Artifacts reduction method for phase-resolved Cone-Beam CT (CBCT) images via a prior-guided CNN

Conventional Cone-Beam Computed Tomography (CBCT) acquisition suffers from motion blurring artifacts at the region of the thorax, and consequently, it may result in inaccuracy in localizing the target of treatment and verifying delivered dose in radiation therapy. Although 4D-CBCT reconstruction technology is available to alleviate the motion blurring artifacts with the strategy of projection sorting followed by independent reconstruction, under-sampling streaking artifacts and noise are observed in the set of 4D-CBCT images due to relatively fewer projections and large angular spacing in each phase. Aiming at improving the overall quality of 4D-CBCT images, we explored the performance of the deep learning model on 4D-CBCT images, which has been paid little attention before. Inspired by the high correlation among the 4D-CBCT images at different phases, we incorporated a prior image reconstructed from full-sampled projections beforehand into a lightweight structured convolutional neural network (CNN) as one input channel. The prior image used in the CNN model can guide the final output image to restore detailed features in the testing process, so it is referred to as Prior-guided CNN. Both simulation and real data experiments have been carried out to verify the effectiveness of our CNN model. Experimental results demonstrate the effectiveness of the proposed CNN regarding artifact suppression and preservation of anatomical structures. Quantitative evaluations also indicate that 33.3% and 21.2% increases in terms of Structural Similarity Index (SSIM) have been achieved by our model when comparing with gated reconstruction and images tested on CNN without prior knowledge, respectively.