Objective:To compare the differences in image quality and clinical benefits of deep learning image reconstruc-tion(DLIR),filtered back-projection(FBP),and adaptive statistical iterative reconstruction-veo(ASIR-V)in abdominal portal ve-nous phase CT images.Methods:Forty-five patients who underwent abdominal contrast-enhanced CT scans were enrolled,and 18 cases with decompensated liver cirrhosis were contained.The portal venous phase images were reestablished by FBP,30％ASIR-V,80％ASIR-V,and DLIR-H algorithms.The CT values and noise of the liver,spleen,splenic vein,portal vein,and left and right branches in each reconstructed image,as well as the signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were measured and compared.The subjective evaluations of each reconstructed image,including collateral vessels in 18 cases with decompensated liver cirrhosis.Results:There was no statistically significant difference in CT values among the four re-constructed image groups(P＞0.05).However,there were statistically significant differences in noise,SNR,and CNR.Compar-isons between FBP and 30％ASIR-V,as well as 80％ASIR-V and DLIR-H,showed no statistically significant differences in CNR and SNR values(adjusted P＜0.008).There were no statistically significant differences in SD values between 80％ASIR-V and DLIR-H algorithms(adjusted P＜0.008),but differences were observed in other comparisons.Subjective evaluation showed statistically significant differences in overall quality,contrast,and distortion/artifacts of DLIR images compared to other groups(adjusted P＜0.008).Only image noise in DLIR did not show significant differences compared to 80％ASIR-V(adjusted P≥0.008).The delineation of vascular structures and clarity in DLIR images showed significant differences compared to other groups(adjusted P＜0.008),with no significant differences in noise compared to 80％ASIR-V.Conclusion:The DLIR algorithm offersadvantages in reducing noise and improving image quality of abdominal CT images,particularly in the visualization of small vascular structures in patients with decompensated liver cirrhosis.This reconstruction algorithm may potentially provide more information for accurates patients diagnosis and risk assessment.
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