Evidence map›Paper›PMID 41346133›Full record

ArticleDiagnostic and interventional radiology (Ankara, Turkey)2026

Potential radiation dose reduction in computed tomography with deep learning reconstruction: a retrospective monocentric study.

Lucas Graber, Melike Zeynep Akış, François Séverac, Luc Mertz, Serhat Akış, Catherine Roy, Mickaël Ohana

Abstract read
In one paragraph

Article in Diagnostic and interventional radiology (Ankara, Turkey), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lucas GraberHôpital Civil, Department of Radiology, Strasbourg, France.ORCID 0000-0001-7919-5960
Melike Zeynep AkışDokuz Eylül University Hospital, Department of Radiology, İzmir, Türkiye.ORCID 0009-0008-0442-7315
François SéveracUniversité de Strasbourg, Department of Biostatistics and Informatics, Strasbourg, France.ORCID 0000-0002-6611-1921
Luc MertzUniversité de Strasbourg, Department of Radiation Safety and Radiophysics, Strasbourg, France.ORCID 0000-0001-9115-2111
Serhat AkışDokuz Eylül University Hospital, Department of Radiology, İzmir, Türkiye.ORCID 0009-0001-7899-9251
Catherine RoyHôpital Civil, Department of Radiology, Strasbourg, France.ORCID 0000-0003-0527-5305
Mickaël OhanaHôpital Civil, Department of Radiology, Strasbourg, France.ORCID 0000-0003-0179-5784

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate whether deep learning reconstruction (DLR) can reduce the radiation dose in routine clinical computed tomography (CT) scans compared with iterative reconstruction (IR) while maintaining or improving image quality. The study assesses DLR's consistency and effectiveness across four distinct CT protocols-chest, head, chest-abdomen-pelvis (CAP) oncology, and lower limb CT angiography (CTA)-representing a wide range of clinical applications.

methodsOur study is retrospective and monocentric. It involves a total population of 13,060 patients who underwent a CT scan using either a DLR algorithm (CT-DLR) or an IR algorithm (CT-IR) in one of four different CT acquisition protocols. Image quality was evaluated qualitatively and quantitatively by measuring standardized signal-to-noise ratio and contrast-to-noise ratio values. Assessment was performed on a subsample of 200 patients (25 per protocol per group).

resultsThe overall reduction in radiation dose for the CT-DLR group compared with the CT-IR group was approximately 20%. By protocol, dose reductions were 22% for chest CT, 21% for CAP oncology, 20% for lower limb CTA, and 19% for head CT. The CT-DLR group exhibited superior subjective and objective image quality to the CT-IR group.

conclusionDLR algorithms allow for a significant reduction in radiation dose while achieving higher image quality compared with IR algorithms. CLINICAL SIGNIFICANCE: This large-scale study confirms that DLR can significantly reduce the radiation dose in routine CT imaging while maintaining or enhancing diagnostic image quality. Its consistent performance across multiple protocols supports broader clinical adoption. Notably, the greatest dose reductions were observed in high-use protocols such as chest and CAP CT, underscoring DLR's potential to improve both individual patient care and long-term population-level radiation safety.

Indexed as

Deep LearningImage Processing, Computer-AssistedRadiation DosageRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdultAgedAged, 80 and overAlgorithmsFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise Ratiocomputed tomography scanDeep learning reconstructionimage qualityradiation dose reduction

Identifiers

PMID41346133
PMCPMC13530545

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.