ArticleScientific reports2025
Deep learning-based metal artifact reduction in CT for total knee arthroplasty.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Assessment of radiation permeability of CoCrMo and Ti-6Al-4V implants by Monte Carlo simulation of mass attenuation coefficients.Physical and engineering sciences in medicine · 2026Article
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Authors and funding
8 authors.
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Abstract
In this study, we investigated the metal artifact reduction (MAR) performance of a deep learning (DL)-based technique in the evaluation of postoperative CT after total knee arthroplasty (TKA). For the development dataset, we collected CT scans from fifty patients without a metal prosthesis, and for the clinical test dataset, we collected CT scans from 44 patients with a previous history of TKA. We developed a DL-based knee MAR network (KMAR-Net) using 25,000 pairs of simulated images generated from 50 patients using the sinogram handling method. Regarding quantitative analysis, the area, mean attenuation, and standard deviation were calculated for Non-MAR, MAR algorithm for orthopedic implants (O-MAR), and KMAR-Net. For qualitative analysis, overall artifact, bone conspicuity, and soft tissue were compared using visual grading analysis. To additionally validate the feasibility of KMAR-Net under controlled conditions, a phantom study using a CTDI phantom with various metallic inserts and scanning parameters was conducted. KMAR-Net outperformed the projection-completion method regarding the area of dark streak artifacts, mean attenuation, and standard deviation within the artifacts. In the qualitative analysis, KMAR-Net was superior to O-MAR in the overall artifact and soft tissue evaluation, and one of the two readers evaluated it as superior for bone conspicuity (P = 0.080 for reader 1 and P < 0.001 for reader 2). In summary, DL-based KMAR-Net showed superior MAR performance in CT compared to the conventional projection-based method.
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