{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T05:54:30Z","timestamp":1766987670446,"version":"3.48.0"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T00:00:00Z","timestamp":1766880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>This systematic review evaluated the effectiveness of iterative reconstruction (IR) and deep learning reconstruction (DLR) in reducing radiation dose in computed tomography (CT) while preserving diagnostic image quality. We systematically searched PubMed, Scopus, and Web of Science (last search 22 March 2025); the protocol was registered in the OSF (DOI: 10.17605\/OSF.IO\/TUQDS). Eligible studies were English-language adult (\u226518 years) investigations published between 2020 and 2025 that used IR or DLR and reported radiation-dose outcomes; studies on paediatric, phantom, cadaver, cone-beam, and spectral CT were excluded. In accordance with PRISMA 2020 guidelines, 4371 records were identified, and 30 met the inclusion criteria. Risk of bias was assessed using the NIH Quality Assessment Tool; most studies were deemed to be at low risk. Data were narratively synthesised and structured by a reconstruction approach and anatomical region. Across the 30 studies, IR achieved a dose reduction of 24\u201350% (mean \u2248 45%) and a DLR reduction of 34\u201389% (mean \u2248 58%); several DLR protocols enabled reductions of \u226575% without impairing diagnostic quality. Thirty studies in total were included (total N = 2581; range 24\u2013289). It was determined that both approaches substantially reduce radiation exposure while maintaining diagnostic image quality; DLR generally demonstrates greater noise suppression and dose efficiency, especially in ultra-low-dose applications. However, heterogeneity in methods, designs, and scanner technologies limits the ability to draw uniform conclusions. Standardised protocols, multi-vendor prospective studies, and long-term evaluations are needed.<\/jats:p>","DOI":"10.3390\/app16010316","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T05:28:19Z","timestamp":1766986099000},"page":"316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiation Dose Reduction in CT Exams with Iterative and Deep Learning Reconstruction: A Systematic Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0343-2937","authenticated-orcid":false,"given":"Sandra","family":"Coelho","sequence":"first","affiliation":[{"name":"Center for Natural Resources and Environment (CERENA), Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4774-2533","authenticated-orcid":false,"given":"Maria de Lurdes","family":"Dinis","sequence":"additional","affiliation":[{"name":"Center for Natural Resources and Environment (CERENA), Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4514-9728","authenticated-orcid":false,"given":"Marco","family":"Freitas","sequence":"additional","affiliation":[{"name":"Radiology Department, School of Health, Polytechnic Institute of Porto, 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-5503","authenticated-orcid":false,"given":"Jo\u00e3o Santos","family":"Baptista","sequence":"additional","affiliation":[{"name":"Associated Laboratory of Energy, Transports and Aerospace (LAETA), Faculty of Engineering, University of Porto, Rua Dr Roberto Frias, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,28]]},"reference":[{"key":"ref_1","first-page":"k4931","article-title":"International Variation in Radiation Dose for Computed Tomography Examinations: Prospective Cohort Study","volume":"364","author":"Wang","year":"2019","journal-title":"BMJ"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yeom, J.A., Kim, K.U., Hwang, M., Lee, J.W., Kim, K.I., Song, Y.S., Lee, I.S., and Jeong, Y.J. 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