In Computed Tomography (CT), an image of the interior structure of an object
is computed from a set of acquired projection images. The quality of these
reconstructed images is essential for accurate analysis, but this quality can
be degraded by a variety of imaging artifacts. To improve reconstruction
quality, the acquired projection images are often processed by a pipeline
consisting of multiple artifact-removal steps applied in various image domains
(e.g., outlier removal on projection images and denoising of reconstruction
images). These artifact-removal methods exploit the fact that certain artifacts
are easier to remove in a certain domain compared with other domains.
Recently, deep learning methods have shown promising results for artifact
removal for CT images. However, most existing deep learning methods for CT are
applied as a post-processing method after reconstruction. Therefore, artifacts
that are relatively difficult to remove in the reconstruction domain may not be
effectively removed by these methods. As an alternative, we propose a
multi-stage deep learning method for artifact removal, in which neural networks
are applied to several domains, similar to a classical CT processing pipeline.
We show that the neural networks can be effectively trained in succession,
resulting in easy-to-use and computationally efficient training. Experiments on
both simulated and real-world experimental datasets show that our method is
effective in reducing artifacts and superior to deep learning-based
post-processing.

计算机断层扫描（CT）中，通过一组获取到的投影图像计算出物体内部结构的图像。为了提高重建图像的质量，常常对这些获取到的投影图像进行多个去伪影步骤的处理。近年来，深度学习方法在 CT 造影图像去伪影方面显示出了很好的结果。然而，大多数现有的 CT 深度学习方法是在重建之后进行后处理的。因此，这些方法可能不能有效去除在重建域中相对难以去除的伪影。作为替代，我们提出了一种多阶段深度学习伪影去除方法，其中神经网络在几个领域中应用，类似于传统的 CT 处理流程。我们展示了这些神经网络可以有效地连续训练，从而实现易用和计算高效。对模拟和真实实验数据集的实验证明，我们的方法在减少伪影方面是有效的，并且优于基于深度学习的后处理方法。