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Med. Biol."],"published-print":{"date-parts":[[2025,12,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    This study proposes Scout-Dose-TCM for direct, prospective estimation of organ-level and effective doses under tube current modulation (TCM) and compares its performance with two established methods.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Contrast-enhanced chest-abdomen-pelvis CT exams from 130 adults (120 kVp, TCM) were analyzed. Reference organ doses for six organs (lungs, kidneys, liver, pancreas, bladder, spleen) were calculated using Monte Carlo (MC)-GPU and TotalSegmentator. These data trained Scout-Dose-TCM, a deep-learning model that predicts organ-level doses corresponding to discrete cosine transform (DCT) basis functions, enabling real-time estimation for any TCM profile. The model includes a feature-learning module that extracts contextual information from lateral and frontal scouts and scan range, and a dose-learning module that outputs DCT-based dose estimates. The loss function incorporated the DCT formulation, ensuring accurate predictions across variable TCM patterns. For comparison, dose estimation was performed following AAPM Task Group 204 (Global CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    ) and its TCM-adapted and organ-specific version (Organ CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    ). The three evaluated methods were extended to estimate effective dose and compared against the approach in AAPM Report 96, which applies a fixed dose-length product-to-effective-dose conversion factor. Five-fold cross-validation assessed generalizability via mean absolute percentage dose errors and\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    correlations with MC benchmarks.\n                    <jats:italic>Main Results.<\/jats:italic>\n                    Mean organ-level absolute percentage errors were 13% (Global CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    ), 9% (Organ CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    ), and 7% (Scout-Dose-TCM). The largest discrepancies occurred for the bladder (15%, 13%, and 9%). Scout-Dose-TCM significantly reduced organ-level dose errors versus Global CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    (\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 1 \u00d7 10\n                    <jats:sup>\u22127<\/jats:sup>\n                    ) and improved predictions for liver, bladder, and pancreas versus Organ CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    (\n                    <jats:italic>p<\/jats:italic>\n                    \u2a7d 0.005). It also achieved higher R\n                    <jats:sup>2<\/jats:sup>\n                    values and lower effective-dose error (3.01%) than Global (3.87%) and Organ (4.16%) CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    , and the AAPM Task Group 96 method (31%).\n                    <jats:italic>Significance.<\/jats:italic>\n                    Scout-Dose-TCM outperformed Global CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    and was comparable to or better than Organ CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    for organ and effective dose estimation without requiring segmentations at inference, demonstrating its clinical potential for prospective dose estimation in CT.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae2181","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T22:54:34Z","timestamp":1763592874000},"page":"245011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Scout-Dose-TCM: direct and prospective scout-based estimation of personalized organ and effective doses from tube current modulated CT exams"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5125-0745","authenticated-orcid":true,"given":"Maria Jose","family":"Medrano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6948-3264","authenticated-orcid":true,"given":"Sen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liyan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah-Al-Zubaer","family":"Imran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5053-0195","authenticated-orcid":true,"given":"Jennie","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1380-9238","authenticated-orcid":true,"given":"Grant M","family":"Stevens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2408-3763","authenticated-orcid":true,"given":"Justin R","family":"Tse","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9234-1264","authenticated-orcid":true,"given":"Adam S","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"pmbae2181bib1","doi-asserted-by":"publisher","first-page":"2448","DOI":"10.1002\/mp.14835","type":"journal-article","article-title":"Three-dimensional image volumes from two-dimensional digitally reconstructed radiographs: a deep learning approach in lower limb CT scans","volume":"48","author":"Almeida","year":"2021","journal-title":"Med. 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