[Show abstract][Hide abstract] ABSTRACT: Purpose:
Automatic exposure control (AEC) algorithms are widely available in coronary computed tomography angiography (CTA) and have been shown to reduce radiation doses by adjusting tube current to patient size. However, the effects of anthropometry-based automatic potential selection (APS) on image quality and radiation dose are unknown. We sought to investigate the effect of an APS algorithm on coronary CTA radiation dose and image quality.
Materials and methods:
For this retrospective case-control study we selected 38 patients who had undergone coronary CTA for coronary artery assessment in whom tube potential and tube current were selected automatically by a combined automatic tube potential and tube current selection algorithm (APS-AEC) and compared them with 38 controls for whom tube voltage was selected according to standard body mass index (BMI) cutoffs and tube current was selected using automatic exposure control (BMI-AEC). Controls were matched for BMI, heart rate, heart rhythm, sex, acquisition mode, and indication for cardiac CTA. Image quality was assessed as contrast-to-noise ratio and signal-to-noise ratio in the proximal coronary arteries. Subjective reader assessment was also made. Total radiation dose (volume-weighted computed tomography dose index) was measured and compared between the 2 groups. In the study group, comparison was made with conventional BMI-guided prior protocols (site protocols and Society of Cardiovascular Computed Tomography recommendations) through disagreement analysis.
The APS-AEC cases received 29.8% lower overall radiation dose compared with controls (P=not significant). APS-AEC resulted in a significantly higher signal-to-noise ratio of the proximal coronary arteries (P<0.01) and contrast-to-noise ratio of the left main (P=0.01). In the study cases, the APS resulted in a change in tube potential versus site protocols and Society of Cardiovascular Computed Tomography recommendations in 45% (n=17) and 50% (n=19) of patients, respectively.
Automated tube potential selection software resulted in significantly improved objective image quality versus standard BMI-based methods of tube potential selection, without increased radiation doses.