{"id":"https://openalex.org/W7154335686","doi":"https://doi.org/10.48550/arxiv.2604.11762","title":"MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI","display_name":"MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154335686","doi":"https://doi.org/10.48550/arxiv.2604.11762"},"language":"en","primary_location":{"id":"pmh:oai:pubmedcentral.nih.gov:13105226","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13105226/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ArXiv","raw_type":"Text"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13105226/","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015154851","display_name":"Paula Arguello","orcid":"https://orcid.org/0000-0001-8266-8802"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Arguello, Paula","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023626233","display_name":"Berk T\u0131naz","orcid":"https://orcid.org/0000-0002-5498-5824"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tinaz, Berk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102636468","display_name":"Mohammad Shahab Sepehri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sepehri, Mohammad Shahab","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016826383","display_name":"Maryam Soltanolkotabi","orcid":"https://orcid.org/0000-0002-4194-1910"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soltanolkotabi, Maryam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133591840","display_name":"Mahdi Soltanolkotabi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soltanolkotabi, Mahdi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5015154851"],"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/T14510","display_name":"Medical Imaging and Analysis","score":0.35089999437332153,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.35089999437332153,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10562","display_name":"Total Knee Arthroplasty Outcomes","score":0.11949999630451202,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.05649999901652336,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/robustness","display_name":"Robustness (evolution)","score":0.652400016784668},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.5504999756813049},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5198000073432922},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4417000114917755},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4392000138759613},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41940000653266907},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4083000123500824},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.3723999857902527}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.652400016784668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6427000164985657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6218000054359436},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.5504999756813049},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5426999926567078},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5198000073432922},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4417000114917755},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4392000138759613},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41940000653266907},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3720000088214874},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.36239999532699585},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3450999855995178},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3122999966144562},{"id":"https://openalex.org/C91682802","wikidata":"https://www.wikidata.org/wiki/Q620538","display_name":"Multidimensional scaling","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.257999986410141}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:pubmedcentral.nih.gov:13105226","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13105226/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ArXiv","raw_type":"Text"},{"id":"doi:10.48550/arxiv.2604.11762","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11762","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":"pmh:oai:pubmedcentral.nih.gov:13105226","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13105226/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ArXiv","raw_type":"Text"},"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":{"Deep":[0],"learning":[1],"underpins":[2],"a":[3,39,61,131,139],"wide":[4],"range":[5],"of":[6,43,46,66,125,142,174,180,210],"applications":[7],"in":[8,103,168],"MRI,":[9],"including":[10],"reconstruction,":[11],"artifact":[12],"removal,":[13],"and":[14,28,36,63,77,95,121,123,154,177,188,199,215,223,237],"segmentation.":[15],"However,":[16],"progress":[17],"has":[18],"been":[19],"driven":[20],"largely":[21],"by":[22,191],"public":[23],"datasets":[24],"focused":[25],"on":[26,160,194,202,231],"brain":[27],"knee":[29],"imaging,":[30],"shaping":[31],"how":[32],"models":[33,48,158,167,193],"are":[34],"trained":[35,159],"evaluated.":[37],"As":[38],"result,":[40],"careful":[41],"studies":[42],"the":[44,83,161,172,178],"reliability":[45],"these":[47],"across":[49],"diverse":[50,64],"anatomical":[51,175],"settings":[52],"remain":[53],"limited.":[54],"In":[55],"this":[56],"work,":[57],"we":[58,137,207],"introduce":[59],"MosaicMRI,":[60],"large":[62],"collection":[65],"fully":[67],"sampled":[68],"raw":[69,86],"musculoskeletal":[70],"(MSK)":[71],"MR":[72],"measurements":[73],"designed":[74],"for":[75,133],"training":[76,192,233],"evaluating":[78],"machine-learning-based":[79],"methods.":[80],"MosaicMRI":[81],"is":[82],"largest":[84],"open-source":[85],"MSK":[87],"MRI":[88],"dataset":[89,99,155],"to":[90,144,150],"date,":[91],"comprising":[92],"2,671":[93],"volumes":[94],"80,156":[96],"slices.":[97],"The":[98],"offers":[100],"substantial":[101],"diversity":[102,176],"volume":[104],"orientation":[105],"(e.g.,":[106,111,116,197,204,213],"axial,":[107],"sagittal),":[108],"imaging":[109],"contrasts":[110],"PD,":[112],"T1,":[113],"T2),":[114],"anatomies":[115,163],"spine,":[117],"knee,":[118],"hip,":[119],"ankle,":[120],"others),":[122],"numbers":[124],"acquisition":[126],"coils.":[127],"Using":[128],"VarNet":[129],"as":[130],"baseline":[132],"accelerated":[134],"reconstruction":[135],"task,":[136],"perform":[138],"comprehensive":[140],"set":[141,234],"experiments":[143],"study":[145],"scaling":[146],"behavior":[147],"with":[148,220],"respect":[149],"both":[151,232],"model":[152],"capacity":[153],"size.":[156],"Interestingly,":[157],"combined":[162],"significantly":[164],"outperform":[165],"anatomy-specific":[166],"low-sample":[169],"regimes,":[170],"highlighting":[171],"benefits":[173],"presence":[179],"exploitable":[181],"cross-anatomical":[182],"correlations.":[183],"We":[184],"further":[185],"evaluate":[186],"robustness":[187],"cross-anatomy":[189],"generalization":[190],"one":[195],"anatomy":[196],"spine)":[198],"testing":[200],"them":[201],"another":[203],"knee).":[205],"Notably,":[206],"identify":[208],"groups":[209],"body":[211],"parts":[212],"foot":[214],"elbow)":[216],"that":[217,225],"generalize":[218],"well":[219],"each":[221],"other,":[222],"highlight":[224],"performance":[226],"under":[227],"domain":[228],"shifts":[229],"depends":[230],"size,":[235],"anatomy,":[236],"protocol-specific":[238],"factors.":[239]},"counts_by_year":[],"updated_date":"2026-04-27T08:22:11.395708","created_date":"2026-04-15T00:00:00"}
