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Conventional CT\u2010based AC provides reliable attenuation (\u03bc\u2010) maps but adds radiation, introduces PET\/CT misalignment artifacts, and is unavailable on stand\u2010alone PET systems. Existing deep learning (DL) methods use non\u2010attenuation corrected (NAC) PET data to predict CT\u2010like or directly AC\u2010PET images, but their dependence on emission characteristics limits generalizability across tracers and anatomical coverage, especially for long axial field\u2010of\u2010view PET.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We propose incorporating tissue\u2010density information from dual\u2010view scout radiographs as supplementary input to NAC PET, acquired with a clinically significant radiation reduction relative to standard low\u2010dose CT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Two DL methods were evaluated for generating transmission (Tr) images as surrogate \u03bc\u2010maps for PET AC: (1) (NAC)\u2010to\u2010(Tr), using coronal and sagittal NAC PET slices, and (2) (NAC\u00a0+ Scout)\u2010to\u2010(Tr), combining NAC PET with anterior\u2010posterior and lateral scout views. 53 research scans across four tracers from the PennPET Explorer were used for training and testing, with three additional tracers included for testing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>(NAC\u00a0+\u00a0Scout)\u2010to\u2010(Tr) improved quantitative accuracy, reducing NRMSE% to\u00a0&lt;\u00a010% (versus up to 18% for (NAC)\u2010to\u2010(Tr)) and SUV biases to within \u201010%, with significant reductions in brain, liver, and muscle compared to (NAC)\u2010to\u2010(Tr). Out\u2010of\u2010distribution evaluation confirmed generalizability, maintaining SUV biases below 5%\u20108% versus 10%\u201013% for (NAC)\u2010to\u2010(Tr). In a longitudinal biodistribution study, the scout\u2010guided model showed consistent performance across repeat scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>By leveraging routinely acquired scout scans, this approach enables accurate quantitative PET reconstruction without a full CT, substantially reducing radiation dose and misalignment artifacts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70647","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T05:16:43Z","timestamp":1787116603000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual\u2010view scout scans with deep learning for ultra\u2010low dose attenuation correction in PET"],"prefix":"10.1002","volume":"53","author":[{"given":"Florence 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