Patient motion during computed tomography (CT) scan can result in serious degradation of imaging quality, and is of increasing concern due to the aging population and associated diseases. In this paper, we address this problem by focusing on the reduction of head motion artifacts. To achieve this, we introduce a head motion simulation system and a multi-scale deep learning architecture. The proposed motion simulation system can simulate rigid movement including translation and rotation. The images with simulated motion serve as the training set for the network, and the original motion free images serve as the gold standard. Motion artifacts exhibit in the image space as streaks and patchy shadows. We propose a multiscale neural network to learn the artifact. With different branches equipped with ResBlock and down-sampling, the network can learn long scale streaks and short scale shadow artifacts. Although we trained the network on simulated images, we find that the learned network generalizes well to images with real motion artifacts.
Bone induced artifacts caused by spectral absorption of skull is intrinsic to head images in CT. Artifacts which blur the images and further temper with the diagnostic power of CT. Several algorithms have been proposed to address the artifacts, but most are complex and take long time to eliminate the artifacts. In the past decade, the deep learning (DL) approach has demonstrated excellent effects in image processing. In this work, we present a twostep convolutional neural networks (CNNs) that reduces the artifacts. First step uses the U-shape network (UNet) to learn and correct the low frequency artifacts. Second step uses residual network (ResNet) to extract the high frequency artifacts. Our proposed method is capable of eliminating the bone induced artifacts within a relatively low time cost. Promising results have been obtained in our experiment with a large number of CT head images.