{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T22:09:47Z","timestamp":1780438187085,"version":"3.54.1"},"reference-count":39,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T00:00:00Z","timestamp":1721520000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Yangfan Project of Science and Technology Commission of Shanghai Municipality","award":["22YF1442400"],"award-info":[{"award-number":["22YF1442400"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271934"],"award-info":[{"award-number":["82271934"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82302183"],"award-info":[{"award-number":["82302183"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Laboratory Open Fund of Key Technology and Materials in Minimally Invasive Spine Surgery","award":["2024JZWC-YBA07"],"award-info":[{"award-number":["2024JZWC-YBA07"]}]},{"name":"Laboratory Open Fund of Key Technology and Materials in Minimally Invasive Spine Surgery","award":["2024JZWC-ZDA03"],"award-info":[{"award-number":["2024JZWC-ZDA03"]}]},{"name":"Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine","award":["TRGG202101"],"award-info":[{"award-number":["TRGG202101"]}]},{"name":"Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine","award":["TRKYRC-XX202204"],"award-info":[{"award-number":["TRKYRC-XX202204"]}]},{"name":"Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine","award":["TRYJ2021JC06"],"award-info":[{"award-number":["TRYJ2021JC06"]}]},{"name":"Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine","award":["TRYXJH18"],"award-info":[{"award-number":["TRYXJH18"]}]},{"name":"Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine","award":["TRYXJH28"],"award-info":[{"award-number":["TRYXJH28"]}]},{"name":"Research Found of Health Commission of Changing District, Shanghai Municipality","award":["2023QN01"],"award-info":[{"award-number":["2023QN01"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P\u2009&lt;\u20090.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P\u2009&lt;\u20090.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance ( P\u2009&lt;\u20090.001) and lesion conspicuity ( P\u2009=\u20090.010).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02841851241262765","type":"journal-article","created":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T07:18:33Z","timestamp":1721546313000},"page":"1133-1146","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity"],"prefix":"10.1177","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9817-2294","authenticated-orcid":false,"given":"Jingyu","family":"Zhong","sequence":"first","affiliation":[{"name":"Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangfan","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR 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