Effect of a New Model-Based Reconstruction Algorithm for Evaluating Early Peripheral Lung Cancer With Submillisievert Chest Computed Tomography

Objective: The aim of this study was to compare a new model-based iterative reconstruction algorithm with either spatial and density resolution balance (MBIRSTND) or spatial resolution preference (MBIRRP20) with the adaptive statistical iterative reconstruction (ASIR) in evaluating early small peripheral lung cancer (SPLC) with submillisievert chest computed tomography (CT). Methods: Low-contrast and spatial resolutions were assessed in a phantom and with 30 pathologically confirmed SPLC patients. Images were reconstructed using 40% ASIR, MBIRSTND, and MBIRRP20. Computed tomography value and image noise were measured by placing the regions of interest on back muscle and subcutaneous fat at 3 levels. Two radiologists used a 4-point scale (1, worst, and 4, best) to rate subjective image quality in 3 aspects: image noise, nodule imaging signs, and nodule internal clarity. Results: The phantom study revealed an improved detectability of low-contrast targets and small objects fo rMBIR(STND) andMBIR(RP20) compared with ASIR. The effective dose for patient scans was 0.88 +/- 0.83 mSv. There was no significant difference in CT value between the 3 reconstructions (P > 0.05), but MBIRSTND and MBIRRP20 significantly reduced image noise compared with ASIR (P < 0.05): 15.69 +/- 1.83 HU and 29.97 +/- 3.84 HU versus 51.06 +/- 11.02 HU in the back muscle, and 15.96 +/- 3.07 HU and 27.37 +/- 3.88 HU versus 38.04 +/- 8.87 HU in subcutaneous fat, respectively. Among the 3 reconstructions, MBIRSTND was the best in reducing image noise and identifying the internal compositions of cancer nodules, and MBIRRP20 was the best in analyzing the internal and external signs of pulmonary nodules. Conclusions: Submillisievert chest CT reconstructed with MBIRSTND andMBIRRP20 provides superior images for the detailed analyses of SPLC compared with ASIR.