mlsanityData to 7 Oct 2026

XctDiff: Reconstruction of CT Images with Consistent Anatomical Structures from a Single Radiographic Projection Image

arXiv 2406.04679 ↗First posted 7 Jun 2024eess.IV cs.CVcited by 3

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Abstract (arXiv)

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

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 7 Jun 2024.

Builds on

15 of 22 references.

References · 15

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3 more
  1. 2020A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)cited by 13
  2. 2022SideRT: A Real-time Pure Transformer Architecture for Single Image Depth Estimationcited by 3
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.