Deep grid inpainting for photon-counting CT detectors

Computed tomography systems with very small detector pixels, such as they also occur in photon-counting computed tomography (PCCT), may have dead detector rows and columns whether due to the manufacturing process, the anti-scatter grid (ASG) that blocks the primary radiation behind the ASG lamellae, or other reasons. Such situations require a sophisticated inpainting algorithm to avoid possible artifacts in reconstructed images. To meet these requirements, we developed grid inpainting with deep learning (GRIDL), a neural network-based algorithm to allow the inpainting of arbitrary gaps in column and row direction. In our experimental setup, we corrupt detector images from spiral CT scans with a regular pattern of gaps in a comparable way as the ASG would be arranged in a PCCT system and perform an inpainting using GRIDL. Our approach yields reconstructed images with image quality comparable to the gapless ground truth. In comparison to a simple inpainting method utilizing linear interpolation or a more sophisticated diffusion-based inpainting, GRIDL demonstrates a reduction in aliasing artifacts and the root mean square error (RMSE) in reconstructed images.