Generative Tomography Reconstruction
This work addresses tomography reconstruction for medical or scientific imaging, offering incremental improvements in accuracy and efficiency.
The authors tackled the problem of noisy tomography reconstruction by proposing an end-to-end differentiable architecture that maps sinograms to denoised reconstructions, achieving more accurate results with fewer parameters and less time compared to existing methods, and introduced a generative model for sampling realistic reconstructions to reduce artifacts.
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.