Sinogram Enhancement with Generative Adversarial Networks using Shape Priors
This addresses the problem of reducing X-Ray exposure in medical imaging by inferring missing data, though it appears incremental as it builds on existing generative models and shape priors.
The paper tackled Limited Angle Tomography by using a Generative Adversarial Network with CAD shape priors to infer missing measurements, demonstrating quantitative and qualitative advantages over state-of-the-art methods.
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?