Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality

Background: To assess the radiation dose and image quality of cardiac computed tomography angiography (CCTA) in an acute stroke imaging protocol using a deep learning reconstruction (DLR) method compared to a hybrid iterative reconstruction algorithm. Methods: Retrospective analysis of 296 consecutive patients admitted to the emergency department for stroke suspicion. All patients underwent a stroke CT imaging protocol including a non-enhanced brain CT, a brain perfusion CT imaging if necessary, a CT angiography (CTA) of the supra-aortic vessels, a CCTA and a post-contrast brain CT. The CCTA was performed with a prospectively ECG-gated volume acquisition. Among all CT scans performed, 143 were reconstructed with an iterative reconstruction algorithm (AIDR 3D, adaptive iterative dose reduction three dimensional) and 146 with a DLR algorithm (AiCE, advanced intelligent clear-IQ engine). Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality (IQ) scored from 1 to 4 were assessed. Dose-length product (DLP), volume CT dose index (CTDIvol) and effective dose (ED) were obtained. Results: The radiation dose was significantly lower with AiCE than with AIDR 3D (DLP =106.450.0 vs. 176.1 +/- 37.1 mGy.cm, CTDIvol =6.9 +/- 3.2 vs. 11.5 +/- 2.2 mGy, and ED =1.5 +/- 0.7 vs. 2.5 +/- 0.5 mSv) (P