  Four-dimensional cone-beam computed tomography (4D CBCT) provides
respiration-resolved images and can be used for image-guided radiation therapy.
However, the ability to reveal respiratory motion comes at the cost of image
artifacts. As raw projection data are sorted into multiple respiratory phases,
there is a limited number of cone-beam projections available for image
reconstruction. Consequently, the 4D CBCT images are covered by severe streak
artifacts. Although several deep learning-based methods have been proposed to
address this issue, most algorithms employ ordinary network models, neglecting
the intrinsic structural prior within 4D CBCT images. In this paper, we first
explore the origin and appearance of streak artifacts in 4D CBCT
images.Specifically, we find that streak artifacts exhibit a periodic
rotational motion along with the patient's respiration. This unique motion
pattern inspires us to distinguish the artifacts from the desired anatomical
structures in the spatiotemporal domain. Thereafter, we propose a
spatiotemporal neural network named RSTAR-Net with separable and circular
convolutions for Rotational Streak Artifact Reduction. The specially designed
model effectively encodes dynamic image features, facilitating the recovery of
4D CBCT images. Moreover, RSTAR-Net is also lightweight and computationally
efficient. Extensive experiments substantiate the effectiveness of our proposed
method, and RSTAR-Net shows superior performance to comparison methods.
