Chest CT with iterative reconstruction algorithms for airway stent evaluation in patients with malignant obstructive tracheobronchial diseases.

Abstract

The aim of the study was to investigate the image quality of low-dose CT images with different reconstruction algorithms including filtered back projection (FBP), hybrid iterative reconstruction (HIR), and iterative model reconstruction (IMR) algorithms by comparison of routine dose images with FBP reconstruction, in patients with malignant obstructive tracheobronchial diseases.In total, 60 patients (59 ± 9.3 years, 37 males) with airway stent who are randomly assigned into 2 groups (routine-dose [RD] and low-dose [LD] group, 30 for each) underwent chest CT on a 256-slice CT (RD-group 120 kV, 250 mAs, LD-group 120 kV, 120 mAs). Images were reconstructed with filtered back projection (FBP) algorithm in the RD group, whereas with FBP, HIR and IMR algorithms in the LD group. Effective radiation dose of both groups was recorded. Image-quality assessment was performed by 2 radiologists according to structure demarcation near stents, artifacts, noise, and diagnostic confidence using a 5-point scale (1 [poor] to 5 [excellent]). Image noise and CNR were measured.The effective radiation dose of LD group was reduced 52.7% compared with the RD group (10.8 mSv ± 0.58 vs 5.1 mSv ± 0.26, P = 0.00). LD-IMR images enabled lowest image noise and best subjective image quality scores of all 4 indices, when compared with RD images reconstructed with FBP (RD-FBP) images (all P < 0.05). LD images reconstructed with and with HIR (LD-HIR) images enabled higher score in subjective image quality of artifacts (P < 0.05), whereas it showed no difference in the other subjective image-quality indices and image noise. Significant higher image noise and lower score of subjective image quality were observed in LD-FBP images (all P < 0.05).Both IMR and HIR improved image quality of low-dose chest CT by comparison of routine dose images reconstructed with FBP. Meanwhile, IMR allows further image quality improvement than HIR.