The application of deep learning algorithm reconstruction in low tube voltage coronary CT angiography

Abstract

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.

Keywords

• |coronary ct angiography|deep learning|iterative reconstruction|radiation dose|image quality