Abstract: Objective: (1) To evaluate the clinical utility of deep learning reconstruction (DLR) combined with metal artifact reduction (MAR) in improving vascular visualization and in mitigating metal artifacts associated with fixed dental prostheses in head and neck spectral computed tomography angiography (CTA). (2) To establish an optimized imaging protocol for the accurate diagnosis of head and neck pathologies in this patient cohort. Methods: A total of 27 patients with unilateral fixed dental prostheses who underwent head and neck spectral CTA were retrospectively included. Raw data from all dual-energy CTA examinations were reconstructed into four image datasets using four protocols based on the 120 kVp-like series: DLR without MAR, DLR with MAR (DLR+MAR), 50% Adaptive Statistical Iterative Reconstruction-V (ASiR-V) without MAR, and 50% ASiR-V with MAR (ASiR-V+MAR). Regions of interest (ROIs) were placed on the internal carotid artery (ICA) and adjacent soft tissues on the slices most severely affected by metal artifacts, carefully avoiding calcifications and atherosclerotic plaques, to measure the CT number and image noise (standard deviation; SD). The artifact index (AI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for each dataset and compared among the four groups. Subjective image quality was independently assessed by two board-certified radiologists who were blinded to the reconstruction protocols. All measurements were performed on identical anatomical slices across all four protocols, and the mean of three repeated measurements was adopted for the final analysis. One-way analysis of variance (ANOVA) was used to compare quantitative parameters among the four groups, and interobserver agreement for subjective scores was evaluated using the weighted Cohen’s kappa statistic. Results: Among the four groups, the DLR+MAR group yielded the highest mean SNR (12.64±7.53) and CNR (32.46±13.47), along with the lowest mean AI (35.71±24.13). One-way ANOVA revealed a statistically significant difference in CNR across the four groups, whereas no statistically significant differences were observed in SNR or AI (P = 0.766 and P = 0.664, respectively). For the subjective image quality assessment, the DLR+MAR group achieved the highest scores, which were significantly superior to those of the other three groups. The two observers showed excellent interobserver agreement (weighted kappa = 0.825). Conclusion: In spectral CT examinations, the combination of DLR and MAR can effectively mitigate metal artifacts associated with fixed dental prostheses and significantly improve the image quality of head and neck CTA, thereby demonstrating substantial clinical utility in routine practice.