Abstract: Objective: This study aimed to investigate the effect of ultra-high-resolution (UHR) detector computed tomography (CT) combined with a deep learning reconstruction algorithm (ClearInfinity (CI)) on cranial CT image quality and its potential for radiation dose reduction. Methods: A NeuViz Epoch Elite CT scanner was used to scan a Catphan® 600 phantom (with CTDI_vol set to 50, 37.5, and 25 mGy) and three rhesus monkeys (CTDI_vol=50 mGy). The collimation width was 128×0.3125 mm. Images were reconstructed using filtered back projection (FBP), adaptive iterative reconstruction (ClearView, CV30% and CV60%), and deep learning reconstruction (CI30% and CI60%). Image quality was evaluated using objective metrics, such as modulation transfer function (MTF), contrast-to-noise ratio (CNR), and artifact severity, as well as double-blind subjective scoring on a 5-point scale. Statistical analyses were then performed. Results: (i) Phantom experiments: At all dose levels, the CNR increased significantly with higher reconstruction levels (P < 0.05), with the CI60% images showing a significantly higher CNR than the other algorithms. At 25 mGy, the CNR of CI60% was comparable to that of FBP at 50 mGy, and no significant decrease was observed for MTF_10% or MTF_50% (P > 0.05). (ii) Animal experiments: At the centrum semiovale level, the CNR of the CI60% images was significantly higher than that obtained with other algorithms (P < 0.05), and artifacts tended to decrease with increasing iteration levels. Inter-observer agreement for image quality assessment was good (Kappa≥0.75). Overall, the subjective scores increased with higher CV/CI levels, with CI60% achieving the highest scores. Conclusion: In UHR detector CT, deep learning reconstruction can improve cranial CT image contrast and reduce noise and artifacts without compromising high-contrast spatial resolution, showing significant potential for radiation dose reduction and demonstrating good clinical application value.