Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet

Cerebral perfusion computed tomography (PCT) is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms. With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging, considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging. Reducing the dose of X-rays causes severe noise and artifacts in PCT images. To solve this problem, we propose a deep learning method called NCS-Unet. The exceptional characteristics of non-subsampled contourlet transform (NSCT) and the Sobel filter are introduced into NCS-Unet. NSCT decomposes the convolved features into high- and low-frequency components. The decomposed high-frequency component retains image edges, contrast imaging traces, and noise, whereas the low-frequency component retains the main image information. The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay. The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal. Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.