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CT Lilun yu yingyong yanjiu
By Yongzhi HU, Xuan SHA, Yuxia TANG and 3 others.
To investigate the performance of an artificial-intelligence–based metal artifact correction algorithm (AIMAC) on pelvic computed tomography (CT) after total hip arthroplasty.
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This retrospective study included data from 34 patients with a history of hip arthroplasty who underwent pelvic CT at our institution between September 2022 and December 2024. Raw data were reconstructed using three methods: routine reconstruction (Karl 3D, level 5), conventional metal artifact correction (MAC), and AIMAC. For subjective evaluation, two radiologists independently scored overall image quality using a five-point scale in a blinded manner, and inter-reader agreement was assessed using the Kappa statistic. For objective evaluation, regions of interest were drawn to calculate CT-number differences between the artifact-affected and reference slices (ΔCTmuscle and ΔCTfat), standard deviation (SDartifact and SDreference), the metal artifact index (MAI), and the contrast-to-noise ratio (CNR). Overall comparisons among the three reconstructions were performed using repeated-measures analysis of variance or the Friedman test, according to normality. Pairwise comparisons were conducted using the paired t-test or the Wilcoxon signed-rank test, with Holm–Bonferroni adjustment for multiple comparisons (P*).
Inter-reader agreement for overall image quality was good (Kappa: 0.866, 0.816, and 0.794). Overall subjective scores differed significantly among the three methods, with AIMAC scoring higher than MAC, and MAC scoring higher than routine reconstruction. For objective metrics, the ΔCTmuscle, ΔCTfat, SDartifact, MAI, and CNR all had significant differences among the three methods, with AIMAC outperforming MAC and routine reconstruction. For SDreference, MAC was lower than routine reconstruction and AIMAC, whereas no significant difference was observed between routine reconstruction and AIMAC.
For pelvic CT after hip arthroplasty, AIMAC more effectively reduced metal artifacts, improved CT-number stability, and enhanced overall image quality compared with routine reconstruction and conventional MAC.
Published in CT Lilun yu yingyong yanjiu. The abstract is shown as published and remains the work of its authors; read the full article at the source.
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