{"id":"https://openalex.org/W4417534306","doi":"https://doi.org/10.1109/access.2026.3703426","title":"MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer\u2019s Disease Imaging","display_name":"MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer\u2019s Disease Imaging","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4417534306","doi":"https://doi.org/10.1109/access.2026.3703426"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3703426","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3703426","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3703426","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jin Young Kim","orcid":"https://orcid.org/0009-0004-9940-3332"},"institutions":[{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jin Young Kim","raw_affiliation_strings":["Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, USA","Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA"],"raw_orcid":"https://orcid.org/0009-0004-9940-3332","affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, USA","institution_ids":["https://openalex.org/I47251452"]},{"raw_affiliation_string":"Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA","institution_ids":["https://openalex.org/I47251452"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jeremy Hudson","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeremy Hudson","raw_affiliation_strings":["Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jeongchul Kim","orcid":"https://orcid.org/0000-0002-2369-4653"},"institutions":[{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeongchul Kim","raw_affiliation_strings":["Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, USA","Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA"],"raw_orcid":"https://orcid.org/0000-0002-2369-4653","affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, USA","institution_ids":["https://openalex.org/I47251452"]},{"raw_affiliation_string":"Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA","institution_ids":["https://openalex.org/I47251452"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Qing Lyu","orcid":"https://orcid.org/0000-0002-9824-0170"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qing Lyu","raw_affiliation_strings":["Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA"],"raw_orcid":"https://orcid.org/0000-0002-9824-0170","affiliations":[{"raw_affiliation_string":"Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":null,"display_name":"Christopher T. Whitlow","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher T. Whitlow","raw_affiliation_strings":["Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00061465,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"94122","last_page":"94136"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.28999999165534973,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"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/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.28999999165534973,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.2687000036239624,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.04450000077486038,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/positron-emission-tomography","display_name":"Positron emission tomography","score":0.6062999963760376},{"id":"https://openalex.org/keywords/neuroimaging","display_name":"Neuroimaging","score":0.517799973487854},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.48240000009536743},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.447299987077713},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.43869999051094055},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42170000076293945},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.391400009393692}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6273000240325928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6244000196456909},{"id":"https://openalex.org/C2775842073","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Positron emission tomography","level":2,"score":0.6062999963760376},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.517799973487854},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.48240000009536743},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.43869999051094055},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42170000076293945},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.38260000944137573},{"id":"https://openalex.org/C3017724952","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Pet imaging","level":3,"score":0.38109999895095825},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C2989087649","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Image synthesis","level":3,"score":0.32440000772476196},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.310699999332428},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C29331040","wikidata":"https://www.wikidata.org/wiki/Q7239554","display_name":"Preclinical imaging","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2026.3703426","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3703426","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2512.15947","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2512.15947","pdf_url":"https://arxiv.org/pdf/2512.15947","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.15947","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.15947","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":"Preprint"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3703426","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3703426","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"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],"positron":[1],"emission":[2],"tomography":[3],"(PET)":[4],"is":[5,19],"a":[6,196],"critical":[7],"diagnostic":[8],"modality":[9],"for":[10,201,213],"Alzheimer\u2019s":[11],"disease":[12],"(AD),":[13],"but":[14],"its":[15],"widespread":[16],"clinical":[17,27,217],"adoption":[18],"hindered":[20],"by":[21],"radiation":[22],"exposure,":[23],"limited":[24],"availability,":[25],"high":[26],"workload,":[28],"and":[29,72,85,93,103,110,147,170,198,216],"substantial":[30],"financial":[31],"costs.":[32],"To":[33],"address":[34],"these":[35,190],"limitations,":[36],"we":[37,101],"propose":[38],"the":[39,61,81,98,180,208],"Multi-scale":[40],"CBAM":[41],"Residual":[42],"Vector":[43],"Quantized":[44],"Generative":[45],"Adversarial":[46],"Network":[47],"(MCR-VQGAN)":[48],"to":[49],"synthesize":[50],"high-fidelity":[51],"tau":[52,94,139,172,203,211],"PET":[53,95,140,173,204],"images":[54],"from":[55,97],"structural":[56],"T1-weighted":[57,91],"MRI.":[58],"MCR-VQGAN":[59,105,113,194],"advances":[60],"standard":[62],"VQGAN":[63],"architecture":[64],"through":[65],"three":[66],"enhancements:":[67],"multi-scale":[68],"convolutions,":[69],"ResNet":[70],"blocks,":[71],"Convolutional":[73],"Block":[74],"Attention":[75],"Modules":[76],"(CBAM),":[77],"which":[78],"collectively":[79],"improve":[80],"capture":[82],"of":[83,210],"local":[84],"global":[86],"features.":[87],"Using":[88],"222":[89],"paired":[90],"MRI":[92],"scans":[96],"ADNI":[99],"database,":[100],"trained":[102,136],"compared":[104],"against":[106],"cGAN,":[107],"WGAN-GP,":[108],"CycleGAN,":[109],"baseline":[111],"VQGAN.":[112],"achieved":[114,141],"superior":[115],"image":[116],"synthesis":[117],"performance":[118],"across":[119,161],"all":[120],"metrics":[121],"(MSE":[122],"=":[123,126,130,177,187],"0.0056\u00b10.0061,":[124],"PSNR":[125],"30.65\u00b14.47":[127],"dB,":[128],"SSIM":[129],"0.9263\u00b10.0469).":[131],"A":[132],"CNN-based":[133],"AD":[134,214],"classifier":[135],"on":[137,144],"real":[138,145,169],"comparable":[142],"accuracy":[143],"(63.64%)":[146],"synthetic":[148,171],"(65.91%)":[149],"images,":[150],"indicating":[151],"that":[152,193],"diagnostically":[153],"relevant":[154],"features":[155],"are":[156],"preserved.":[157],"Regional":[158],"SUVR":[159],"analysis":[160],"Braak-defined":[162],"ROIs":[163],"further":[164],"confirmed":[165],"strong":[166],"agreement":[167,182],"between":[168],"(Pearsonr=":[174],"0.78\u20130.88;":[175],"ICC":[176],"0.71\u20130.84),":[178],"with":[179],"strongest":[181],"in":[183],"Braak":[184],"V/VI":[185],"(ICC":[186],"0.838).":[188],"Together,":[189],"results":[191],"demonstrate":[192],"offers":[195],"reliable":[197],"scalable":[199],"surrogate":[200],"conventional":[202],"imaging,":[205],"potentially":[206],"improving":[207],"accessibility":[209],"biomarkers":[212],"research":[215],"workflows.":[218]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-12-21T00:00:00"}