Deep Learning Reconstruction Improves the Image Quality of CT Angiography Derived From 80-kVp Cerebral CT Perfusion Data.

Rationale And Objective: To investigate the impact of the deep learning reconstruction (DLR) technique on the image quality of CT angiography (CTA) derived from 80-kVp cerebral CT perfusion (CTP) data and compare it with hybrid-iterative reconstruction (HIR).

Materials And Methods: Thirty-three patients underwent CTP at 80 kVp were prospectively enrolled. CTP data were reconstructed with HIR and DLR. Four image datasets were reconstructed: HIR and DLR were single arterial phase images derived from the time point showing the peak value, HIR and HIR were time-resolved maximum intensity projection image and time-resolved average image derived from three time points with the greatest enhancement of HIR. The mean CT values, standard deviation, signal-to-noise ratio, and contrast-to-noise ratio of the internal carotid artery and basilar artery were compared among the four image dataset. Image quality was performed using a five-point rating scale. Arterial stenosis was evaluated.

Results: DLR had the highest CT value and contrast-to-noise ratio in the internal carotid artery and basilar artery (all p < 0.001). DLR showed the best subjective image quality and had the highest score (4.93 ± 0.4) compared to the other three HIR CTA images (all p < 0.001). The degree of vascular stenosis was consistent among the four evaluated sequences (HIR, HIR, and HIR DLR).

Conclusion: For CTA derived from 80-kVp cerebral CTP data, images reconstructed with deep learning showed better image quality and improved intracranial artery visualization than those processed with HIR and other currently used techniques.