上海科技大学知识管理系统

Recent development of clinical Computed tomography (CT) technologies has led to research for novel CT systems that allow safer and faster imaging, such as low-dose cardiac CT imaging via stationary CT. However, the complex data acquisition schemes in stationary CT often cause severe artifacts and noise in the resulted images; this calls for the development of a new kind of image reconstruction algorithms. Recent advancements in deep learning have shown remarkable progress in medical image reconstruction, processing, and analysis. In this paper, we propose a generative network with dual-domain discriminators for low-dose CT reconstruction in a stationary CT system. The image-domain discriminator optimizes the generation network by comparing the generated CT images with the reference images, while the sinogram-domain discriminator preserves the structure of the sinograms and suppresses the noise. The network incorporates uncertainty to automatically adjust the weights of a multi-term loss function, eliminating the need for the manual tuning of hyperparameters in the loss function. The results from our numerical experiments demonstrate the effectiveness of our proposed reconstruction algorithm for low-dose imaging in stationary CT. © 2023 ACM.