid.org/0000-0003-3488-8063"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Susanne Blotwijk","raw_affiliation_strings":["Dept. Public Health (GEWE), Vrije Universiteit Brussel Biostatistics and Medical Informatics (BISI), Brussels"],"affiliations":[{"raw_affiliation_string":"Dept. Public Health (GEWE), Vrije Universiteit Brussel Biostatistics and Medical Informatics (BISI), Brussels","institution_ids":["https://openalex.org/I13469542"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019777536","display_name":"Kurt Barb\u00e9","orcid":"https://orcid.org/0000-0002-7825-9077"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Kurt Barbe","raw_affiliation_strings":["Dept. Mathematics (DWIS), Dept. Public Health (GEWE), Vrije Universiteit Brussel, Brussels"],"affiliations":[{"raw_affiliation_string":"Dept. Mathematics (DWIS), Dept. Public Health (GEWE), Vrije Universiteit Brussel, Brussels","institution_ids":["https://openalex.org/I13469542"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090638743"],"corresponding_institution_ids":["https://openalex.org/I13469542"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.11653681,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9998000264167786,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9980999827384949,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/rician-fading","display_name":"Rician fading","score":0.8201624155044556},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7663747668266296},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6796622276306152},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5934965014457703},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5618316531181335},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5237833857536316},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5215006470680237},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40536001324653625},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.37560826539993286},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3464531898498535},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3420036733150482},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30423203110694885},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.20255222916603088},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10237619280815125}],"concepts":[{"id":"https://openalex.org/C60472773","wikidata":"https://www.wikidata.org/wiki/Q7331156","display_name":"Rician fading","level":4,"score":0.8201624155044556},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7663747668266296},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6796622276306152},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5934965014457703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5618316531181335},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5237833857536316},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5215006470680237},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40536001324653625},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37560826539993286},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3464531898498535},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3420036733150482},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30423203110694885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.20255222916603088},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10237619280815125},{"id":"https://openalex.org/C81978471","wikidata":"https://www.wikidata.org/wiki/Q1196572","display_name":"Fading","level":3,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/memea.2018.8438755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/memea.2018.8438755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","raw_type":"proceedings-article"},{"id":"pmh:oai:vubissmart:VUBISSMART:2000:122166","is_oa":false,"landing_page_url":"https://biblio.vub.ac.be/vubir/effect-size-comparison-for-gaussian-and-rician-modelling-within-fmri-data(8f6df28a-5920-443b-bb63-e4091f12c60c).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306402573","display_name":"VUBIR (Vrije Universiteit Brussel)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I13469542","host_organization_name":"Vrije Universiteit Brussel","host_organization_lineage":["https://openalex.org/I13469542"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1555033159","https://openalex.org/W2020915144","https://openalex.org/W2023173554","https://openalex.org/W2056099894","https://openalex.org/W2059784307","https://openalex.org/W2069733091","https://openalex.org/W2074373813","https://openalex.org/W2096545952","https://openalex.org/W2107031757","https://openalex.org/W2151952539","https://openalex.org/W2482374272","https://openalex.org/W2501473864","https://openalex.org/W2797884775","https://openalex.org/W4299627282","https://openalex.org/W7071764571"],"related_works":["https://openalex.org/W2109135198","https://openalex.org/W2173022174","https://openalex.org/W2746366400","https://openalex.org/W2802664802","https://openalex.org/W4251128056","https://openalex.org/W1994910710","https://openalex.org/W2182921069","https://openalex.org/W2183297761","https://openalex.org/W2513564542","https://openalex.org/W1976244879"],"abstract_inverted_index":{"It":[0],"has":[1],"been":[2,35],"argued":[3,87],"that":[4],"due":[5,89],"to":[6,37,53,59,90,102,135],"the":[7,12,28,32,39,48,55,63,70,72,94,106,123,136,142],"bias":[8],"at":[9,114],"low":[10],"SNR,":[11],"Gaussian":[13,137,143],"approach":[14,131,144],"is":[15,51,86,97,125,132,145],"unsuitable":[16],"for":[17,77],"modelling":[18],"Rician":[19,29,111,129],"fMRI":[20,47,120,149],"data.":[21],"As":[22,65],"a":[23,98],"result":[24],"several":[25,109],"estimators":[26,113],"incorporating":[27],"nature":[30],"of":[31,69],"data":[33],"have":[34],"proposed":[36],"measure":[38,54,60],"signal":[40],"as":[41,43,80],"accurately":[42],"possible.":[44],"However,":[45],"within":[46,62,119,148],"main":[49],"objective":[50],"not":[52],"signal,":[56,71],"but":[57],"rather":[58],"changes":[61,104],"signal.":[64],"an":[66],"increasing":[67],"function":[68],"mean":[73],"can":[74],"be":[75],"used":[76],"this":[78,83,128],"purpose":[79],"well.":[81],"In":[82],"paper":[84],"it":[85],"that,":[88],"its":[91,140],"lower":[92],"variance,":[93],"sample":[95],"average":[96],"more":[99],"suitable":[100],"tool":[101],"detect":[103],"in":[105],"amplitude":[107],"than":[108],"conventional":[110],"parameter":[112],"those":[115],"SNR":[116],"values":[117],"common":[118],"measurements.":[121],"While":[122],"interpretation":[124],"slightly":[126],"different,":[127],"mean-based":[130],"essentially":[133],"equivalent":[134],"approach.":[138],"Despite":[139],"bias,":[141],"therefore":[146],"preferable":[147],"analysis.":[150]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
