Publicação
Radiation dose reduction in CT exams with iterative and deep learning reconstruction: A systematic review
| dc.contributor.author | Coelho, Sandra | |
| dc.contributor.author | Dinis, Maria de Lurdes | |
| dc.contributor.author | Freitas, Marco | |
| dc.contributor.author | Baptista, João Santos | |
| dc.date.accessioned | 2026-07-14T09:55:18Z | |
| dc.date.available | 2026-07-14T09:55:18Z | |
| dc.date.issued | 2025-12-28 | |
| dc.description.abstract | 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 (≥18 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 24–50% (mean ≈ 45%) and a DLR reduction of 34–89% (mean ≈ 58%); several DLR protocols enabled reductions of ≥75% without impairing diagnostic quality. Thirty studies in total were included (total N = 2581; range 24–289). 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 longterm evaluations are needed. | eng |
| dc.identifier.citation | Coelho, S., Dinis, M. de L., Freitas, M., & Baptista, J. S. (2026). Radiation dose reduction in CT exams with iterative and deep learning reconstruction: A systematic review. Applied Sciences, 16(1), 316. https://doi.org/10.3390/app16010316 | |
| dc.identifier.doi | 10.3390/app16010316 | |
| dc.identifier.eissn | 2076-3417 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/32582 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation.hasversion | https://www.mdpi.com/2076-3417/16/1/316 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Radiation dose reduction | |
| dc.subject | Computed tomography | |
| dc.subject | Iterative reconstruction | |
| dc.subject | Deep learning | |
| dc.title | Radiation dose reduction in CT exams with iterative and deep learning reconstruction: A systematic review | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 1 | |
| oaire.citation.title | Applied Sciences | |
| oaire.citation.volume | 16 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |