Objective To demonstrate the feasibility of high-resolution computed tomography(HRCT)reconstructed with a model-based iterative reconstruction (MBIR)for evaluating early peripheral lung cancer (≤3 cm),by comparing image quality obtained from MBIR,filtered back proj ection reconstruction(FBP)and state of the art adaptive statistical iterative reconstruction(ASIR)algorithm respectively.Methods A total of 30 patients confirmed with lung cancer by postoperative pathology were enrolled in the study.A chest phantom was also used to evaluate image noise,spatial resolution and density resolution.Both patients and chest phantom were received HRCT,and the images were reconstructed using FBP,ASIR(40% ASIR and FBP mix)and MBIR.The objective CT value, standard deviation(SD)and signal noise ratio(SNR)were measured.Two radiologists used a semi-quantitative to rate subjective image quality of lung nodules.Results There was no significant difference in CT value between the three reconstruction algorithms (P>0.05).But significant improvements in objective image noise were observed in MBIR compared with FBP and ASIR (P<0.05):including the SD value in back muscle [(12.63±1.70)with MBIR vs (31.58±5.21)with FBP and (24.55±4.14)with ASIR],and in subcutaneous fat [(12.77±2.53)vs (24.39±5.08)and (19.20±4.11)].Subjective image noise of the three group were also significantly difference:FBP with lowest subjective noise score;and MBIR with highest subjective noise score.The sharpness of small vessels and bronchi and diagnostic acceptability with MBIR were significantly better than with FBP and ASIR (P< 0.05).Conclusion Lung HRCT reconstructed with MBIR provides diagnostically more acceptable images for the detailed analyses of peripheral lung cancer compared with FBP and ASIR.