Ultrafast electron beam X-ray computed tomography produces noisy data due to short measurement times, causing reconstruction artifacts and limiting overall image quality. To counteract these issues, two self-supervised deep learning methods for denoising of raw detector data were investigated and compared against a non-learning based denoising method. We found that the application of the deep-learning-based methods was able to enhance signal-to-noise ratios in the detector data and also led to consistent improvements of the reconstructed images, outperforming the non-learning based method.

翻译：超快电子束X射线计算机断层扫描因测量时间短而产生噪声数据，导致重建伪影并限制整体图像质量。为应对这些问题，本文研究并比较了两种用于原始探测器数据去噪的自监督深度学习方法与一种非学习型去噪方法。研究发现，基于深度学习的方法能够提升探测器数据的信噪比，并持续改善重建图像质量，其性能优于非学习型方法。