Deep Learning Reconstruction Improves the Image Quality of CT Angiography Derived From 80-kVp Cerebral CT Perfusion
Yu Chen1, Yanling Wang1, Tong Su1
1Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences. No.1 Shuai Fu Yuan, Dong Cheng District, Beijing 100730, China.
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: HIRpeak and DLRpeak were single arterial phase images derived from the time point showing the peak value, HIRtMIP and HIRtAve 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:
DLRpeak had the highest CT value and contrast-to-noise ratio in the internal carotid artery and basilar artery (all p < 0.001). DLRpeak 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 (HIRtAve, HIRpeak, and HIRtMIP DLRpeak).
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.
