AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.

IF 3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Aanuoluwapo Clement David-Olawade, David B Olawade, Laura Vanderbloemen, Oluwayomi B Rotifa, Sandra Chinaza Fidelis, Eghosasere Egbon, Akwaowo Owoidighe Akpan, Sola Adeleke, Aruni Ghose, Stergios Boussios
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Abstract

The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic imaging and minimizing radiation exposure remains a fundamental challenge in radiology. Artificial intelligence (AI) has emerged as a transformative solution, enabling low-dose imaging protocols that enhance image quality while significantly reducing radiation doses. This review explores the role of AI-assisted low-dose imaging, particularly in CT, X-ray, and magnetic resonance imaging (MRI), highlighting advancements in deep learning models, convolutional neural networks (CNNs), and other AI-based approaches. These technologies have demonstrated substantial improvements in noise reduction, artifact removal, and real-time optimization of imaging parameters, thereby enhancing diagnostic accuracy while mitigating radiation risks. Additionally, AI has contributed to improved radiology workflow efficiency and cost reduction by minimizing the need for repeat scans. The review also discusses emerging directions in AI-driven medical imaging, including hybrid AI systems that integrate post-processing with real-time data acquisition, personalized imaging protocols tailored to patient characteristics, and the expansion of AI applications to fluoroscopy and positron emission tomography (PET). However, challenges such as model generalizability, regulatory constraints, ethical considerations, and computational requirements must be addressed to facilitate broader clinical adoption. AI-driven low-dose imaging has the potential to revolutionize radiology by enhancing patient safety, optimizing imaging quality, and improving healthcare efficiency, paving the way for a more advanced and sustainable future in medical imaging.

人工智能驱动的低剂量成像和增强技术进展综述
医学成像技术如x射线和计算机断层扫描(CT)的广泛使用引起了人们对电离辐射暴露的严重关切,特别是在需要经常成像的脆弱人群中。实现高质量诊断成像和最小化辐射暴露之间的平衡仍然是放射学的基本挑战。人工智能(AI)已经成为一种变革性的解决方案,使低剂量成像协议能够在显著降低辐射剂量的同时提高图像质量。这篇综述探讨了人工智能辅助的低剂量成像的作用,特别是在CT、x射线和磁共振成像(MRI)方面,重点介绍了深度学习模型、卷积神经网络(cnn)和其他基于人工智能的方法的进展。这些技术在降噪、去除伪影和实时优化成像参数方面有了实质性的改进,从而提高了诊断的准确性,同时降低了辐射风险。此外,人工智能通过最大限度地减少重复扫描的需要,有助于提高放射学工作流程的效率和降低成本。该综述还讨论了人工智能驱动的医学成像的新兴方向,包括将后处理与实时数据采集相结合的混合人工智能系统,针对患者特征量身定制的个性化成像方案,以及人工智能应用扩展到透视和正电子发射断层扫描(PET)。然而,必须解决诸如模型通用性、监管约束、伦理考虑和计算需求等挑战,以促进更广泛的临床应用。人工智能驱动的低剂量成像有可能通过增强患者安全、优化成像质量和提高医疗效率来彻底改变放射学,为医学成像更先进和可持续的未来铺平道路。
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
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
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