{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T23:36:33Z","timestamp":1772840193582,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Key R & D projects of Hebei Province","award":["20377765D"],"award-info":[{"award-number":["20377765D"]}]},{"name":"Hebei province program of training and basic project of clinical medicine of China","award":["361007"],"award-info":[{"award-number":["361007"]}]},{"name":"Medical Science Foundation of Hebei University","award":["2021A10"],"award-info":[{"award-number":["2021A10"]}]},{"name":"Hebei Province medical technology tracking project","award":["2023093"],"award-info":[{"award-number":["2023093"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01808-9","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T08:39:19Z","timestamp":1752136759000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Feasibility study of \u201cdouble-low\u201d scanning protocol combined with artificial intelligence iterative reconstruction algorithm for abdominal computed tomography enhancement in patients with obesity"],"prefix":"10.1186","volume":"25","author":[{"given":"Mei-Tong","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ren-Ren","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han-Shuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong-Xia","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"1808_CR1","doi-asserted-by":"publisher","first-page":"104204","DOI":"10.1016\/j.bspc.2022.104204","volume":"79","author":"SY Lu","year":"2023","unstructured":"Lu SY, Yang B, Xiao Y, Liu S, Liu MZ, Yin LR, et al. Iterative reconstruction of low-dose CT based on differential sparse. Biomed Signal Process Control. 2023;79:104204.","journal-title":"Biomed Signal Process Control"},{"issue":"10","key":"1808_CR2","doi-asserted-by":"publisher","first-page":"5021","DOI":"10.1007\/s00261-021-03135-3","volume":"46","author":"MH Lee","year":"2021","unstructured":"Lee MH, Lubner MG, Mellnick VM, Menias CO, Bhalla S, Pickhardt PJ. The CT scout view: complementary value added to abdominal CT interpretation. Abdom Radiol (NY). 2021;46(10):5021\u201336.","journal-title":"Abdom Radiol (NY)"},{"issue":"8","key":"1808_CR3","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/s11604-017-0649-4","volume":"35","author":"Y Fukushima","year":"2017","unstructured":"Fukushima Y, Miyazawa H, Nakamura J, Taketomi-Takahashi A, Suto T, Tsushima Y. Contrast-induced nephropathy (CIN) of patients with renal dysfunction in CT examination. Jpn J Radiol. 2017;35(8):427\u201331.","journal-title":"Jpn J Radiol"},{"issue":"7","key":"1808_CR4","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1148\/rg.2021210105","volume":"41","author":"Y Nagayama","year":"2021","unstructured":"Nagayama Y, Sakabe D, Goto M, Emoto T, Oda S, Nakaura T, et al. Deep Learning-based reconstruction for Lower-Dose pediatric CT: technical principles, image characteristics, and clinical implementations. Radiographics. 2021;41(7):1936\u201353.","journal-title":"Radiographics"},{"issue":"3","key":"1808_CR5","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1148\/radiol.2019191422","volume":"293","author":"A Mileto","year":"2019","unstructured":"Mileto A, Guimaraes LS, McCollough CH, Fletcher JG, Yu L. State of the Art in abdominal CT: the limits of iterative reconstruction algorithms. Radiology. 2019;293(3):491\u2013503.","journal-title":"Radiology"},{"issue":"7","key":"1808_CR6","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1016\/j.crad.2023.01.006","volume":"78","author":"L Yang","year":"2023","unstructured":"Yang L, Liu H, Han J, Xu S, Zhang G, Wang Q, et al. Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software. Clin Radiol. 2023;78(7):525\u201331.","journal-title":"Clin Radiol"},{"issue":"2","key":"1808_CR7","doi-asserted-by":"publisher","first-page":"e230192","DOI":"10.1148\/ryai.230192","volume":"6","author":"DH Lee","year":"2024","unstructured":"Lee DH, Lee JM, Lee CH, Afat S, Othman A. Image quality and diagnostic performance of Low-Dose liver CT with deep learning reconstruction versus Standard-Dose CT. Radiol Artif Intell. 2024;6(2):e230192.","journal-title":"Radiol Artif Intell"},{"issue":"4","key":"1808_CR8","doi-asserted-by":"publisher","first-page":"2384","DOI":"10.1007\/s00330-023-10171-8","volume":"34","author":"D Caruso","year":"2024","unstructured":"Caruso D, De Santis D, Del Gaudio A, Guido G, Zerunian M, Polici M, et al. Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm. Eur Radiol. 