Inter-observer agreement and image quality of model-based algorithm applied to the Coronary Artery Disease-Reporting and Data System score

Purpose To evaluate the inter-observer agreement of the CAD-RADS reporting system and compare image quality between model-based iterative reconstruction algorithm (MBIR) and standard iterative reconstruction algorithm (IR) of low-dose cardiac computed tomography angiography (CCTA). Methods One-hundred-sixty patients undergone a 256-slice MDCT scanner using low-dose CCTA combined with prospective ECG-gated techniques were enrolled. CCTA protocols were reconstructed with both MBIR and IR. Each study was evaluated by two readers using the CAD-RADS lexicon. Vessels enhancement, image noise, signal-to-noise (SNR), and contrast-to-noise (CNR) were computed in the axial native images, and inter-observer agreement was assessed. Radiation dose exposure as dose-length product (DLP) and effective dose were finally reported. Results The reliability analysis between the two readers was almost perfect for all CAD-RADS standard categories. Moreover, a significantly higher value of subjective qualitative analysis, SNR, and CNR in MBIR images compared to IR were found, due to a lower noise level (all p < 0.05). The mean DLP measured was 63.9 mGy*cm, and the mean effective dose was 0.9 mSv. Conclusion Inter-observer agreement of CAD-RADS was excellent confirming the importance, the feasibility, and the reproducibility of the CAD-RADS scoring system for CCTA. Moreover, lower noise and higher image quality with MBIR compared to IR were found. Implications for practice MBIR, by reducing noise and improving image quality, can help a better assessment of CAD-RADS, in comparison with standard IR algorithm.

Automated summary

This study evaluated the inter-observer agreement of the CAD-RADS reporting system and compared the image quality between MBIR and IR in low-dose CCTA. The results showed excellent inter-observer agreement for CAD-RADS and better image quality with MBIR. This implies that MBIR can help improve the evaluation of CAD-RADS.