Low-dose cerebral CT perfusion restoration via non-local convolution neural network: Initial study

Computed tomography perfusion (CTP) imaging can be used to detect ischemic stroke via high-resolution and quantitative hemodynamic maps. However, due to its repeated scanning protocol, CTP imaging involves a substantial radiation dose, which might increase potential cancer risks. Therefore, reducing radiation dose in CTP has raised significant research interests. In this work, we present a non-local convolution neural network (NL-Net) to yield high quality CTP images and high precision hemodynamic maps at low-dose cases. Specifically, different from the traditional network in CT imaging, this NL-Net takes into consideration the non-local information from adjacent frames as one of the input. Then, the low-dose CTP images combining with the non-local information feeds into the pre-trained network to produce desired CTP images with high quality. The clinical patient data are used to demonstrate the performance of the NL-Net, and corresponding results indicate that the presented NL-Net can obtain better CTP images and more accurate hemodynamic maps compared with the competing approaches.