{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T09:38:36Z","timestamp":1787650716185,"version":"build-2736575974"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Tohoku Branch Research Grant from the Japan Society of Radiological Technology","award":["Grant No. 2301"],"award-info":[{"award-number":["Grant No. 2301"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Computed tomography (CT) is a widely used diagnostic tool, but variations in patient anatomy make accurate radiation dose estimation challenging. While size-specific dose estimates (SSDE) can serve as a more effective tool for dose prediction than conventional indices like CTDI\n                    <jats:sub>vol<\/jats:sub>\n                    and DLP, calculating the water-equivalent diameter (D\n                    <jats:sub>w<\/jats:sub>\n                    ) for each slice is time-consuming and often impractical. To address this, we propose a novel method, the SSDE slice-averaged image (SSDE\n                    <jats:sub>SAI<\/jats:sub>\n                    ), which calculates D\n                    <jats:sub>w<\/jats:sub>\n                    from a single image generated by averaging CT values across all slices. This approach captures anatomical variability while reducing calculation effort. We retrospectively analyzed CT data from 282 adult patients in three scan regions: chest, abdomen\u2013pelvis, and chest\u2013abdomen\u2013pelvis (CAP). SSDE\n                    <jats:sub>SAI<\/jats:sub>\n                    was compared with SSDE\n                    <jats:sub>center<\/jats:sub>\n                    and the reference mean SSDE using regression analysis and root mean square error (RMSE). SSDE\n                    <jats:sub>SAI<\/jats:sub>\n                    showed stronger agreement with mean SSDE than SSDE\n                    <jats:sub>center<\/jats:sub>\n                    across all regions, achieving R\n                    <jats:sup>2<\/jats:sup>\n                    values up to 0.991 and lower RMSE. These results suggest that SSDE\n                    <jats:sub>SAI<\/jats:sub>\n                    is a more advanced approach for dose prediction and may serve as a practical alternative for routine clinical use.\n                  <\/jats:p>","DOI":"10.1038\/s41598-025-27035-4","type":"journal-article","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:32:58Z","timestamp":1764588778000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A practical slice averaged image method for precise CT size specific dose estimates"],"prefix":"10.1038","volume":"15","author":[{"given":"Yutaka","family":"Dendo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keisuke","family":"Abe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Onodera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shingo","family":"Kayano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hideki","family":"Ota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kei","family":"Takase","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,1]]},"reference":[{"issue":"2 Suppl","key":"27035_CR1","doi-asserted-by":"publisher","first-page":"S45","DOI":"10.1148\/radiol.14141356","volume":"273","author":"GD Rubin","year":"2014","unstructured":"Rubin, G. D. Computed tomography: Revolutionizing the practice of medicine for 40 years. Radiology 273(2 Suppl), S45\u2013S74 (2014).","journal-title":"Radiology"},{"key":"27035_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s00247-017-3991-x","volume":"48","author":"CK Rigsby","year":"2018","unstructured":"Rigsby, C. K. et al. Radiation dose management for pediatric cardiac computed tomography: A report from the Image Gently \u2018Have-A-Heart\u2019 campaign. Pediatr. Radiol. 48, 5\u201320 (2018).","journal-title":"Pediatr. Radiol."},{"key":"27035_CR3","unstructured":"American Association of Physicists in Medicine (AAPM). AAPM Report No. 246. Estimating Patient Organ Dose with Computed Tomography: A Review of Present Methodology and Required DICOM Information (College Park, MD, 2021): AAPM."},{"key":"27035_CR4","unstructured":"American Association of Physicists in Medicine (AAPM) Task Group 204. 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