Comparison of precise imaging and iterative reconstruction techniques at low doses using the dose right index in 100-kVp cerebral CT angiography

BACKGROUND: Artificial intelligence (AI)-driven image reconstruction has shown potential for enhancing image quality in CT angiography while reducing radiation exposure. However, reproducibility and methodological transparency are essential for real-world adoption. PURPOSE: To compare image quality and radiation dose of cerebral CT angiography reconstructed with conventional iterative reconstruction (IR) and AI-based Precise Imaging (PI) at 100 kVp using a reduced Dose Right Index (DRI-15). METHODS: ) and PI. Quantitative analysis included attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), while two blinded radiologists independently performed qualitative assessment using a 5-point Likert scale. Radiation dose metrics (CTDIvol, DLP, effective dose) were recorded. Standardized acquisition protocols, fixed contrast injection, and consistent ROI placement ensured reproducibility. RESULTS: PI reconstructions yielded significantly lower noise and higher SNR and CNR than IR (all p < 0.001; r = 0.61-0.71), with no difference in vascular attenuation (p = 0.118). Qualitative scores were superior for PI across all criteria (p < 0.01), while inter-rater agreement was substantial for both methods, slightly higher with IR. The median effective dose was 0.785 mSv (IQR 0.71-0.90). CONCLUSION: AI-based PI reconstruction enhances image quality without increasing radiation dose or altering vascular attenuation, consistently outperforming IR and supporting its use in low-dose cerebral CT angiography, especially in dose-sensitive patients.