Chest CT at a dose below 0.3 mSv: impact of iterative reconstruction on image quality and lung analysis. (March 2016)

• Record Type: Journal Article
• Authors: Nakajo, Chiaki; Heinzer, Susanne; Montandon, Stéphane; Dunet, Vincent; Bize, Pierre; Feldman, Andrei; Beigelman-Aubry, Catherine
• Is Part Of: Acta radiologica. Volume 57:Number 3(2016:Mar.)
• Page Start: 311
• Page End: 317
• Publication Date: 2016-03
• Subjects: Reduced dose chest computed tomography -- image quality -- iterative reconstruction -- pulmonary nodules; Radiology, Medical -- Periodicals; Radiography, Medical -- Periodicals; Radiotherapy -- Periodicals; 616.0757
• DOI: 10.1177/0284185115578469
• Languages: English
• ISSNs: 0284-1851

Abstract

Background: In chest computed tomography (CT), iterative reconstruction (IR) algorithms maintain diagnostic image quality (IQ) while significantly reducing the dose. Purpose: To evaluate the impact of IR on IQ of chest CT at effective doses below 0.3 mSv. Material and Methods: Twenty chest CT scans performed at effective dose below 0.3 mSv (CT1) were reconstructed varying three parameters: filtered back-projection and IR iDose 4 algorithms; 512 × 512 and 768 × 768 matrices; and sharp and soft kernels, thus generating eight series per patient. The qualitative evaluation of the IQ was performed by ranking series from 1 to 8 (8 corresponding to the highest rank) which was subsequently compared to quantitative assessment of IQ by using an appropriated merit formula. Intra- and inter-reader IQ ranking reliability was also evaluated using Cohen's kappa. Analysis of lung findings was finally compared between the best CT1 series and the reference CT (CT0). Results: The best series in terms of qualitative and quantitative IQ was obtained using IR, 512 2 matrix and soft kernel. The best CT1 series detected nodules greater than 4 mm with an almost perfect match with CT0. Conclusion: Chest CT performed at effective doses below 0.3 mSv may be used to confidently diagnose lesions greater than 4 mm using iDose 4, soft kernel and 512 × 512 matrix.