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

Four-dimensional cone-beam computed tomography (4D 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 4D 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 2D image enhancement task, neglecting the dynamic nature of 4D CBCT. In this paper, we first identify the origin and appearance of streak artifacts in 4D 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 4D space. Therefore, we introduce RSTAR4D-Net, a 4D model that performs Rotational STreak Artifact Reduction by exploring the dynamic prior of 4D CBCT images. Specifically, we overcome the computational and training difficulties of a 4D neural network. The specially designed model decomposes the 4D convolutions into multiple lower-dimensional operations and thus efficiently processes a whole 4D image. Additionally, a Tetris training strategy is proposed to effectively train the model using limited 4D data. Extensive experiments substantiate the superior performance of RSTAR4D-Net compared to existing methods.