  In this paper, we present XctDiff, an algorithm framework for reconstructing
CT from a single radiograph, which decomposes the reconstruction process into
two easily controllable tasks: feature extraction and CT reconstruction.
Specifically, we first design a progressive feature extraction strategy that is
able to extract robust 3D priors from radiographs. Then, we use the extracted
prior information to guide the CT reconstruction in the latent space. Moreover,
we design a homogeneous spatial codebook to improve the reconstruction quality
further. The experimental results show that our proposed method achieves
state-of-the-art reconstruction performance and overcomes the blurring issue.
We also apply XctDiff on self-supervised pre-training task. The effectiveness
indicates that it has promising additional applications in medical image
analysis. The code is available at:https://github.com/qingze-bai/XctDiff
