Generative Tomography Reconstruction

• URL: http://arxiv.org/abs/2010.14933v2
• Date: Wed, 25 Nov 2020 22:05:56 GMT
• Title: Generative Tomography Reconstruction
• Authors: Matteo Ronchetti, Davide Bacciu
• Abstract summary: We propose an end-to-end differentiable architecture for tomography reconstruction that maps a noisy sinogram into a denoised reconstruction. We also propose a generative model that, given a noisy sinogram, can sample realistic reconstructions.
• Score: 11.460692362624533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an end-to-end differentiable architecture for tomography reconstruction that directly maps a noisy sinogram into a denoised reconstruction. Compared to existing approaches our end-to-end architecture produces more accurate reconstructions while using less parameters and time. We also propose a generative model that, given a noisy sinogram, can sample realistic reconstructions. This generative model can be used as prior inside an iterative process that, by taking into consideration the physical model, can reduce artifacts and errors in the reconstructions.

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