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

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.