Abstract: Objective: Exploration of the application of a deep-learning reconstruction algorithm (DLIR) and adaptive statistical iterative reconstruction (ASIR-V) based on the combination of low radiation dose and low iodine contrast agent in liver CT enhancement. Methods: A total of 82 patients who underwent abdominal enhanced CT were prospectively selected and randomly separated into groups A and B. Group A (control group) received a conventional dose (tube voltage 120 kVp; iodine contrast 85mL) and inferior portal image reconstruction was applied using 30%, 50%, and 70% ASIR-V (AV 30, AV 50, AV 70). In Group B (experimental group), image reconstruction was based on medium- and high-intensity deep learning (DLIR-M, DLIR-H). Image noise (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), lesion contrast-to-noise ratio (LLR), quality factor (FOM), effective radiation dose (ED), and iodine intake were calculated. Subjective image quality results were obtained for different reconstruction methods at different doses. No significant differences in gender, age, and BMI between groups A and B were found. For 38.40% effective dose and 23.53% reduction in the contrast agent dosage, no significant SD differences were found between DLIR-M and AV 50, DLIR-H and liver parenchyma and AV 70. Only DLIR-M and AV 50 in the portal SNR were not statistically significant. No significant differences were found between DLIR-M and AV 70 in liver parenchyma and portal CNR. Concerning LLR and FOM, no significant differences were found between DLIR-M and AV 70. For various subjective image quality assessments, DLIR at double low doses outperformed AVIR-V, especially DLIR-H. Conclusions: DLIR can improve image quality and the ability to detect liver low contrast lesions at “double low” (low radiation dose low contrast) compared to ASIR-V.