Adaptive Statistical Iterative Reconstruction Technique for Pulmonary CT: Image Quality of the Cadaveric Lung on Standard- and Reduced-Dose CT

摘要: Rationale and Objectives To evaluate thin-section computed tomography (CT) images of the lung reconstructed using adaptive statistical iterative reconstruction (ASIR) on standard- reduced-dose CT. Materials Methods Eleven cadaveric lungs were scanned by multidetector-row CT with two different tube currents (standard dose, 400 mA; reduced 10 mA). The degree ASIR was classified into six levels: 0% (non-ASIR), 20%, 40%, 60%, 80%, 100% (maximum-ASIR). (20%, 100%) compared (0%) assessed visually three independent observers for image quality a 7-point scale. evaluation items included abnormal findings, normal structures, subjective visual noise. median scores assigned analyzed statistically. Quantitative noise calculated measuring standard deviation in circular region interest each selected (0%–100%). Results On standard-dose CT, overall significantly improved increasing ( P ≤ .009, Wilcoxon signed-ranks test Bonferroni correction). As increased, however, intralobular reticular opacities peripheral vessels tended to be obscure. (100%) better than that (20%) .009). Using quantitative both Conclusion improves decreasing Maximum-ASIR may needed improving highly However, excessive obscure subtle shadows.