In our previous work, we proposed to reduce motion artifacts in computed tomography (CT) using an image-based convolutional neural network (CNN). However, its motion compensation performance was limited when the degree of motion was large. We note that a fast scan mode can reduce the degree of motion but also cause streak artifacts due to sparse view sampling. In this study, we aim to initially reduce motion artifacts using a fast scan mode, and to reduce both the streak and motion artifacts using a CNN-based two-phase approach. In the first phase, we focus on reducing streak artifacts caused by sparse projection views. To effectively reduce streak artifacts in the presence of motion artifacts, a CNN with the U-net architecture and residual learning scheme was used. In the second phase, we focus on compensating motion artifacts in output image of the first phase. For this task, the attention blocks with global average pooling were used. To generate datasets, we used extended cardiac-torso phantoms and simulated sparse-view CT using half, quarter, and one-eighth of full projection views with corresponding 6-degree of freedom rigid motions. The results showed that the proposed two-phase approach effectively reduced both the motion and streak artifacts and taking fewer projection views down to one-eighth views (thus improving a scanning speed) provided the better image quality in our simulation study.
In X-ray CT imaging, the metal objects produce significant beam hardening and streak artifacts in the reconstructed CT images. To reduce the metal artifacts, several sinogram inpainting based methods have been proposed, where projection data within the metal trace region of the sinogram are treated as missing, and estimated by interpolation. However, they generally assume data truncation does not occur and all metal objects reside inside the FOV. For small FOV imaging such as dental CT, these assumptions are violated, and thus using traditional inpainting based MAR would not be effective. In this work, we proposed a new MAR method to reduce the metal artifacts effectively when the metal objects reside outside the FOV for the small FOV imaging. The proposed method synthesizes the projection data of small FOV image by conducting forward projection, which is treated as the originally measured sinogram. Thus the effect of metal objects outside the FOV was minimized during the inpainting procedure. The performance of the proposed method is compared with the traditional linear MAR and NMAR. The results showed the effectiveness of the proposed method to reduce the residual artifacts, which were present in the traditional linear MAR and NMAR images.