Sinogram inpainting with Generative Adversarial Networks and shape priors

Abstract

X-Ray computed tomography is a widely used, non-destructive imaging technique that computes cross-sectional images of an object from a set of X-Ray absorption profiles (the so-called sinogram). The computation of the image from the sinogram is an ill-posed inverse problem, which becomes under-determined when we are only able to collect insufficiently many X-Ray measurements. We are here interested in solving X-ray tomography image reconstruction problems where we are unable to scan the object from all directions, but where we have prior information about the object's shape. We thus propose a method that reduces image artefacts due to limited tomographic measurements by inferring missing measurements using shape priors. Our method uses a Generative Adversarial Network that combines limited acquisition data and shape information. While most existing methods focus on evenly spaced missing scanning angles, we propose an approach that infers a substantial number of consecutive missing acquisitions. We show that our method consistently improves image quality compared to images reconstructed using the previous state-of-the-art sinogram-inpainting techniques. In particular, we demonstrate a 7dB Peak Signal-to-Noise Ratio improvement compared to other methods.

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Identifiers

ORCID for Emilien Marius Mael Valat: orcid.org/0000-0002-1825-0097

ORCID for Kate Farrahi: orcid.org/0000-0001-6775-127X

ORCID for Thomas Blumensath: orcid.org/0000-0002-7489-265X

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