{"id":"https://openalex.org/W7165033894","doi":"https://doi.org/10.48550/arxiv.2606.17420","title":"Feynman Kac Reweighted Schr\u00f6dinger Bridge Matching for Surface-Based Tau PET Harmonization","display_name":"Feynman Kac Reweighted Schr\u00f6dinger Bridge Matching for Surface-Based Tau PET Harmonization","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165033894","doi":"https://doi.org/10.48550/arxiv.2606.17420"},"language":"en","primary_location":{"id":"pmh:oai:pubmedcentral.nih.gov:13308448","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13308448/","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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/PMC13308448/","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138825090","display_name":"Jianwei Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jianwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030043177","display_name":"Xinyu Nie","orcid":"https://orcid.org/0009-0006-0969-9411"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nie, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138762539","display_name":"Jiaxin Yue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue, Jiaxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100902783","display_name":"Y Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Yonggang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.640500009059906,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.640500009059906,"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":0.18240000307559967,"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":0.065700002014637,"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/solver","display_name":"Solver","score":0.5921000242233276},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5702000260353088},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5133000016212463},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.47850000858306885},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4523000121116638},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3790999948978424},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.3662000000476837},{"id":"https://openalex.org/keywords/sign","display_name":"Sign (mathematics)","score":0.35899999737739563},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3521000146865845}],"concepts":[{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.5921000242233276},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5702000260353088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5253000259399414},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5133000016212463},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.47850000858306885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45750001072883606},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4523000121116638},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3865000009536743},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3434000015258789},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.319599986076355},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C168773036","wikidata":"https://www.wikidata.org/wiki/Q264164","display_name":"Recursion (computer science)","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.296099990606308},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.25600001215934753},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:pubmedcentral.nih.gov:13308448","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13308448/","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ArXiv","raw_type":"Text"},{"id":"doi:10.48550/arxiv.2606.17420","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17420","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:13308448","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13308448/","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ArXiv","raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Tau":[0,177],"positron":[1],"emission":[2],"tomography":[3],"(PET)":[4],"is":[5,43,232],"widely":[6],"used":[7,121],"for":[8,79,99,161],"the":[9,23,53,80,118,182,207],"in":[10,18,38,77,122,214],"vivo":[11],"characterization":[12],"of":[13,25,47,56,82,227],"disease":[14,20],"stage":[15],"and":[16,142,185,202,219],"progression":[17],"Alzheimer's":[19],"(AD).":[21],"With":[22],"adoption":[24],"multiple":[26],"tau":[27,57,71,83,101,228],"PET":[28,58,72,102,178],"tracers":[29,188],"including":[30,197],"AV-1451,":[31],"PI-2620,":[32],"MK-6240":[33],"with":[34,138,181],"different":[35],"binding":[36],"behaviors":[37],"various":[39],"large-scale":[40,191],"studies,":[41],"there":[42],"a":[44,96,106,146,157],"great":[45],"need":[46],"effective":[48],"harmonization":[49,163],"methods":[50,62,196],"to":[51,68,194],"enable":[52],"cross-tracer":[54,100],"integration":[55],"datasets.":[59,192],"While":[60],"previous":[61,195],"such":[63],"as":[64],"CenTauR":[65],"were":[66],"proposed":[67,208],"standardize":[69],"scalar":[70],"measures,":[73],"they":[74],"are":[75],"limited":[76],"accounting":[78],"heterogeneity":[81],"pathology.":[84,229],"In":[85,168],"this":[86],"work,":[87],"we":[88,171],"propose":[89],"Feynman-Kac":[90,147],"Reweighted":[91],"Schr\u00f6dinger":[92,114,204],"Bridge":[93,115,205],"Matching":[94],"(FKRSBM),":[95],"surface-based":[97],"framework":[98],"harmonization.":[103],"FKRSBM":[104,130,155,209],"learns":[105],"direct":[107],"stochastic":[108],"transport":[109],"between":[110],"tracer":[111],"domains":[112],"using":[113],"matching,":[116],"avoiding":[117],"Gaussian-prior":[119],"routing":[120],"diffusion-based":[123],"translation.":[124],"To":[125,151],"promote":[126],"biologically":[127],"consistent":[128],"transport,":[129],"introduces":[131],"an":[132],"endpoint":[133,149],"penalty":[134],"favoring":[135],"bridge":[136],"pairings":[137],"matched":[139],"tau-pathology":[140],"status":[141],"implements":[143],"it":[144],"through":[145],"reweighted":[148],"proposal.":[150],"preserve":[152],"cortical":[153,165,225],"organization,":[154],"uses":[156],"spherical":[158],"convolutional":[159],"network":[160],"vertex-level":[162],"on":[164],"surface":[166],"meshes.":[167],"our":[169,173],"experiments,":[170],"demonstrate":[172],"method":[174,210],"by":[175],"harmonizing":[176],"images":[179],"acquired":[180],"AV-1451":[183],"(n=1480)":[184],"PI-2620":[186],"(n=2458)":[187],"from":[189],"two":[190],"Compared":[193],"ComBat,":[198],"CycleGAN,":[199],"Diffusion":[200],"Model(DF),":[201],"unregularized":[203],"Model(DSBM),":[206],"outperforms":[211],"these":[212],"baselines":[213],"subgroup-level":[215],"alignment,":[216],"tau-positivity":[217],"consistency,":[218],"diagnostic":[220],"classification":[221],"while":[222],"preserving":[223],"subject-specific":[224],"topography":[226],"The":[230],"code":[231],"available":[233],"at:":[234],"https://github.com/jianweizhang17/FKRSBM.":[235]},"counts_by_year":[],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2026-06-18T00:00:00"}