OBJECTIVE: To evaluate the image quality and clinical acceptance of a deep learning reconstruction (DLR) algorithm compared to traditional iterative reconstruction (IR) algorithms. METHODS: CT acquisitions were performed with two phantoms and a total of nine dose levels. Images were reconstructed with two types of IR algorithms, DLR and filtered-back projection. Spatial resolution, image texture, mean noise value, and objective and subjective low-contrast detectability were compared. Ten senior radiologists evaluated the clinical acceptance of these algorithms by scoring ten CT exams reconstructed with the DLR and IR algorithms evaluated. RESULTS: Compared to MBIR, DLR yielded a lower noise and a higher low-contrast detectability index at low doses (CTDI ≤ 2.2 and ≤ 4.5 mGy, respectively). Spatial resolution and detectability at higher doses were better with MBIR. Compared to HIR, DLR yielded a higher spatial resolution, a lower noise, and a higher detectability index. Despite these differences in algorithm performance, significant differences in subjective low-contrast performance were not found (p ≥ 0.005). DLR texture was finer than that of MBIR and closer to that of HIR. Radiologists preferred DLR images for all criteria assessed (p < 0.0001), whereas MBIR was rated worse than HIR (p < 0.0001) in all criteria evaluated, except for noise (p = 0.044). DLR reconstruction time was 12 times faster than that of MBIR. CONCLUSION: DLR yielded a gain in objective detection and noise at lower dose levels with the best clinical acceptance among the evaluated reconstruction algorithms. KEY POINTS: • DLR yielded improved objective low-contrast detection and noise at lower dose levels. • Despite the differences in objective detectability among the algorithms evaluated, there were no differences in subjective detectability. • DLR presented significantly higher clinical acceptability scores compared to MBIR and HIR.