2024;34(4):2384\u201393.","journal-title":"Eur Radiol"},{"key":"1808_CR9","unstructured":"American Association of Physicists in Medicine. Report of AAPM TG293: size-specific dose estimate (SSDE) for head CT. USA: AAPM. 2019;1G24."},{"key":"1808_CR10","doi-asserted-by":"publisher","first-page":"111657","DOI":"10.1016\/j.radphyschem.2024.111657","volume":"219","author":"MA Barde","year":"2024","unstructured":"Barde MA, Anam C, Razali M A S M, Naharuddin HM, Suhaimi FM, Isa NAM, et al. Comparison between manual-calculated and IndoseCT-calculated SSDE based on Deff and Dw methods on truncated CT images. Radiat Phys Chem. 2024;219:111657.","journal-title":"Radiat Phys Chem"},{"key":"1808_CR11","unstructured":"American Association of Physicistsin Medicine. Report of AAPM TG204: size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. USA: AAPM. 2011;1G22."},{"key":"1808_CR12","unstructured":"American Association of Physicistsin Medicine. Report of AAPM TG 220: use of water equivalent diameter for calculating patient size and size-specific dose estimate (SSDE) in CT. USA: AAPM. 2014;1G23."},{"issue":"7","key":"1808_CR13","doi-asserted-by":"publisher","first-page":"3951","DOI":"10.1007\/s00330-020-06724-w","volume":"30","author":"J Greffier","year":"2020","unstructured":"Greffier J, Hamard A, Pereira F, Barrau C, Pasquier H, Beregi JP, et al. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a Phantom study. Eur Radiol. 2020;30(7):3951\u20139.","journal-title":"Eur Radiol"},{"issue":"10","key":"1808_CR14","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s11934-022-01102-z","volume":"23","author":"R Bhanot","year":"2022","unstructured":"Bhanot R, Hameed ZBM, Shah M, Julieb\u00f8-Jones P, Skolarikos A, Somani B. ALARA in urology: steps to minimise radiation exposure during all parts of the endourological journey. Curr Urol Rep. 2022;23(10):255\u20139.","journal-title":"Curr Urol Rep"},{"issue":"4","key":"1808_CR15","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1120\/jacmp.v17i4.5763","volume":"17","author":"H Li","year":"2016","unstructured":"Li H, Dolly S, Chen HC, Anastasio MA, Low DA, Li HH, et al. A comparative study based on image quality and clinical task performance for CT reconstruction algorithms in radiotherapy. J Appl Clin Med Phys. 2016;17(4):377\u201390.","journal-title":"J Appl Clin Med Phys"},{"issue":"5","key":"1808_CR16","doi-asserted-by":"publisher","first-page":"e13955","DOI":"10.1002\/acm2.13955","volume":"24","author":"B Li","year":"2023","unstructured":"Li B, Wang X, Fan Y, Wang S, Tong X, Zhang J, et al. Evaluation of BMI-based tube voltage selection in CT colonography: a prospective comparison of low kV versus routine 120\u00a0kv protocol. J Appl Clin Med Phys. 2023;24(5):e13955.","journal-title":"J Appl Clin Med Phys"},{"key":"1808_CR17","volume-title":"American college of radiology. ACR\u2013AAPM\u2013SPR practice parameter for diagnostic reference levels and achievable doses in medical X-Ray imaging","author":"American Association of Physicistsin Medicine","year":"2023","unstructured":"American Association of Physicistsin Medicine. American college of radiology. ACR\u2013AAPM\u2013SPR practice parameter for diagnostic reference levels and achievable doses in medical X-Ray imaging. USA: AAPM; 2023. https:\/\/gravitas.acr.org\/PPTS\/DownloadPreviewDocument?DocId=16."},{"issue":"3","key":"1808_CR18","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.21037\/qims-24-1570","volume":"15","author":"QS Qiu","year":"2025","unstructured":"Qiu QS, Chen XS, Wang WT, Wang JH, Yan C, et al. Image quality, diagnostic performance of reduced-dose abdominal CT with artificial intelligence model-based iterative reconstruction for colorectal liver metastasis: a prospective cohort study. Quant Imaging Med Surg. 2025;15(3):2106\u201318.","journal-title":"Quant Imaging Med Surg"},{"issue":"6","key":"1808_CR19","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.jcct.2009.10.002","volume":"3","author":"R Nakazato","year":"2009","unstructured":"Nakazato R, Dey D, Gutstein A, Le Meunier L, Cheng VY, Pimentel R, et al. Coronary artery calcium scoring using a reduced tube voltage and radiation dose protocol with dual-source computed tomography. J Cardiovasc Comput Tomogr. 