The application of deep learning algorithm reconstruction in low tube voltage coronary CT angiography
Received date: 2021-12-14
Online published: 2022-08-17
目的：比较深度学习重建算法（deep learning-based image reconstruction,DLIR）、自适应统计迭代重建算法（adaptive statistical iterative reconstruction-veo,ASiR-V）和滤波反投影重建算法（filtered back-projection,FBP）对低管电压冠状动脉CT血管成像（coronary computed tomographic angiography,CCTA）图像质量的优化效果。方法：前瞻性纳入100例行CCTA扫描的患者,根据其身体质量指数（body mass index,BMI）,选择使用70 kVp（50例,BMI≤26 kg/m2）或80 kVp（50例,BMI>26 kg/m2）管电压扫描,每例患者的图像分别用FBP（A组）、ASiR-V 50%（B组）、 中级DLIR（DLIR-Medium,DLIR-M,C组）和高级DLIR（DLIR-High,DLIR-H,D组）进行重建,比较4组重建算法图像的CT值、噪声、信噪比（signal-to-noise ratio,SNR）和对比噪声比（contrast-noise ratio,CNR）,并采用李克特5级评分法对图像质量进行主观评价。结果：在客观图像质量评价中, A、B、C、D 4组两两组间比较,图像噪声、SNR、CNR差异均有统计学意义（P<0.05）,其中D组的图像噪声最低,而SNR和CNR最高;在主观图像质量评价中,C组与D组间差异无统计学意义,但均明显高于A组及B组（P<0.05）。结论：在低管电压CCTA扫描中,使用DLIR重建的图像质量优于ASiR-V 50% 和FBP,提示DLIR适用于临床低管电压CCTA扫描。
关键词： 冠状动脉CT血管成像; 深度学习; 迭代重建; 辐射剂量; 图像质量
范婧, 杨文洁, 王梦真, 陆伟, 石骁萌, 朱宏 . 深度学习重建算法在低管电压冠状动脉CT血管成像中的应用[J]. 诊断学理论与实践, 2022 , 21(03) : 374 -379 . DOI: 10.16150/j.1671-2870.2022.03.014
Objective: To compare the quality of low tube voltage coronary CT angiography (CCTA) images reconstructed with deep learning-based image reconstruction (DLIR) and with filter back projection (FBP) and with adaptive statistical iterative reconstruction-veo (ASiR-V). Methods: One hundred patients who underwent CCTA were included. The CCTA tube voltage were set as 70 kVp (n=50, BMI≤26 kg/m2) and 80 kVp (n=50, BMI>26 kg/m2) according to body mass index(BMI). The images were reconstructed with FBP (Group A), ASIR-V 50% (Group B), DLIR at medium (DLIR-M, Group C) and high DLIR(DLIR-H, Group D) levels, respectively. Objective evaluation indice including CT attenuation, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio(CNR) were measured or calculated between groups, and Likert 5-point scale was adopted for subjective image quality assessment. Results: There were significant differences in image noise, SNR, CNR among the 4 groups(P<0.05), and Group D had the highest SNR and CNR, and lowest noise. There was no significant difference between Group C and Group D in subjective scores, but Group C and D both had higher subjective scores than those of Group A and B (P<0.05). Conclusions: For low tube voltage CCTA, images reconstructed with DLIR generate higher quality,and DLIR may be suitable to apply in low tube voltage CCTA.
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