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We investigated participant features predicting data exclusion in magnetic resonance (MR)\u2010dependent analyses and evaluated an existing MR\u2010free PET pipeline to quantify these missing data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>Imaging, clinical, cognitive, and sociodemographic data were analyzed for 2119 individuals in a multi\u2010site cohort. Agreement between MR\u2010dependent and MR\u2010free Centiloids (CL) assessed using intra\u2010class correlations and features predicting data exclusion were examined using logistic regressions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      MR\u2010free and MR\u2010dependent CLs generally agreed, but MR\u2010free CLs underestimated MR\u2010dependent cross\u2010sectionally and longitudinally. Approximately 19.5% (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0405) of our cohort would have been excluded in MR\u2010dependent analyses. Age and cerebrovascular comorbidities were consistent exclusion features across multiple sites.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>Data exclusion in imaging studies is not entirely random. Flexible quantification methods like MR\u2010free PET could supplement traditional methods to improve generalizability in large, multi\u2010site studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dad2.70389","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T03:11:14Z","timestamp":1782097874000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Features predicting data exclusion in imaging studies of Alzheimer's disease"],"prefix":"10.1002","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2726-8704","authenticated-orcid":false,"given":"Shaney","family":"Flores","sequence":"first","affiliation":[{"name":"Mallinckrodt Institute of Radiology Washington University in St. Louis School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jalen","family":"Scott","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology Washington University in St. Louis School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruijin","family":"Lu","sequence":"additional","affiliation":[{"name":"Charles F. and Joanne Knight Alzheimer's Disease Research Center Washington University in St. Louis School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Su","sequence":"additional","affiliation":[{"name":"Banner Alzheimer's Institute  Phoenix Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah J.","family":"Keefe","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology Washington University in St. Louis School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T. 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