Application of CCTA under 80 kV tube voltage based on deep learning image reconstruction algorithm

Abstract

Objective To explore the application value of 80 kV deep learning image reconstruction （DLIR） algorithm in coronary CT angiography （CCTA）. Methods Sixty patients who underwent CCTA were divided into two groups based on the scanning protocols： 100 kV group （Group A， n = 30） and 80 kV group （Group B， n = 30）. In Group A， 60% ASIR-V （A-AV60） and DLIR high-level reconstruction （A-DLIR） was adopted. In Group B， DLIR high-level reconstruction （B-DLIR） was employed. The CT volumetric dose index （CTDIvol） and the dose length product （DLP） were recorded in both groups， and the effective dose （ED） was calculated. Regions of interest （ROI） were placed in the aortic root （AR）， left anterior descending coronary artery （LAD）， left circumflex coronary artery （LCX）， right coronary artery （RCA）， and the same-layer pectoral fat area. The CT values and noise values of each ROI were recorded. Signal-to-noise ratio （SNR） and contrast-to-noise ratio （CNR） were calculated. Subjective evaluation was performed on the original axis， curved planar reconstruction （CPR）， volume rendering （VR）， and maximum intensity projection （MIP） reconstructions after the second-generation freeze technology （Snapshot Freeze 2， SSF-2）， and the images in two groups were subject to subjective image quality evaluation. Results The ED in Group B was reduced by 45.14% compared to that in Group A. The CT values for AR， LAD， LCX， and RCA in the B-DLIR were higher than those in the A-AV60 and A-DLIR groups， and the differences were statistically significant （all P < 0.001）. The noise values for AR， LAD and LCX were similar， whereas statistical significance was observed in RCA between the A-DLIR and B-DLIR groups （P < 0.05）. The noise values in the A-DLIR and B-DLIR groups were smaller than that in the A-AV60 group， and the differences were statistically significant （both P < 0.001）. The SNR and CNR for AR， LAD， LCX and RCA were similar between the A-DLIR and B-DLIR groups， which were higher than those in the A-AV60 group （all P < 0.05）. The average subjective evaluation score of image quality in the B-DLIR group was higher than that in the A-AV60 group （P < 0.05）， whereas lower than that in the A-DLIR group （P < 0.05）. There were no significant differences in clarity， artifact and small branch visibility between the A-DLIR and B-DLIR groups （all P > 0.05）. Conclusions During CCTA， the 80 kV DLIR algorithm contributes to yielding high-quality images， further improves the diagnostic efficiency and reduces the irradiation dose.

Keywords

• deep learning image reconstruction|adaptive statistical iterative reconstruction|coronary ct angiography|signal-to-noise ratio|contrast-to-noise ratio