{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T03:06:25Z","timestamp":1781319985405,"version":"3.54.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T00:00:00Z","timestamp":1625702400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T00:00:00Z","timestamp":1625702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Beijing Children\u2019s Hospital Young Investigator Program","award":["BCH-YIPB-2016-06"],"award-info":[{"award-number":["BCH-YIPB-2016-06"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>To evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625\u00a0mm thin-slice images.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Low-dose axial head CT scans of 50 children with 120\u00a0kV, 0.8\u00a0s rotation and age-dependent 150\u2013220\u00a0mA tube current were selected. Images were reconstructed at 5\u00a0mm and 0.625\u00a0mm slice thickness using Filtered back projection (FBP), Adaptive statistical iterative reconstruction-v at 50% strength (50%ASIR-V) (as reference standard), 100%ASIR-V and DLIR-high (DL-H). The CT attenuation and standard deviation values of the gray and white matters in the basal ganglia were measured. The clarity of sulci\/cisterns, boundary between white and gray matters, and overall image quality was subjectively evaluated. The number of lesions in each reconstruction group was counted.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The 5\u00a0mm FBP, 50%ASIR-V, 100%ASIR-V and DL-H images had a subjective score of 2.25\u2009\u00b1\u20090.44, 3.05\u2009\u00b1\u20090.23, 2.87\u2009\u00b1\u20090.39 and 3.64\u2009\u00b1\u20090.49 in a 5-point scale, respectively with DL-H having the lowest image noise of white matter at 2.00\u2009\u00b1\u20090.34 HU; For the 0.625\u00a0mm images, only DL-H images met the diagnostic requirement. The 0.625\u00a0mm DL-H images had similar image noise (3.11\u2009\u00b1\u20090.58 HU) of the white matter and overall image quality score (3.04\u2009\u00b1\u20090.33) as the 5\u00a0mm 50% ASIR-V images (3.16\u2009\u00b1\u20090.60 HU and 3.05\u2009\u00b1\u20090.23). Sixty-five lesions were recognized in 5\u00a0mm 50%ASIR-V images and 69 were detected in 0.625\u00a0mm DL-H images.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>DL-H improves the head CT image quality for children compared with ASIR-V images. The 0.625\u00a0mm DL-H images improve lesion detection and produce similar image noise as the 5\u00a0mm 50%ASIR-V images, indicating a potential 85% dose reduction if current image quality and slice thickness are desired.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-021-00637-w","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T16:03:27Z","timestamp":1625760207000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection"],"prefix":"10.1186","volume":"21","author":[{"given":"Jihang","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianying","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuofu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8213-9716","authenticated-orcid":false,"given":"Yun","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"issue":"3","key":"637_CR1","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1053\/j.ro.2016.05.001","volume":"51","author":"KR Fink","year":"2016","unstructured":"Fink KR. 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