Hybrid Iterative Reconstruction Algorithm Improves Image Quality in Craniocervical CT Angiography

Abstract

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. 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. In pooled data, image quality in iterative reconstruction was graded superior to FBP regarding all five quality criteria (p < 0.0001), with the greatest improvement observed in the vertebral arteries. Iterative reconstruction resulted in elimination of arterial segments graded poor. Interobserver percentage agreement was significantly better (p = 0.024) for iterative reconstruction (69%) than for FBP (66%) but worse than intraobserver percentage agreement (mean, 79%). Noise levels, signal-to-noise ratio, and contrast-to-noise ratio were significantly (p < 0.001) improved in iterative reconstruction at all measured levels. The iterative reconstruction algorithm significantly improves image quality in craniocervical CT, especially at the thoracic inlet. Despite careful study design, considerable interobserver and intraobserver variability was noted.