A deep-learning reconstruction algorithm that improves the image quality of low-tube-voltage coronary CT angiography

Published:November 24, 2021DOI:


      • 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.



      To assess the image quality (IQ) of low tube voltage coronary CT angiography (CCTA) images reconstructed with deep learning image reconstruction (DLIR).


      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.


      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).


      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.



      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)
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