Abstract: Objective: To explore the combination of spectral computed tomography (CT) monochromatic imaging with a metal artifact reduction (MAR) algorithm and deep learning reconstruction (DLR). We also aimed to evaluate the image quality improvements in computed tomography angiography (CTA) of the lower extremity arteries after total hip arthroplasty (THA) using the DLR algorithm. Methods: A total of 24 patients who underwent dual-energy CTA examination of the lower-extremity arteries after hip replacement were retrospectively enrolled. The original dual-energy CTA data were reconstructed using four schemes: group A (conventional mixed-energy images), group B (conventional image+MAR), group C (conventional mixed-energy image+DLR), and group D (single energy+MAR+DLR). The subjective scores (5-point scale) of the four groups of images were compared with the objective parameters (CT value, noise (SD), signal-to-noise ratio SNR, contrast-to-noise ratio CNR, and artifact index AI) of the femoral artery and adjacent subcutaneous fat at the metal artifact level. Analysis of variance and the weighted Kappa test were used for statistical comparisons. Results: Except for SNR, the differences in the other objective indices among the four groups were significant. In group D, AI decreased with increasing keV (P < 0.001); however, vessel enhancement, SNR, and CNR decreased with increasing keV. The 60 keV images had the highest overall score (4.89 ± 0.32), which achieved the best balance between vessel display and artifact suppression. When the energy level was >70 keV, the changes in the AI value and the vessel noise plateaued. Conclusion: The triple scheme of spectral CT monochromatic energy imaging (60 keV), MAR algorithm, and DLR technology can effectively suppress metal artifacts and improve the definition of vascular structures after THA, which has certain clinical application value.