Optimizing Chest Computed Tomography Imaging Protocols: A Narrative Review on Dose Reduction and Diagnostic Efficacy
Suhas Tivaskar, Anurag Luharia, Gaurav V. Mishra, Aditya Shrivastav, Sarthak DasBackground:
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.