{"id":"https://openalex.org/W7154746538","doi":"https://doi.org/10.48550/arxiv.2604.14800","title":"Generative Modeling of Complex-Valued Brain MRI Data","display_name":"Generative Modeling of Complex-Valued Brain MRI Data","publication_year":2026,"publication_date":"2026-04-16","ids":{"openalex":"https://openalex.org/W7154746538","doi":"https://doi.org/10.48550/arxiv.2604.14800"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.14800","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14800","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.14800","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123904391","display_name":"Marco Schlimbach","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schlimbach, Marco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133845161","display_name":"Moritz Rempe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rempe, Moritz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133870272","display_name":"Jessica Mnischek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mnischek, Jessica","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123973244","display_name":"Lukas T. Rotkopf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rotkopf, Lukas T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133831601","display_name":"Jens Weingarten","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weingarten, Jens","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133851275","display_name":"Jens Kleesiek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kleesiek, Jens","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123924412","display_name":"Kevin Kr\u00f6ninger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kr\u00f6ninger, Kevin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.5776000022888184,"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":0.5776000022888184,"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":0.14390000700950623,"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":0.10589999705553055,"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/autoencoder","display_name":"Autoencoder","score":0.6498000025749207},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5400999784469604},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5339999794960022},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5304999947547913},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5199999809265137},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.461899995803833},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.4025999903678894},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.3596000075340271}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6991000175476074},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6498000025749207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6215000152587891},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5400999784469604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5304999947547913},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5199999809265137},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.461899995803833},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3686999976634979},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27889999747276306},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.26570001244544983},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.25780001282691956},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.14800","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14800","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.14800","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14800","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Objective.":[0],"Standard":[1],"Magnetic":[2],"Resonance":[3],"Imaging":[4],"(MRI)":[5],"reconstruction":[6],"pipelines":[7],"discard":[8],"phase":[9,58,85,118,200,238],"information":[10,59,201,225],"captured":[11],"during":[12],"acquisition,":[13],"despite":[14],"evidence":[15],"that":[16],"it":[17,214],"encodes":[18],"tissue":[19,111],"properties":[20],"relevant":[21],"to":[22,45],"tumor":[23],"diagnosis.":[24],"Current":[25],"machine":[26],"learning":[27],"approaches":[28],"inherit":[29],"this":[30,42],"limitation":[31],"by":[32,102],"operating":[33],"exclusively":[34],"on":[35,106,152,166,175],"reconstructed":[36],"magnitude":[37,56,198,236],"images.":[38],"The":[39,65,115,189],"aim":[40],"of":[41,53,60,158,164,195,221,234],"study":[43],"is":[44,51,95],"build":[46],"a":[47,70,88,98,167,182,216],"generative":[48,67,90],"framework":[49,68,191],"which":[50,74],"capable":[52],"jointly":[54,196,239],"modeling":[55,197],"and":[57,101,130,199,204,230,237],"complex-valued":[61,76,206],"MRI":[62,77,208,224],"scans.":[63],"Approach.":[64],"proposed":[66,190],"combines":[69],"conditional":[71],"variational":[72],"autoencoder,":[73],"compresses":[75],"scans":[78],"into":[79],"compact":[80],"latent":[81],"representations":[82],"while":[83],"preserving":[84],"coherence,":[86],"with":[87,185],"flow-matching-based":[89],"model.":[91],"Synthetic":[92],"sample":[93],"quality":[94],"assessed":[96],"via":[97],"real-versus-synthetic":[99],"classifier":[100],"training":[103],"downstream":[104,146],"classifiers":[105,149],"synthetic":[107,153,211],"data":[108,154,212],"for":[109,202,218],"abnormal":[110,205],"detection.":[112],"Main":[113],"results.":[114],"autoencoder":[116],"preserves":[117],"coherence":[119],"above":[120],"0.997.":[121],"Real-versus-synthetic":[122],"classification":[123],"yields":[124],"low":[125],"AUROC":[126,157],"values":[127],"between":[128],"0.50":[129],"0.66":[131],"across":[132],"all":[133],"acquisition":[134],"sequences,":[135],"indicating":[136],"generated":[137],"samples":[138],"are":[139],"nearly":[140],"indistinguishable":[141],"from":[142,181],"real":[143],"data.":[144,209],"In":[145],"normal-versus-abnormal":[147],"classification,":[148],"trained":[150],"entirely":[151],"achieve":[155],"an":[156,176],"0.880,":[159],"surpassing":[160],"the":[161,193,219],"real-data":[162],"baseline":[163],"0.842":[165],"publicly":[168],"available":[169],"dataset":[170],"(fastMRI).":[171],"This":[172],"advantage":[173],"persists":[174],"independent":[177],"external":[178],"test":[179],"set":[180],"different":[183],"institution":[184],"biopsy-confirmed":[186],"labels.":[187],"Significance.":[188],"demonstrates":[192],"feasibility":[194],"normal":[203],"brain":[207,223],"Beyond":[210],"generation,":[213],"establishes":[215],"foundation":[217],"usage":[220],"complete":[222],"in":[226],"future":[227],"diagnostic":[228],"applications":[229],"enables":[231],"systematic":[232],"investigation":[233],"how":[235],"encode":[240],"pathology-specific":[241],"features.":[242]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-18T00:00:00"}