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.

翻译：我们提出一个终极至终端可区别的地形重建架构,该架构可直接将噪音的罪恶图绘制成无名重建。 与我们端对端结构的现有方法相比,我们端对端结构可以产生更准确的重建,同时使用较少的参数和时间。 我们还提出一个基因模型,在噪音的罪恶图下,可以对现实的重建进行抽样。 这种基因模型可以作为以前迭代过程使用,通过考虑物理模型,可以减少重建过程中的文物和错误。