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.

翻译：四维锥束计算机断层扫描（4D CBCT）可提供呼吸分辨图像，并可用于图像引导放射治疗。然而，揭示呼吸运动的能力是以图像伪影为代价的。由于原始投影数据被分入多个呼吸相位，可用于图像重建的锥束投影数量有限。因此，4D CBCT图像被严重的条纹伪影所覆盖。尽管已提出多种基于深度学习方法解决此问题，但多数算法采用常规网络模型，忽略了4D CBCT图像中的固有结构先验。本文首先探究了4D CBCT图像中条纹伪影的起源与表现形式。具体而言，我们发现条纹伪影随患者呼吸呈现周期性旋转运动。这一独特运动模式启发我们在时空域中将伪影与期望的解剖结构区分开来。随后，我们提出一种名为RSTAR-Net的时空神经网络，它采用可分离卷积与环形卷积实现旋转条纹伪影抑制。该特殊设计的网络能有效编码动态图像特征，促进4D CBCT图像的恢复。此外，RSTAR-Net兼具轻量级与高计算效率特性。大量实验验证了所提方法的有效性，且RSTAR-Net展现出优于对比方法的性能。