论文标题
生成断层扫描重建
Generative Tomography Reconstruction
论文作者
论文摘要
我们为断层扫描重建提供了端到端的可区分体系结构，该体系结构将嘈杂的曲构图直接映射到了一个被固定的重建中。与现有方法相比，我们的端到端体系结构会产生更准确的重建，同时使用更少的参数和时间。我们还提出了一个生成模型，鉴于嘈杂的曲《图》可以采样现实的重建。该生成模型可以用作迭代过程中的先验，通过考虑物理模型，可以减少重建中的伪影和错误。
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.
