{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T19:54:01Z","timestamp":1790452441880,"version":"4.1.0"},"reference-count":25,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["XST"],"published-print":{"date-parts":[[2022,4,15]]},"abstract":"<jats:p>OBJECTIVE: To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V). METHODS: We prospectively recruited 52 patients. All images were reconstructed with ASiR-V 30%, ASiR-V 70%, and DLIR at low, medium, and high reconstruction strengths. CT number, noise, noise reduction rate, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated within the region of interest (ROI) on subcutaneous fat, bilateral renal cortices, renal medulla, renal arteries, and adrenal glands. For qualitative analyses, the differentiation of the renal cortex and medulla, conspicuity of the adrenal gland boundary, sharpness, artifacts, and subjective noise were assessed. The overall image quality was calculated on a scale from 0 (worst) to 15 (best) based on the five values above and the score\u22659 was acceptable. RESULTS: CT number does not significantly differ between the reconstruction datasets. Noise does not significantly differ between ASiR-V 30% and DLIR-L, but it is significantly lower using ASiR-V 70%, DLIR-M, and DLIR-H. The noise reduction rate relative to ASiR-V 30% is significantly different between the DLIR groups and ASiR-V 70%, and DLIR-H yields the highest noise reduction rate (61.6%). SNR and CNR are higher for DLIR-M, DLIR-H, and ASiR-V 70% than for ASiR-V 30% and DLIR-L. DLIR-H shows the best SNR and CNR. The overall image quality yields the same pattern for DLIR-H, with the highest score. Percentages of cases with overall image quality score\u22659 are 100% (DLIR-H), 94.23% (DLIR-M), 90.38% (ASiR-V70%), 67.31% (DLIR-L), and 63.46% (ASiR-V30%), respectively. CONCLUSIONS: DLIR significantly improved the objective and subjective image quality of renal and adrenal CTs, yielding superior noise reduction compared with ASiR-V.<\/jats:p>","DOI":"10.3233\/xst-211105","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T19:26:22Z","timestamp":1644348382000},"page":"409-418","source":"Crossref","is-referenced-by-count":6,"title":["Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography"],"prefix":"10.1177","volume":"30","author":[{"given":"Yifan","family":"Bie","sequence":"first","affiliation":[{"name":"Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingchao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changlei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Radiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"9840","key":"10.3233\/XST-211105_ref1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/S0140-6736(12)60815-0","article-title":"Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study","volume":"380","author":"Pearce","year":"2012","journal-title":"Lancet"},{"issue":"22","key":"10.3233\/XST-211105_ref2","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1056\/NEJMra072149","article-title":"Computed tomography - 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