Ultra-low-dose CT of the lung: effect of iterative reconstruction techniques on image quality.
OBJECTIVE
To compare quality of ultra-low-dose thin-section computed tomography (CT) images of the lung reconstructed using model-based iterative reconstruction (MBIR) and adaptive statistical iterative reconstruction (ASIR) to filtered back projection (FBP) and to determine the minimum tube current-time product on MBIR images by comparing to standard-dose FBP images.


METHODS
Ten cadaveric lungs were scanned using 120 kVp and four different tube current-time products (8, 16, 32, and 80 mAs). Thin-section images were reconstructed using MBIR, three ASIR blends (30%, 60%, and 90%), and FBP. Using the 8-mAs data, side-to-side comparison of the four iterative reconstruction image sets to FBP was performed by two independent observers who evaluated normal and abnormal findings, subjective image noise, streak artifact, and overall image quality. Image noise was also measured quantitatively. Subsequently, 8-, 16-, and 32-mAs MBIR images were compared to standard-dose FBP images. Comparisons of image sets were analyzed using the Wilcoxon signed rank test with Bonferroni correction.


RESULTS
At 8 mAs, MBIR images were significantly better (P < .005) than other reconstruction techniques except in evaluation of interlobular septal thickening. Each set of low-dose MBIR images had significantly lower (P < .001) subjective and objective noise and streak artifacts than standard-dose FBP images. Conspicuity and visibility of normal and abnormal findings were not significantly different between 16-mAs MBIR and 80-mAs FBP images except in identification of intralobular reticular opacities.


CONCLUSIONS
MBIR imaging shows higher overall quality with lower noise and streak artifacts than ASIR or FBP imaging, resulting in nearly 80% dose reduction without any degradations of overall image quality.
Department of Diagnostic Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305; Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan. Electronic address: masayana@stanford.edu.
Masahiro
M
Yanagawa
Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Tomoko
T
Gyobu
Department of Diagnostic Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305.
Ann N
AN
Leung
Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Misa
M
Kawai
Department of Radiology, Osaka Rosai Hospital, Sakai, Osaka, Japan.
Yutaka
Y
Kawata
Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Hiromitsu
H
Sumikawa
Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Osamu
O
Honda
Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Noriyuki
N
Tomiyama
D016477:Artifacts; D002102:Cadaver; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D061330:Multidetector Computed Tomography / Q000379:methods*; D015588:Observer Variation; D011829:Radiation Dosage; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
Radiation dose reduction; adaptive statistical iterative reconstruction; filtered back projection; image quality; model-based iterative reconstruction