Optimizing Chest Computed Tomography Imaging Protocols: A Narrative Review on Dose Reduction and Diagnostic Efficacy.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology and Imaging Technology, School of Allied Health Sciences, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, India.
- Department of Medical Physics and Radiation Safety, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, India.
- Department of Radiodiagnosis, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, India.
- Department of Radiology and Imaging Technology and Cardiovascular and Thoracic Surgery, School of Allied Health Sciences, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, India.
Abstract
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. 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. 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. 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. 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.