Observer Sensitivity for Detection of Pulmonary Nodules in Ultra-Low Dose Computed Tomography Protocols Using a Third-Generation Dual-Source CT with Ultra-High Pitch-A Phantom Study

• Main Authors: Natascha Leitzig (Author), Sonja Janssen (Author), Hany Kayed (Author), Stefan O. Schönberg (Author), Hans Scheffel (Author)
• Formato: Libro
• Publicado: MDPI AG, 2023-01-01T00:00:00Z.
• Subjects: article
• Acceso en liña: Connect to this object online.
• Notes: 10.3390/anatomia2010002; 2813-0545
• Keywords: anthropomorphic chest phantom; detectability; iterative reconstruction; pulmonary nodules; ultra-low-dose CT; Human anatomy; QM1-695
• Source: Anatomia, Vol 2, Iss 1, Pp 15-26 (2023)

This study evaluates ultra-low-dose computed tomography (ULDCT) protocols concerning the detectability of pulmonary nodules. The influence of tube current settings, kernels, strength levels of third-generation iterative reconstruction algorithms, and pitch was investigated. A chest phantom with artificial spherical nodules of different densities and diameters was examined with a third-generation dual-source CT. Scanning and post-processing protocols, tube current levels, and ultra-high and non-high pitch modes were applied. Images were reconstructed with filtered back-projection (FBP) or advanced model-based iterative reconstruction (ADMIRE) algorithms. Sharp (Bl57) or medium-soft (Br36) convolution kernels were applied. The reading was performed by an experienced and an inexperienced reader. The highest observer sensitivity was found using a non-high pitch protocol at tube currents of 120 mAs and 90 mAs with the sharp kernel and iterative reconstruction level of 5. Non-high pitch protocols showed better detectability of solid nodules. Combinations with the medium-soft kernel achieved slightly higher observer sensitivity than with the sharp kernel. False positives (FP) occurred more often for subsolid nodules, at a tube current level of 120 mAs, and with the sharp kernel. A tube current level of 90 mAs combined with the highest iterative reconstruction level achieved the highest accuracy in lung nodule detection regardless of size, density, and reader experience.