Prospective evaluation of thoracic diseases using a compact flat-panel detector spiral computed tomographic scanner.

Objective: To prospectively evaluate the image quality and diagnostic performance of a compact flat-panel detector (FD) scanner for thoracic diseases compared to a clinical CT scanner.
Materials and Methods: The institutional review board approved this single-center prospective study, and all participants provided informed consent. From December 2020 to May 2021, 30 patients (mean age, 67.1 ± 8.3 years) underwent two same-day low-dose chest CT scans using clinical state-of-art and compact FDCT scanners. Image quality was assessed visually and quantitatively. Two readers evaluated the diagnostic performance for nodules, parenchymal opacifications, bronchiectasis, linear opacities, and pleural abnormalities in 40 paired CT scans. The other 20 paired CT scans were used to examine the agreement of semi-quantitative CT scoring regarding bronchiectasis, bronchiolitis, nodules, airspace consolidations, and cavities.
Results: FDCT images had significantly lower visual image quality than clinical CT images (all p  < 0.001). The two CT image sets showed no significant differences in signal-to-noise and contrast-to-noise ratios (56.8 ± 12.5 vs. 57.3 ± 15.2; p  = 0.985 and 62.9 ± 11.7 vs. 60.7 ± 16.9; p  = 0.615). The pooled sensitivity was comparable for nodules, parenchymal opacifications, linear opacities, and pleural abnormalities ( p  = 0.065-0.625), whereas the sensitivity was significantly lower in FDCT images than in clinical CT images for micronodules ( p  = 0.007) and bronchiectasis ( p  = 0.004). The specificity was mostly 1.0. Semi-quantitative CT scores were similar between the CT image sets ( p  > 0.05), and intraclass correlation coefficients were around 0.950 or higher, except for bronchiectasis (0.869).
Conclusion: Compact FDCT images provided lower image quality but comparable diagnostic performance to clinical CT images for nodules, parenchymal opacifications, linear opacities, and pleural abnormalities.
(© 2022 The Authors.)