Abstract: In this study, we systematically evaluated the iodine quantification accuracy and image noise suppression capabilities of a deep learning reconstruction algorithm (ClearInfinity, CI) under 60-kVp ultra-low tube voltage computed tomography (CT) conditions, comparing it with filtered backprojection (FBP) and hybrid iterative reconstruction (ClearView, CV). A CT performance phantom containing inserts with varying iodine concentrations (40, 28, 22, 12, 6, 3, and 2 mg/mL) was scanned six times (60 kVp, 386 mA) using a NeuViz Epoch Elite CT scanner. Images were reconstructed using FBP, CV (at 20%, 40%, 60%, and 80% intensities), and CI (at equal intensity). CT values, image noise (standard deviation SD), and coefficients of variation of the iodine inserts were measured. Absolute percentage bias (APB) and contrast-to-noise ratio (CNR) were calculated. Results show that CI achieved optimal quantitative accuracy at 40% reconstruction intensity and provided the strongest noise reduction at 80%, with a maximum SD reduction of up to 79.59%. At all intensity levels, CI significantly outperformed CV and FBP in terms of APB, noise suppression (especially at low iodine concentrations), measurement stability, and CNR. These findings confirm that CI is an effective solution for producing low-noise, low-bias, and highly stable images in ultra-low-dose CT.