RSTAR4D: Rotational Streak Artifact Reduction in 4-D CBCT Using Separable 4-D Convolutions

Four-dimensionalcone-beam computed tomography (4-D CBCT) provides respiration-resolved images and facilitates 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, the reconstructed 4-D CBCT images are covered by severe streak artifacts. Although several deep learning-based methods have been proposed to address this issue, most algorithms formulate it as a 2-D image enhancement task, neglecting the dynamic nature of 4-D CBCT. In this article, we first identify the origin and appearance of streak artifacts in 4-D CBCT images. We find that streak artifacts exhibit a unique “rotational motion” along with the patient’s respiration, distinguishable from diaphragm-driven respiratory motion in 4-D space. Therefore, we introduce RSTAR4D-Net, a 4-D model that performs rotational streak artifact reduction by exploring the dynamic prior of 4-D CBCT images. Specifically, we overcome the computational and training difficulties of a 4-D neural network. The specially designed model decomposes the 4-D convolutions into multiple lower-dimensional operations and thus efficiently processes a whole 4-D image. Additionally, a Tetris training strategy is proposed to effectively train the model using limited 4-D data. Extensive experiments substantiate the superior performance of RSTAR4D-Net compared to existing methods.