Artifact correction is a great challenge in cardiac imaging. During the correction of coronary tissue with motion-induced artifacts, the spatial distribution of CT value not only shifts according to the motion vector field (MVF), but also shifts according to the volume change rate of the local voxels. However, the traditional interpolation method does not conserve the CT value during motion compensation. A new sample interpolation algorithm is developed based on the constraint of conservation of CT value before and after image deformation. This algorithm is modified on the existing interpolation algorithms and can be embedded into neural networks with deterministic back propagation. Comparative experimental results illustrate that the method can not only correct motion-induced artifacts, but also ensure the conservation of CT value in the region of interest (ROI) area, so as to obtain corrected images with clinically recognized CT value. Both effectiveness and efficiency are proved in forward motion correction process and backward training steps in deep learning. Simultaneously, the visualized motion vector field transparentizes the correction process, making this method more interpretable than the existing image-based end-to-end deep learning method.
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.
Beating of the heart is a type of motion which is the most difficult to control during the cardiac CT scan and causes significant artifacts. Hearts have the least motion at systole and diastole phase which for an average heart happens at 45% and 75% phase respectively. However, in practice this is not guaranteed, so doctors sometimes reconstruct several phases, review all those images and then make diagnosis from the phase that has the least artifacts. The new method for automatic dynamic optimal phase reconstruction has image quality comparable to the manual phase selection but it also significantly reduces the exam time by omitting the review of unnecessary phases.