Abstract

Background: Radiation exposure from computed tomography (CT) remains a significant concern in medical imaging, prompting continuous technological innovation to optimize dose reduction while maintaining diagnostic image quality. This systematic review synthesizes recent developments in CT technology for radiation dose reduction from 2020 to 2025.

Objective: To systematically review and analyze technological advancements in CT imaging that reduce X-ray radiation exposure, evaluate their clinical effectiveness, and identify future research directions.

Methods: A comprehensive literature search was conducted across four major databases (SciSpace, PubMed, Google Scholar, and full-text repositories) for publications from January 2020 to November 2025. Search terms included combinations of “computed tomography,” “CT,” “radiation dose reduction,” “dose optimization,” “low dose,” and related technological terms. Studies were included if they reported on technological innovations for CT dose reduction with quantitative outcomes. A total of 99 unique papers were identified, combined, and reranked by relevance.

Results: Three primary technological tracks emerged: ⁽¹⁾ Advanced reconstruction algorithms (iterative reconstruction and deep learning-based methods) achieving 36-89% dose reductions; ⁽²⁾ Photon-counting detector CT (PCCT) providing superior image quality at significantly lower doses compared to conventional energy-integrating detectors; ⁽³⁾ Optimized acquisition techniques including automatic exposure control, tube current modulation, and spectral shaping with tin filters achieving up to 89% dose reduction in specific protocols. Real-world clinical implementations demonstrated CTDIvol reductions up to 82% and DLP reductions up to 83.9% while maintaining or improving diagnostic accuracy.

Conclusions: Substantial progress has been made in CT dose reduction through convergent advances in reconstruction algorithms, detector hardware, and acquisition strategies. Deep learning reconstruction and photon-counting detectors represent paradigm shifts with proven clinical benefits. However, challenges remain in external validation, standardization across vendors, and task-specific optimization. Future research should focus on prospective multicenter trials, standardized benchmarking protocols, and integrated AI-driven dose optimization systems.