A Frequency Domain Constraint for Synthetic X-ray Image Super Resolution

Synthetic X-ray images can be helpful for image guiding systems and VR simulations. However, it is difficult to produce high-quality arbitrary view synthetic X-ray images in real-time due to limited CT scanning resolution, high computation resource demand or algorithm complexity. Our goal is to generate high-resolution synthetic X-ray images in real-time by upsampling low-resolution im-ages. Reference-based Super Resolution (RefSR) has been well studied in recent years and has been proven to be more powerful than traditional Single Image Su-per-Resolution (SISR). RefSR can produce fine details by utilizing the reference image but it still inevitably generates some artifacts and noise. In this paper, we propose texture transformer super-resolution with frequency domain (TTSR-FD). We introduce frequency domain loss as a constraint to further improve the quality of the RefSR results with fine details and without obvious artifacts. This makes a real-time synthetic X-ray image-guided procedure VR simulation system possible. To the best of our knowledge, this is the first paper utilizing the frequency domain as part of the loss functions in the field of super-resolution. We evaluated TTSR-FD on our synthetic X-ray image dataset and achieved state-of-the-art results.

HRINet: Alternative Supervision Network for High-resolution CT image Interpolation

Image interpolation in medical area is of high importance as most 3D biomedical volume images are sampled where the distance between consecutive slices significantly greater than the in-plane pixel size due to radiation dose or scanning time. Image interpolation creates a number of new slices between known slices in order to obtain an isotropic volume image. The results can be used for the higher quality of 3D reconstruction and visualization of human body structures. Semantic interpolation on the manifold has been proved to be very useful for smoothing image interpolation. Nevertheless, all previous methods focused on low-resolution image interpolation, and most of them work poorly on high-resolution image. We propose a novel network, High Resolution Interpolation Network (HRINet), aiming at producing high-resolution CT image interpolations. We combine the idea of ACAI and GANs, and propose a novel idea of alternative supervision method by applying supervised and unsupervised training alternatively to raise the accuracy of human organ structures in CT while keeping high quality. We compare an MSE based and a perceptual based loss optimizing methods for high quality interpolation, and show the tradeoff between the structural correctness and sharpness. Our experiments show the great improvement on 256 2 and 5122 images quantitatively and qualitatively.