Background. Computed tomography perfusion (CTP) imaging plays a critical role in the acute stroke syndrome assessment due to its widespread availability, speed of image acquisition, and relatively low cost. However, due to its repeated scanning protocol, CTP imaging involves a substantial radiation dose, which might increase potential cancer risks. Methods. In this work, we present a novel deep learning model called non-local perfusion texture learning network (NPTN) for high-quality CTP imaging at low-dose cases. Specifically, considering abundant similarities in the CTP images, i.e. latent self-similarities within the non-local region in the CTP images, we firstly search the most similar pixels from the adjacent frames within a fixed search window to obtain the non-local similarities and to construct non-local textures vector. Then, both the low-dose frame and these non-local textures from adjacent frames are fed into a convolution neural network to predict high-quality CTP images, which can help better characterize the structure details and contrast variants in the targeted CTP image rather than simply utilizing the targeted frame itself. The residual learning strategy and batch normalization are utilized to boost the performance of the convolution neural network. In the experiment, the CTP images of 31 patients with suspected stroke disease are collected to demonstrate the performance of the presented NPTN method. Results. The results show the presented NPTN method obtains superior performance compared with the competing methods. From numerical value, at all dose levels, the presented NPTN method has achieved around 3.0 dB improvement of average PSNR, an increase of around 1.4% of average SSIM, and a decrease of around 4.8% of average RMSE in the low-dose CTP reconstruction task, and also has achieved an increase of around 3.4% of average SSIM and a decrease of around 61.1% of average RMSE in the cerebral blood flow (CBF) estimation task. Conclusions. The presented NPTN method can obtain high-quality CTP images and estimate high-accuracy CBF map by characterizing more structure details and contrast variants in the CTP image and outperform the competing methods at low-dose cases.

背景。计算机断层扫描灌注 (CTP) 成像由于其广泛的可用性、图像采集速度和相对较低的成本，在急性中风综合征评估中起着至关重要的作用。然而，由于其重复扫描协议，CTP 成像涉及大量辐射剂量，这可能会增加潜在的癌症风险。方法. 在这项工作中，我们提出了一种称为非局部灌注纹理学习网络 (NPTN) 的新型深度学习模型，用于低剂量情况下的高质量 CTP 成像。具体来说，考虑到 CTP 图像中丰富的相似性，即 CTP 图像中非局部区域内的潜在自相似性，我们首先在固定搜索窗口内从相邻帧中搜索最相似的像素，以获得非局部相似性和构造非局部纹理向量。然后，将低剂量帧和来自相邻帧的这些非局部纹理输入卷积神经网络以预测高质量的 CTP 图像，这有助于更好地表征目标 CTP 图像中的结构细节和对比度变体，而不是只需利用目标框架本身。利用残差学习策略和批量归一化来提高卷积神经网络的性能。在实验中，收集了 31 名疑似脑卒中患​​者的 CTP 图像，以证明所提出的 NPTN 方法的性能。结果。结果表明，与竞争方法相比，所提出的 NPTN 方法获得了优越的性能。从数值上看，在所有剂量水平下，所提出的NPTN方法在低剂量CTP重建中实现了平均PSNR提高约3.0 dB，平均SSIM提高约1.4%，平均RMSE降低约4.8%任务，并且在脑血流量（CBF）估计任务中也实现了平均 SSIM 约 3.4% 的增加和平均 RMSE 约 61.1% 的降低。结论。所提出的 NPTN 方法可以通过表征 CTP 图像中的更多结构细节和对比度变体来获得高质量的 CTP 图像和估计高精度 CBF 图，并且在低剂量情况下优于竞争方法。