A deep-learning reconstruction algorithm that improves the image quality of low-tube-voltage coronary CT angiography. Issue 146 (January 2022)

• Record Type: Journal Article
• Authors: Wang, Mengzhen; Fan, Jing; Shi, Xiaofeng; Qin, Le; Yan, Fuhua; Yang, Wenjie
• Is Part Of: European journal of radiology. Issue 146(2022)
• Publication Date: 2022-01
• Subjects: Coronary computed tomography angiography -- Deep learning -- Low tube voltage -- Image reconstruction; ASiR-V adaptive statistical iterative reconstruction-Veo -- AO Aortic root -- FBP Filtered back-projection -- BMI Body mass index -- bpm Beats per minute -- CAD Coronary artery disease -- CCTA Coronary CT angiography -- CNR Contrast-noise ratio -- DNN Deep neural networks -- CPR Curved planar reformation -- CTDIvol Volumetric CT dose index -- DLIR Deep learning–based image reconstruction -- DLP Dose-length product -- ED Effective dose -- ERD Edge rise distance -- HR Heart rates -- ICA Invasive coronary angiography -- IQ Image quality -- ROI Region of interest -- SD Standard deviation -- SNR Signal-to-noise ratio -- VR Volume rendering; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.110070
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Deep-learning reconstruction algorithm can improve the image quality of low-tube-voltage coronary CT angiography. Deep-learning reconstruction with high level did not decrease the sharpness of the coronary artery. Abstract: Purpose: To assess the image quality (IQ) of low tube voltage coronary CT angiography (CCTA) images reconstructed with deep learning image reconstruction (DLIR). Methods: According to body mass index (BMI), eighty patients who underwent 70kVp CCTA (Group A, N = 40, BMI ≤ 26 kg/m2) or 80kVp CCTA (Group B, N = 40, BMI > 26 kg/m2) were prospectively included. All images were reconstructed with four algorithms, including filtered back-projection (FBP), adaptive statistical iterative reconstruction-Veo at a level of 50% (ASiR-V50%), and DLIR at medium (DLIR-M) and high (DLIR-H) levels. Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and edge rise distance (ERD) within aorta root and coronary arteries were calculated. The IQ was subjectively evaluated by using a 5-point scale. Results: Compared with FBP, ASiR-V50% and DLIR-M, DLIR-H led to the lowest noise (Group A: 24.7 ± 5.0HU; Group B, 21.6 ± 2.8 HU), highest SNR (Group A, 24.9 ± 5.0; Group B, 28.0 ± 5.8), CNR (Group A, 42.2 ± 15.2; Group B, 43.6 ± 10.5) and lowest ERD (Group A, 1.49 ± 0.30 mm; Group B, 1.50 ± 0.22 mm) with statistical significance (all P < 0.05). For the objective assessment, the percentages of 4 and 5 IQ scores were significantly higher for DLIR-H (Group A, 93.8%; Group B, 90.0%) and DLIR-M (Group A, 85.6%; Group B, 86.9 %) compared to ASiR-V50% (Group A, 58.8%; Group B, 58.8%) and FBP (Group A, 34.4%; Group B, 33.1%) algorithms (all P < 0.05). Conclusion: The application of DLIR significantly improves both objective and subjective IQ in low tube voltage CCTA compared with ASiR-V and FBP, which may promote a further radiation dose reduction in CCTA.