A sequential approach using convolutional neural networks for motion artifacts reduction with a fast CT scan mode

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.