Impact of dose reduction and iterative reconstruction algorithm on the detectability of pulmonary nodules by artificial intelligence

Abstract

PurposeThe purpose of this study was to assess whether the performances of an automated software for lung nodule detection with computed tomography (CT) are affected by radiation dose and the use of iterative reconstruction algorithm. Materials and methodsA chest phantom (Multipurpose Chest Phantom N1; Kyoto Kagaku Co. Ltd, Kyoto, Japan) with 15 pulmonary nodules was scanned with a total of five CT protocol settings with up to 20-fold dose reduction. All CT examinations were reconstructed with iterative reconstruction algorithms ADMIRE 3 and ADMIRE 5 and were then analyzed for the presence of pulmonary nodules with a fully automated computer aided detection software system (InferReadTM CT Lung, Infervision), which is based on deep neural networks. ResultsThe sensitivity of fully automated pulmonary nodule detection for ground-glass nodules at standard dose CT was greater (70.0%; 14/20; 95% CI: 51.6-88.4%) than at 10-fold and 20-fold dose reduction (30.0%; 6/20; 95% CI: 0.0%-62.5%). There were less false positive findings when ADMIRE 5 reconstruction was used (4.0 ± 2.8 [SD]; range: 2–6) instead of ADMIRE 3 reconstruction (25.0 ± 15.6 [SD]; range: 14–36). There was no difference in the sensitivity of detection of solid and subsolid nodules between standard dose (100%; 95% CI: 100–100%) and 10- and 20-fold reduced dose CT (92.5%; 95% CI: 83.8–100.0%). Image noise was significantly greater with ADMIRE 3 (81 ± 2 [SD] [range: 79–84]; 104 ± 3 [SD] [range: 101–107]; 114 ± 5 [SD] [range: 110–119]; 193 ± 10 [SD] [range: 183-203]; 220 ± 16 [SD] [range: 210–238]) compared to ADMIRE 5 (44 ± 2 [SD] [range: 42–46]; 60 ± 2 [SD] [range: 57–61]; 66 ± 1 [SD] [range: 65–67]; 103 ± 4 [SD] [range: 98–106]; 110 ± 1 [SD] [range: 109–111]), respectively in each of the five CT protocols. ConclusionThis phantom study suggests that dose reduction and iterative reconstruction settings have an impact on detectability of pulmonary nodules by artificial intelligence software and we therefore encourage adaption of dose levels and reconstruction methods prior to widespread implementation of fully automatic nodule detection software for lung cancer screening purposes.