Deep grid inpainting for photon-counting CT detectors

Jan Magonov; Joscha Maier; Eric Fournié; Johan Sunnegårdh; Karl Stierstorfer; Marc Kachelrieß

doi:10.1117/12.3006725

1 April 2024 Deep grid inpainting for photon-counting CT detectors

Jan Magonov, Joscha Maier, Eric Fournié, Johan Sunnegårdh, Karl Stierstorfer, Marc Kachelrieß

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129252S (2024) https://doi.org/10.1117/12.3006725
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jan Magonov, Joscha Maier, Eric Fournié, Johan Sunnegårdh, Karl Stierstorfer, and Marc Kachelrieß "Deep grid inpainting for photon-counting CT detectors", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129252S (1 April 2024); https://doi.org/10.1117/12.3006725

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