Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review
Medicina e TecnologiaResumo original em inglês
O que aconteceu
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.
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.
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.
O que encontraram
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.
Por que isso importa
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.
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.
Resumo publicado sob licença CC BY. A tradução para o português ainda não chegou a este estudo.
O estudo original
Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review
Cecile E J Kleber, Ramez Karius, Lucas E Naessens, Coen O Van Toledo, Jochen A C van Osch, Martijn F Boomsma, Jan W T Heemskerk, Aart J van der Molen
Descritores MeSH
- Deep Learning
- Tomography, X-Ray Computed
- Artifacts
- Metals
- Supervised Machine Learning
- Prostheses and Implants
- Algorithms
- Humans
- Radiographic Image Interpretation, Computer-Assisted
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