Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review

European Journal of Radiology, vol. 181, pp. 111732

Abstract

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.

Authors 8

1. Cecile E J Kleber

   Delft University of Technology

   Affiliation as printed

   Department of Clinical Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands

2. Ramez Karius

   Delft University of Technology

   Affiliation as printed

   Department of Clinical Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands

3. Lucas E Naessens

   Delft University of Technology

   Affiliation as printed

   Department of Clinical Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands

4. Coen O Van Toledo

   Delft University of Technology

   Affiliation as printed

   Department of Clinical Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands

5. Jochen A. C. van Osch

   Isala

   Affiliation as printed

   Department of Radiology, Isala Hospital, Zwolle, the Netherlands

6. Martijn F. Boomsma

   Isala

   Affiliation as printed

   Department of Radiology, Isala Hospital, Zwolle, the Netherlands

7. Jan W T Heemskerk Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands

8. Aart J. van der Molen Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: A.J.van_der_Molen@lumc.nl