ReviewJournal of medical physics
Optimizing Chest Computed Tomography Imaging Protocols: A Narrative Review on Dose Reduction and Diagnostic Efficacy.
Review in Journal of medical physics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The worldwide deployment of chest computed tomography (CT) has increased substantially across cancer-related, respiratory and cardiac applications, leading to increasing aggregate radiation burden and substantially intercenter variability in radiation dose management strategies. Purpose: The purpose of this study was to comprehensively review existing literature on radiation dose optimization approaches in chest CT, with particular emphasis on phantom-based protocol optimization, deep learning-based reconstruction methods, and evidence-based protocol modification. Methods: This review was conducted using a systematic narrative synthesis with elements of systematic procedure to enrich transparency and reproducibility. PubMed, Scopus, and ScienceDirect were searched for English-language studies published between January 2015 and January 2025 that interrogated radiation dose optimization strategies in adult chest CT imaging. Human and phantom studies reporting radiation metrics and image-quality outcomes were included. Evidence was qualitatively synthesized across technological, methodological, and clinical implementation domains. Results: Iterative and model-based reconstruction techniques uniformly achieved dose reductions of approximately 30%-60% while without compromising image quality. Deep learning-based reconstruction demonstrated more effective noise reduction and preservation of anatomical detail, supporting dose reductions of up to 70% in selected thoracic applications. Phantom-based verification enhanced protocol reproducibility and reduced interscanner variability. Ultra-low-dose chest CT (<0.4 mSv) was shown to be clinically dependable for lung nodule detection in selected clinical settings. Conclusions: Incorporation of phantom-based verification with a state-of-the-art reconstruction algorithm facilitates substantial radiation dose reduction in chest CT without compromising diagnostic quality. Task-specific protocol optimization and centre-specific validation remain for safe and reproducible clinical implementation.
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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.