Objective: We aimed to evaluate a new model-based iterative reconstruction (MBIRn) algorithm either with spatial resolution and noise reduction balance (MBIRSTND) or spatial resolution preference (MBIRRP20) for quantitative analysis of airway in low-dose chest computed tomography (CT) with a computer-aided detection (CAD) software, in comparison to adaptive statistical iterative reconstruction (ASIR) in routine-dose CT.
Methods: Thirty patients who underwent both the routine-dose (noise index [NI] = 14 HU) and low-dose (at 30% level with NI = 28 HU) CT examination for pulmonary disease were included. Image acquisition was performed with 120 kVp tube voltage and automatic tube current modulation. Routine-dose scans were reconstructed with ASIR, whereas low-dose scans were reconstructed with ASIR, MBIRSTND, and MBIRRP20. Airway dimensions of the right middle lobe bronchus from the four reconstructions were measured using CAD software. Two radiologists used a semiquantitative 5 scoring criteria (-2, inferior to; +2, superior to; -1 slightly inferior to; +1, slightly superior to; and 0, equal to ASIR in routine-dose CT) to rate the subjective image quality of MBIRSTND and MBIRRP20 of airway trees. The paired t test and Wilcoxon signed-rank test were used for statistical comparison.
Results: The low-dose CT provided 70.76% dose reduction compared to the routine-dose CT (0.88 ± 0.83 mSv vs 3.01 ± 1.89 mSv). MBIRSTND and MBIRRP20 with low-dose CT provided longer bronchial length measurements and were better in measurement variability and continuity and completeness of bronchial walls than ASIR in routine-dose CT (P < .05). MBIRSTND was better for subjective noise and MBIRRP20 for showing distal branches. CONCLUSIONS: MBIRSTND and MBIRRP20 algorithms provide better airway quantification at 30% of the radiation dose, compared to ASIR at routine-dose CT.
Keywords: Model-based iterative reconstruction; X-ray computed; computer-aided detection; radiation dosage; tomography.
Copyright © 2018. Published by Elsevier Inc.