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 < 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.
Conclusion: 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.