COMPARATIVE STUDY
JOURNAL ARTICLE

Adaptive iterative dose reduction using 3D processing for reduced- and low-dose pulmonary CT: comparison with standard-dose CT for image noise reduction and radiological findings

Yoshiharu Ohno, Daisuke Takenaka, Tomonori Kanda, Takeshi Yoshikawa, Sumiaki Matsumoto, Naoki Sugihara, Kazuro Sugimura
AJR. American Journal of Roentgenology 2012, 199 (4): W477-85
22997397

OBJECTIVE: The purpose of this study was to determine the utility of adaptive iterative dose reduction using 3D processing (AIDR 3D) for image noise reduction and assessment of radiologic findings obtained with reduced- and low-dose chest CT in patients with various pulmonary diseases.

SUBJECTS AND METHODS: Chest CT examinations at three different tube current settings and using 16- and 64-MDCT scanners were performed for 37 patients. Standard-dose (150 mAs) data were reconstructed as thin-section CT without AIDR 3D, and low-dose (25 mAs) and reduced-dose (50 mAs) data were reconstructed as thin-section CT without and with AIDR 3D. To compare image quality, image noises at all CT doses were quantitatively assessed by region of interest measurements. For comparison of radiologic finding assessments, likelihoods of occurrence of emphysema, ground-glass opacity, reticular opacity, bronchiectasis, honeycomb pattern, and nodules were evaluated on a 5-point scale. Then, image noise and agreements of radiologic findings between standard-dose CT and others were statistically evaluated.

RESULTS: The image quality scores of reduced- and low-dose CT without AIDR 3D were significantly lower than those of both protocols with AIDR 3D and standard-dose CT (p<0.05). All intermethod agreements for emphysema, ground-glass opacity, bronchiectasis, honeycomb pattern, and nodules, except for those observed on low-dose CT without AIDR 3D, were almost perfect (κ>0.81).

CONCLUSION: AIDR 3D is useful for image noise reduction and assessment of radiologic findings obtained with reduced- and low-dose CT for patients with various pulmonary diseases.

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