Intelligent Organ Recognition Technology Combined With an Artificial Intelligence Iterative Reconstruction Algorithm in Contrast-enhanced CT of the Chest, Abdomen, and Pelvis.

RATIONALE AND

OBJECTIVES

To evaluate the application value of intelligent organ recognition technology combined with the artificial intelligence iterative reconstruction (AIIR) algorithm in contrast-enhanced chest-abdomen-pelvis (CAP) computed tomography (CT) and to determine the optimal reconstruction level for the AIIR algorithm. MATERIALS AND

METHODS

130 patients were randomly assigned to groups A and B. Group A was the conventional-dose group, with images reconstructed using the Karl iterative reconstruction algorithm. Group B utilized intelligent organ recognition technology, with images reconstructed using the AIIR algorithm. After comparing the image quality of group B and selecting the optimal reconstruction level for the AIIR algorithm, the image quality, radiation dose, and average tube current between groups A and B were compared.

RESULTS

The optimal reconstruction level for soft-tissue window images in group B was AIIR level 4, and for lung window images was AIIR level 1. The mean values of the signal-to-noise ratio, contrast-to-noise ratio, figure of merit, and subjective scores for the optimal reconstruction level images in group B were higher than those in group A (p < 0.001). Compared to group A, the mean effective dose, the tube current for the chest and abdominopelvic regions in group B were decreased by 36%, 45%, 33% respectively (p < 0.001).

CONCLUSION

The application of intelligent organ recognition technology, combined with the AIIR algorithm, significantly improved the image quality of contrast-enhanced CAP-CT while reducing radiation dose. AIIR level 4 should be the optimal reconstruction level for soft-tissue window images, and AIIR level 1 should be the optimal reconstruction level for lung window images.