2009;3(6):394\u2013400.","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"1808_CR20","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ejmp.2022.01.007","volume":"95","author":"M Hoshika","year":"2022","unstructured":"Hoshika M, Nakaura T, Oda S, Kidoh M, Nagayama Y, Sakabe D, et al. Comparison of the effects of varying tube voltage and iodinated concentration on increasing the iodinated radiation dose in computed tomography. Phys Med. 2022;95:57\u201363.","journal-title":"Phys Med"},{"issue":"3","key":"1808_CR21","doi-asserted-by":"publisher","first-page":"e221257","DOI":"10.1148\/radiol.221257","volume":"306","author":"LR Koetzier","year":"2023","unstructured":"Koetzier LR, Mastrodicasa D, Szczykutowicz TP, van der Werf NR, Wang AS, Sandfort V, et al. Deep learning image reconstruction for CT: technical principles and clinical prospects. Radiology. 2023;306(3):e221257.","journal-title":"Radiology"},{"key":"1808_CR22","doi-asserted-by":"publisher","first-page":"111355","DOI":"10.1016\/j.ejrad.2024.111355","volume":"172","author":"H Chen","year":"2024","unstructured":"Chen H, Li Q, Zhou L, Li F. Deep learning-based algorithms for low-dose CT imaging: A review. Eur J Radiol. 2024;172:111355.","journal-title":"Eur J Radiol"},{"issue":"4","key":"1808_CR23","doi-asserted-by":"publisher","first-page":"e0827","DOI":"10.1097\/RTI.0000000000000827","volume":"40","author":"F Zhang","year":"2025","unstructured":"Zhang F, Peng L, Zhang G, Xie R, Sun M, et al. Artificial intelligence iterative reconstruction for dose reduction in pediatric chest CT: A clinical assessment via below 3 years patients with congenital heart disease. J Thorac Imaging. 2025;40(4):e0827.","journal-title":"J Thorac Imaging"},{"issue":"1","key":"1808_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40658-022-00521-8","volume":"10","author":"Y Hu","year":"2023","unstructured":"Hu Y, Zheng Z, Yu H, Wang J, Yang X, Shi H. Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET\/CT examination: a preliminary study. EJNMMI Phys. 2023;10(1):1.","journal-title":"EJNMMI Phys"},{"issue":"4","key":"1808_CR25","first-page":"101087","volume":"17","author":"W Ding","year":"2024","unstructured":"Ding W, Liu ZY, Ma ZP, Zhang TL, Zhao YX. Comparative study of image quality and radiation dose in thoracic-abdominal-pelvic CT enhancement with different tube voltages and reconstruction algorithms. J Radiation Res Appl Sci. 2024;17(4):101087.","journal-title":"J Radiation Res Appl Sci"},{"issue":"6","key":"1808_CR26","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.crad.2021.01.010","volume":"76","author":"CM McLeavy","year":"2021","unstructured":"McLeavy CM, Chunara MH, Gravell RJ, Rauf A, Cushnie A, Staley Talbot C, et al. The future of CT: deep learning reconstruction. Clin Radiol. 2021;76(6):407\u201315.","journal-title":"Clin Radiol"},{"issue":"7","key":"1808_CR27","doi-asserted-by":"publisher","first-page":"5139","DOI":"10.1007\/s00330-020-07537-7","volume":"31","author":"JG Nam","year":"2021","unstructured":"Nam JG, Ahn C, Choi H, Hong W, Park J, Kim JH, et al. Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques. Eur Radiol. 2021;31(7):5139\u201347.","journal-title":"Eur Radiol"},{"key":"1808_CR28","doi-asserted-by":"publisher","first-page":"110221","DOI":"10.1016\/j.ejrad.2022.110221","volume":"149","author":"W Li","year":"2022","unstructured":"Li W, You Y, Zhong S, Shuai T, Liao K, Yu J, et al. Image quality assessment of artificial intelligence iterative reconstruction for low dose aortic CTA: A feasibility study of 70 kVp and reduced contrast medium volume. Eur J Radiol. 2022;149:110221.","journal-title":"Eur J Radiol"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01808-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01808-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01808-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T04:08:13Z","timestamp":1757218093000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01808-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1808"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01808-9","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"assertion":[{"value":"18 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures performed in the study involving human participants were in accordance with the ethical standards of the Affiliated Hospital of Hebei University and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not include any studies of animal experiments by any of the authors. This study was approved by the Ethics Committee of the Affiliated Hospital of Hebei University (Approval Number: HDFYLL-KY-2024-029). Before the examination, patients or their immediate family members signed the informed consent form for the examination.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"276"}}