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Image Quality Assessment of Artificial Intelligence Iterative Reconstruction for Low-dose Bronchial Artery CTA in
Kunyao Li1, Dan Liu1, Fei Liu1
1Department of Radiology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China.
Objectives:
To investigate the feasibility and image quality of artificial intelligence iterative reconstruction (AIIR) for computed tomography angiography (CTA) of the bronchial artery (BA) with a reduced radiation dose and contrast agent dosage.
Materials And Methods:
A total of 110 hemoptysis patients were prospectively enrolled for bronchial artery CTA (BA-CTA) and were randomly divided into 2 groups. Routine-dose group (group A, n=55) used a routine CTA protocol (tube voltage: 120 kVp; contrast dosage: 80 mL) with hybrid iterative reconstruction, while the low-dose group (group B, n=55) used the low-dose protocol (tube voltage: 100 kVp; contrast dosage: 50 mL) with AIIR. Attenuation values, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for objective analysis. Subjective image quality was rated by 2 blinded radiologists using 5-point scales.
Results:
No significant differences in demographic characteristics were observed between the 2 groups (all P >0.05). The radiation dose in group B was reduced by 73.8%, respectively, compared with group A. The mediastinal segment of BA was shown in both group images, while the hilar segment of BA was higher in group B than in group A ( P <0.05). The mean subjective scores between the 2 groups showed no significant difference (all P >0.05), while SNR and CNR of group B were higher than those of group A (all P <0.0001).
Conclusions:
The simultaneous reconstruction of BA-CTA images using the AIIR algorithm with reduced tube voltage and contrast agent dosage not only substantially reduces the radiation dose of preoperative BA-CTA for BAE but also achieves better image quality than routine-dose images.
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