Hybrid Iterative Reconstruction Algorithm Improves Image Quality in Craniocervical CT Angiography

OBJECTIVE. The purpose of this study was to evaluate the potential of a hybrid iterative reconstruction algorithm for improving image quality in craniocervical CT angiography (CTA) and to assess observer performance. SUBJECTS AND METHODS. Thirty patients (mean age, 58 years; range 16–80 years) underwent standard craniocervical CTA (volume CT dose index, 6.8 mGy, 2.8 mSv). Images were reconstructed using both filtered back projection (FBP) and a hybrid iterative reconstruction algorithm. Five neuroradiologists assessed general image quality and delineation of the vessel lumen in seven arterial segments using a 4-grade scale. Interobserver and intraobserver variability were determined. Mean attenuation and noise were measured and signal-to-noise and contrast-to-noise ratios calculated. Descriptive statistics are presented and data analyzed using linear mixed-effects models. RESULTS. In pooled data, image quality in iterative reconstruction was graded superior to FBP regarding all five quality criteria (p < ...