Objective <p>To evaluate the impact of combining a second-generation super-resolution deep learning–based reconstruction (DLR2) with a metal artifact reduction (MAR) algorithm in CT images of patients with metal hip implants by assessing both quantitative metrics and qualitative reader ratings.</p> Materials and methods <p>This retrospective study included 40 patients (30 females; age range, 54–93 years) with metal hip implants. Images were reconstructed using DLR2, a first-generation DLR (DLR1), and a conventional hybrid iterative reconstruction (HIR), each combined with MAR. Images without MAR were also reconstructed using DLR2 (DLR2-only). Standard deviations (SDs) of attenuation in the regions of interest (ROI) over the bladder, gluteus maximus muscle, and gluteal fat were recorded. Artifact indices for muscle (AI_muscle) and fat (AI_fat) were also calculated. Three radiologists independently assessed the depiction of pelvic structures (femoral artery, bladder, rectum, uterus/prostate), artifact reduction, and diagnostic usability with 5-point scores. For statistical analysis, the Wilcoxon signed-rank test was used with the Holm correction for multiple comparisons.</p> Results <p>DLR2 + MAR showed significantly lower SDs than DLR1 + MAR, HIR + MAR, and DLR2-only across all three ROIs (<i>p</i> &lt; 0.01–0.02). For both AI_muscle and AI_fat, DLR2 + MAR had the lowest values, though differences with DLR1 + MAR were not significant (<i>p</i> = 0.80, 0.11). DLR2 + MAR showed significantly higher scores for the depiction of the femoral artery and rectum compared with other + MAR reconstruction methods (<i>p</i> &lt; 0.01–0.04), with no significant differences in the remaining categories (<i>p</i> = 0.08–0.91). Inter-rater agreement ranged from 0.67 to 0.77, indicating substantial agreement.</p> Conclusion <p>Combining DLR2 and MAR improves image quality and visualization of pelvic structures.</p>