{"title":"用于减少计算机断层扫描中金属伪影的有监督深度学习的进展:系统综述","authors":"","doi":"10.1016/j.ejrad.2024.111732","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal artefact reduction (MAR) algorithms are entering clinical practice.</p></div><div><h3>Objective</h3><p>This systematic review provides an overview of the performance of the current supervised DL-based MAR algorithms for CT, focusing on three different domains: sinogram, image, and dual domain.</p></div><div><h3>Methods</h3><p>A literature search was conducted in PubMed, EMBASE, Web of Science, and Scopus. Outcomes were assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) or any other objective measure comparing MAR performance to uncorrected images.</p></div><div><h3>Results</h3><p>After screening, fourteen studies were selected that compared DL-based MAR-algorithms with uncorrected images. MAR-algorithms were categorised into the three domains. Thirteen MAR-algorithms showed a higher PSNR and SSIM value compared to the uncorrected images and to non-DL MAR-algorithms. One study showed statistically significant better MAR performance on clinical data compared to the uncorrected images and non-DL MAR-algorithms based on Hounsfield unit calculations.</p></div><div><h3>Conclusion</h3><p>DL MAR-algorithms show promising results in reducing metal artefacts, but standardised methodologies are needed to evaluate DL-based MAR-algorithms on clinical data to improve comparability between algorithms.</p><p>Clinical relevance statement:</p><p>Recent studies highlight the effectiveness of supervised Deep Learning-based MAR-algorithms in improving CT image quality by reducing metal artefacts in the sinogram, image and dual domain. A systematic review is needed to provide an overview of newly developed algorithms.</p></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0720048X24004480/pdfft?md5=1c9570e2dfde4f1920a5e5bea4f8ffa2&pid=1-s2.0-S0720048X24004480-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review\",\"authors\":\"\",\"doi\":\"10.1016/j.ejrad.2024.111732\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal artefact reduction (MAR) algorithms are entering clinical practice.</p></div><div><h3>Objective</h3><p>This systematic review provides an overview of the performance of the current supervised DL-based MAR algorithms for CT, focusing on three different domains: sinogram, image, and dual domain.</p></div><div><h3>Methods</h3><p>A literature search was conducted in PubMed, EMBASE, Web of Science, and Scopus. Outcomes were assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) or any other objective measure comparing MAR performance to uncorrected images.</p></div><div><h3>Results</h3><p>After screening, fourteen studies were selected that compared DL-based MAR-algorithms with uncorrected images. MAR-algorithms were categorised into the three domains. Thirteen MAR-algorithms showed a higher PSNR and SSIM value compared to the uncorrected images and to non-DL MAR-algorithms. One study showed statistically significant better MAR performance on clinical data compared to the uncorrected images and non-DL MAR-algorithms based on Hounsfield unit calculations.</p></div><div><h3>Conclusion</h3><p>DL MAR-algorithms show promising results in reducing metal artefacts, but standardised methodologies are needed to evaluate DL-based MAR-algorithms on clinical data to improve comparability between algorithms.</p><p>Clinical relevance statement:</p><p>Recent studies highlight the effectiveness of supervised Deep Learning-based MAR-algorithms in improving CT image quality by reducing metal artefacts in the sinogram, image and dual domain. 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引用次数: 0
摘要
背景金属植入物造成的金属伪影是计算机断层扫描(CT)成像中的常见问题,会降低图像质量和诊断准确性。随着人工智能的发展,基于深度学习(DL)的新型金属伪影减少(MAR)算法正在进入临床实践。目的本系统综述概述了当前基于深度学习的有监督 MAR 算法在 CT 方面的性能,重点关注三个不同领域:正弦图、图像和双域。方法在 PubMed、EMBASE、Web of Science 和 Scopus 上进行了文献检索。结果使用峰值信噪比(PSNR)和结构相似性指数测量法(SSIM)或任何其他比较 MAR 性能与未校正图像的客观测量法进行评估。MAR 算法分为三个领域。与未校正图像和非 DL MAR 算法相比,13 项 MAR 算法显示出更高的 PSNR 和 SSIM 值。一项研究显示,与未经校正的图像和基于 Hounsfield 单位计算的非 DL MAR 算法相比,临床数据的 MAR 性能在统计学上有显著提高。结论DL MAR 算法在减少金属伪影方面显示出良好的效果,但需要标准化的方法来评估临床数据中基于 DL 的 MAR 算法,以提高算法之间的可比性。需要对新开发的算法进行系统综述。
Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review
Background
Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal artefact reduction (MAR) algorithms are entering clinical practice.
Objective
This systematic review provides an overview of the performance of the current supervised DL-based MAR algorithms for CT, focusing on three different domains: sinogram, image, and dual domain.
Methods
A literature search was conducted in PubMed, EMBASE, Web of Science, and Scopus. Outcomes were assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) or any other objective measure comparing MAR performance to uncorrected images.
Results
After screening, fourteen studies were selected that compared DL-based MAR-algorithms with uncorrected images. MAR-algorithms were categorised into the three domains. Thirteen MAR-algorithms showed a higher PSNR and SSIM value compared to the uncorrected images and to non-DL MAR-algorithms. One study showed statistically significant better MAR performance on clinical data compared to the uncorrected images and non-DL MAR-algorithms based on Hounsfield unit calculations.
Conclusion
DL MAR-algorithms show promising results in reducing metal artefacts, but standardised methodologies are needed to evaluate DL-based MAR-algorithms on clinical data to improve comparability between algorithms.
Clinical relevance statement:
Recent studies highlight the effectiveness of supervised Deep Learning-based MAR-algorithms in improving CT image quality by reducing metal artefacts in the sinogram, image and dual domain. A systematic review is needed to provide an overview of newly developed algorithms.
期刊介绍:
European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field.
Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.