Effect of New Model-Based Iterative Reconstruction on Quantitative Analysis of Airway Tree by Computer-Aided Detection Software in Chest Computed Tomography.

ABSTRACT

OBJECTIVE: Compared the performance of computer-aided detection (CAD) software for quantitative analysis of airway using computed tomography (CT) images reconstructed with versions of model-based iterative reconstruction (MBIR) that either balances spatial and density resolution (MBIRSTND) or prefers spatial resolution (MBIRRP20), and adaptive statistical iterative reconstruction (ASIR) with lung kernel. METHODS: Thirty patients were included who were scanned for pulmonary disease using a routine dose multidetector CT system. Data were reconstructed with ASIR, MBIRSTND, and MBIRRP20. Airway dimensions from the 3 reconstructions were measured using an automated, quantitative CAD software designed to segment and quantify the bronchial tree automatically using a skeletonization algorithm. For each patient and reconstruction algorithm, the right middle lobe bronchus was selected as a representative for measuring the bronchial length of the matched airways. Two radiologists used a semiquantitative 5-point scale to rate the subjective image quality of MBIRSTND and MBIRRP20 reconstructions on airway trees analysis. RESULTS: Algorithm impacts the measurement variability of bronchus length in chest CT, MBIRRP20 were the best, whereas ASIR were the worst (P < 0.05). In addition, the optimal reconstruction algorithm was found to be MBIRSTND for the airway trees being assessed about subjective noise and MBIRRP20 about bronchial end shows, and there were no significant differences in the continuity and completeness of bronchial wall, whereas ASIR performed inferiorly compared with them (P < 0.05). CONCLUSIONS: Compared with ASIR, MBIRSTND, and MBIRRP20 from MBIRn algorithm potentially allow the desired airway quantification accuracy to be achieved on the performance of CAD, especially for MBIRRP20.