DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or Mono-planar 2D X-ray(s)
<|reference_start|>DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or Mono-planar 2D X-ray(s): Computational tomography (CT) provides high-resolution medical imaging, but it can expose patients to high radiation. X-ray scanners have low radiation exposure, but their resolutions are low. This paper proposes a new conditional diffusion model, DX2CT, that reconstructs three-dimensional (3D) CT volumes from bi or mono-planar X-ray image(s). Proposed DX2CT consists of two key components: 1) modulating feature maps extracted from two-dimensional (2D) X-ray(s) with 3D positions of CT volume using a new transformer and 2) effectively using the modulated 3D position-aware feature maps as conditions of DX2CT. In particular, the proposed transformer can provide conditions with rich information of a target CT slice to the conditional diffusion model, enabling high-quality CT reconstruction. Our experiments with the bi or mono-planar X-ray(s) benchmark datasets show that proposed DX2CT outperforms several state-of-the-art methods. Our codes and model will be available at: https://www.github.com/intyeger/DX2CT.<|reference_end|>
@article{jeong2024dx2ct:,
    title={DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or Mono-planar
  2D X-ray(s)},
    author={Yun Su Jeong, Hye Bin Yoo, Il Yong Chun},
    journal={arXiv preprint arXiv:2409.08850},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08850},
    primaryClass={eess.IV cs.CV}
}