The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Optimization and image quality in CT studies: application of DRLs based on clinical indication
Open accessAbstract
The use of ionizing radiation in computed tomography (CT) has significantly enhanced diagnostic accuracy and clinical decision-making. However, optimizing CT protocols is crucial to balance the diagnostic benefits with potential radiation risks, particularly for vulnerable populations. Diagnostic Reference Levels (DRLs), introduced to guide dose optimization, highlight variations in dose indices due to local protocol differences rather than patient or equipment characteristics. This underscores the importance of standardizing technical parameters to ensure consistent image quality and minimize dose variability. Recent advancements, including iterative reconstruction algorithms and artificial intelligence, offer promising avenues for dose reduction without compromising diagnostic value. Effective optimization strategies require a multidisciplinary approach, incorporating continuous protocol evaluation, adherence to quality assurance programs, and clinical audits. Standardized DRLs tailored to clinical indications further enhance imaging practices, reducing variability and fostering safer, more effective patient care. The purpose of this article is to demonstrate, through a review of the literature and recent papers, applied to clinical practice, the role of diagnostic reference levels (DRLs) in CT optimization and image’s quality.
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“The implementation of DRLs is a crucial element of radiation protection protocols in medical imaging, designed to uphold the principle of optimization.70,71”
From Computed Tomography Diagnostic Reference Levels Worldwide and in Jordan: Implications for Patient Dose Reduction (2026) · cites this paper as [71]
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Citations
Cites
International Agency for Research on Cancer
National Cancer Institute (2020) · Definitions · doi:10.32388/pfg7jq · Cited by 9,394
Computed Tomography — An Increasing Source of Radiation Exposure
Brenner, D. J., & Hall, E. J. (2007) · New England Journal of Medicine · doi:10.1056/nejmra072149 · Cited by 8,738
ICRP Publication 135: Diagnostic Reference Levels in Medical Imaging
Vañó, E., Miller, D. L., Martin, C. J., Rehani, M. M., Kang, K., Rosenstein, M., Ortiz-López, P., Mattsson, S., Padovani, R., & Rogers, A. (2017) · Annals of the ICRP · doi:10.1177/0146645317717209 · Cited by 1,043
Current Applications and Future Impact of Machine Learning in Radiology
Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P. V., Brink, J. A., & Dreyer, K. J. (2018) · Radiology · doi:10.1148/radiol.2018171820 · Cited by 800
Deep learning for tomographic image reconstruction
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Radiological protection and safety in medicine
(1996) · Annals of the ICRP · doi:10.1016/s0146-6453(00)89195-2 · Cited by 438
Smith‐Bindman, R., Wang, Y., Chu, P., Chung, R., Einstein, A. J., Balcombe, J. N., Cocker, M. R., Das, M., Delman, B. N., Flynn, M., et al. (2019) · BMJ · doi:10.1136/bmj.k4931 · Cited by 244
CT in the Emergency Department: A Real-Time Study of Changes in Physician Decision Making
Pandharipande, P. V., Reisner, A. T., Binder, W. D., Zaheer, A., Gunn, M. L. D., Linnau, K. F., Miller, C., Avery, L. L., Herring, M. S., Tramontano, A. C., et al. (2015) · Radiology · doi:10.1148/radiol.2015150473 · Cited by 151
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Cited by
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Oglat, A., Shdaifat, S., Sayah, M., Abukonna, A., & Khogali, W. (2026) · Drug, Healthcare and Patient Safety · doi:10.2147/dhps.s595721
“70,71 According to ICRP Publication 135, DRLs function as investigative thresholds rather than regulatory dose limits, aimed at identifying abnormally high radiation doses in standard diagnostic and interventional procedures.”
Metadata
- Authors
- Picone, C., Porto, A., Fusco, R., Granata, C., Brunese, M. C., Granata, V., Ottaiano, A., Barretta, M., Caranci, F., Pecori, B., Sorgente, E., Salerno, S., Mormile, R., Pinto, F., Pinto, A., Magistrelli, A., & Petrillo, A.
- Affiliations
- Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (IT)
- IRCCS Materno Infantile Burlo Garofolo (IT)
- University of Molise (IT)
- University of Campania "Luigi Vanvitelli" (IT)
- University of Palermo (IT)
- Azienda Ospedaliera S.Giuseppe Moscati (IT)
- Bambino Gesù Children's Hospital (IT)
- Corresponding author
- Picone, Carmine
- ORCID
- Venue
- Discover Applied Sciences
- Published
- 2025-05-22
- Volume
- 7
- Issue
- 6
- ISSN
- 3004-9261 (ISSN-L)
- OpenAlex
- W4410614211
- Cited by
- 3
- References
- 17
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 1.74
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Discover Applied Sciences (publishedVersion, cc-by-nc-nd)
- CINECA IRIS Institutional Research Information System (University of Basilicata) (submittedVersion, public-domain)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Digital Radiography and Breast Imaging
- Keywords
- diagnostic reference levels, CT dose optimization, radiation dose reduction, image quality, iterative reconstruction, quality assurance, clinical audit, protocol standardization
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