Adaptive Statistical Iterative Reconstruction Technique for Pulmonary CT: Image Quality of the Cadaveric Lung on Standard- and Reduced-dose CT
Received 31 March 2010; accepted 19 May 2010. published online 16 July 2010. Corrected Proof
Rationale and Objectives
To evaluate thin-section computed tomography (CT) images of the lung reconstructed using adaptive statistical iterative reconstruction (ASIR) on standard- and reduced-dose CT.
Materials and Methods
Eleven cadaveric lungs were scanned by multidetector-row CT with two different tube currents (standard dose, 400 mA; reduced dose, 10 mA). The degree of ASIR was classified into six different levels: 0% (non-ASIR), 20%, 40%, 60%, 80%, and 100% (maximum-ASIR). The ASIR (20%, 60%, and 100%) images were compared with the ASIR (0%) images and assessed visually by three independent observers for image quality using a 7-point scale. The evaluation items included abnormal CT findings, normal lung structures, and subjective visual noise. The median scores assigned by the three observers were analyzed statistically. Quantitative noise was calculated by measuring the standard deviation in a circular region of interest on each selected image of ASIR (0%–100%).
Results
On standard-dose CT, the overall image quality significantly improved with increasing degree of ASIR (P ≤ .009, Wilcoxon signed-ranks test with Bonferroni correction). As ASIR increased, however, intralobular reticular opacities and peripheral vessels tended to be obscure. On reduced-dose CT, the overall image quality of ASIR (100%) was significantly better than that of ASIR (20%) (P ≤ .009). As ASIR increased, however, intralobular reticular opacities tended to be obscure. Using ASIR significantly reduced subjective and quantitative image noise on both standard- and reduced-dose CT (P < .001, Bonferroni/Dunn's method).
Conclusion
ASIR improves the image quality by decreasing image noise. Maximum-ASIR may be needed for improving image quality on highly reduced-dose CT. However, excessive ASIR may obscure subtle shadows.
ABSTRACT