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 In this prospective single-center study, 68 patients underwent 100 kVp cerebral CT angiography with a 50% reduced Dose Right Index (DRI-15 vs. DRI-36). Identical raw datasets were reconstructed using IR (iDose⁴) 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.