Carotid DECTA in patients with dental hardware: Impact of deep learning image reconstruction and metal artifact reduction

To evaluate the effectiveness of deep learning image reconstruction (DLIR) with metal artifact reduction (MAR) (DLIR-MAR) for improving carotid dual-energy CT angiography (DECTA) in patients with dental hardware. This retrospective study included 49 patients with dental hardware who underwent carotid DECTA. Virtual monochromatic images (VMIs) were reconstructed with DLIR-MAR, adaptive statistical iterative reconstruction-Veo (ASIR-V), and ASIR-V with MAR (ASIRV-MAR) at 40-keV and 50-keV energy levels. We quantitatively compared image noise, CT attenuation, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and artifact index (AI) among different reconstructions, and performed a qualitative assessment of internal carotid artery (ICA) visualization affected by metal artifacts and overall image quality using a 5-point scale. Statistical analyses used repeated measures ANOVA or Friedman tests, with paired t-tests or Wilcoxon signed-rank tests as appropriate. Both ASIRV-MAR and DLIR-MAR reduced metal artifacts compared with ASIR-V, with lower AI and higher vascular visualization scores ( P < 0.001). Compared to ASIRV-MAR, DLIR-MAR reduced image noise at both keV levels ( P < 0.001). DLIR-MAR 40-keV VMIs exhibited comparable image noise ( P > 0.05), higher CT attenuation, SNR, CNR, and overall image quality compared with ASIRV-MAR 50-keV VMIs ( P < 0.05). Although DLIR-MAR 40-keV VMIs had higher AI values than ASIRV-MAR 50-keV VMIs ( P < 0.001), this did not significantly affect ICA visualization ( P > 0.05). DLIR reconstructed with MAR improves carotid DECTA images in patients with dental hardware, reducing image noise in lower-keV VMIs to capture their contrast enhancement benefits. • Metal artifact reduction (MAR) significantly reduces the metal artifacts in carotid dual-energy CT angiography (DECTA) images of patients with dental hardware. • Deep learning image reconstruction (DLIR) with MAR further reduces image noise in 40-keV virtual monochromatic images (VMIs) compared to iterative reconstruction with MAR. • DLIR with MAR ensures the use of 40-keV VMIs to further improve contrast enhancement for carotid DECTA.