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.