Sinogram enhancement with generative adversarial networks using shape priors

Abstract

Compensating scarce measurements by inferring them from computational models is a way to address ill-posed inverse problems. We tackle Limited Angle Tomography by completing the set of acquisitions using a generative model and prior-knowledge about the scanned object. Using a Generative Adversarial Network as model and Computer-Assisted Design data as shape prior, we demonstrate a quantitative and qualitative advantage of our technique over other state-of-the-art methods. Inferring a substantial number of consecutive missing measurements, we offer an alternative to other image inpainting techniques that fall short of providing a satisfying answer to our research question: can X-Ray exposition be reduced by using generative models to infer lacking measurements?

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Identifiers

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

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

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

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