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

Angélique Bernard; Pierre-Olivier Comby; Brivaël Lemogne; Karim Haioun; Frédéric Ricolfi; Olivier Chevallier; Romaric Loffroy

doi:10.21037/qims-20-626

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

Quant Imaging Med Surg. 2021 Jan;11(1):392-401. doi: 10.21037/qims-20-626.

Authors

Angélique Bernard¹, Pierre-Olivier Comby¹, Brivaël Lemogne¹, Karim Haioun², Frédéric Ricolfi¹, Olivier Chevallier³, Romaric Loffroy³

Affiliations

¹ Department of Neuroradiology and Emergency Radiology, François-Mitterrand University Hospital, Dijon, France.
² Computed Tomography Division, Canon Medical Systems France, Suresnes, France.
³ Department of Cardiovascular and Interventional Radiology, ImViA Laboratory-EA 7535, François-Mitterrand University Hospital, Dijon, France.

Abstract

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 =106.4±50.0 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<0.001). The median SNR and CNR were higher [9.9 (IQR, 8.1-12.3); and 12.6 (IQR, 10.5-15.5), respectively], with AiCE than with AIDR 3D [6.5 (IQR, 5.2-8.5); and 8.4 (IQR, 6.7-11.0), respectively] (P<0.001). SNR and CNR were increased by 51% and 49%, respectively, with AiCE compared to AIDR 3D. The image quality was significantly better with AiCE (mean IQ score =3.4±0.7) than with AIDR 3D (mean IQ score =3±0.9) (P<0.001).

Conclusions: The use of a DLR algorithm for cardiac CTA in an acute stroke imaging protocol reduced the radiation dose by about 40% and improved the image quality by about 50% compared to an iterative reconstruction algorithm.

Keywords: Computed tomography angiography (CTA); artificial intelligence; cardiac imaging; deep learning; image reconstruction.