Potential of artificial intelligence for radiation dose reduction in computed tomography —A scoping review

IF 2.5 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiography Pub Date : 2025-05-07 DOI:10.1016/j.radi.2025.102968

M. Bani-Ahmad , A. England , L. McLaughlin , Y.H. Hadi , M. McEntee

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引用次数: 0

Abstract

Introduction

Artificial intelligence (AI) is now transforming medical imaging, with extensive ramifications for nearly every aspect of diagnostic imaging, including computed tomography (CT). This current work aims to review, evaluate, and summarise the role of AI in radiation dose optimisation across three fundamental domains in CT: patient positioning, scan range determination, and image reconstruction.

Methods

A comprehensive scoping review of the literature was performed. Electronic databases including Scopus, Ovid, EBSCOhost and PubMed were searched between January 2018 and December 2024. Relevant articles were identified from their titles had their abstracts evaluated, and those deemed relevant had their full text reviewed. Extracted data from selected studies included the application of AI, radiation dose, anatomical part, and any relevant evaluation metrics based on the CT parameter in which AI is applied.

Results

90 articles met the selection criteria. Included studies evaluated the performance of AI for dose optimisation through patient positioning, scan range determination, and reconstruction across various CT scans, including the abdomen, chest, head, neck, and pelvis, as well as CT angiography. A concise overview of the present state of AI in these three domains, emphasising benefits, limitations, and impact on the transformation of dose reduction in CT scanning, is provided.

Conclusions

AI methods can help minimise positioning offsets and over-scanning caused by manual errors and helped to overcome the limitation associated with low-dose CT settings through deep learning image reconstruction algorithms. Further clinical integration of AI will continue to allow for improvements in optimising CT scan protocols and radiation dose.

Implications for practice

This review underscores the significance of AI in optimizing radiation doses in CT imaging, focusing on three key areas: patient positioning, scan range determination, and image reconstruction.

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人工智能在计算机断层扫描中降低辐射剂量的潜力——范围综述

人工智能（AI）正在改变医学成像，对诊断成像的几乎每个方面都有广泛的影响，包括计算机断层扫描（CT）。本研究旨在回顾、评估和总结人工智能在CT三个基本领域的辐射剂量优化中的作用：患者定位、扫描范围确定和图像重建。方法对文献进行全面的范围综述。在2018年1月至2024年12月期间检索了包括Scopus、Ovid、EBSCOhost和PubMed在内的电子数据库。从标题中找出相关文章，并对其摘要进行评估，对认为相关的文章进行全文审查。从选定的研究中提取的数据包括人工智能的应用、辐射剂量、解剖部位以及基于人工智能应用的CT参数的任何相关评价指标。结果90篇文章符合入选标准。纳入的研究通过患者定位、扫描范围确定和各种CT扫描（包括腹部、胸部、头部、颈部和骨盆）以及CT血管造影，评估了人工智能在剂量优化方面的性能。简要概述了人工智能在这三个领域的现状，强调了益处、局限性和对CT扫描剂量减少转化的影响。结论ai方法可以最大限度地减少人为误差造成的定位偏移和过度扫描，并通过深度学习图像重建算法克服低剂量CT设置的局限性。人工智能的进一步临床整合将继续允许优化CT扫描方案和辐射剂量的改进。本综述强调了人工智能在优化CT成像辐射剂量方面的重要性，重点关注三个关键领域：患者定位、扫描范围确定和图像重建。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Radiography RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.70

自引率

34.60%

发文量

169

审稿时长

63 days

期刊介绍： Radiography is an International, English language, peer-reviewed journal of diagnostic imaging and radiation therapy. Radiography is the official professional journal of the College of Radiographers and is published quarterly. Radiography aims to publish the highest quality material, both clinical and scientific, on all aspects of diagnostic imaging and radiation therapy and oncology.