Reconstruct Spine CT from Biplanar X-Rays via Diffusion Learning
<|reference_start|>Reconstruct Spine CT from Biplanar X-Rays via Diffusion Learning: Intraoperative CT imaging serves as a crucial resource for surgical guidance; however, it may not always be readily accessible or practical to implement. In scenarios where CT imaging is not an option, reconstructing CT scans from X-rays can offer a viable alternative. In this paper, we introduce an innovative method for 3D CT reconstruction utilizing biplanar X-rays. Distinct from previous research that relies on conventional image generation techniques, our approach leverages a conditional diffusion process to tackle the task of reconstruction. More precisely, we employ a diffusion-based probabilistic model trained to produce 3D CT images based on orthogonal biplanar X-rays. To improve the structural integrity of the reconstructed images, we incorporate a novel projection loss function. Experimental results validate that our proposed method surpasses existing state-of-the-art benchmarks in both visual image quality and multiple evaluative metrics. Specifically, our technique achieves a higher Structural Similarity Index (SSIM) of 0.83, a relative increase of 10\%, and a lower Fr\'echet Inception Distance (FID) of 83.43, which represents a relative decrease of 25\%.<|reference_end|>
@article{qiao2024reconstruct,
    title={Reconstruct Spine CT from Biplanar X-Rays via Diffusion Learning},
    author={Zhi Qiao, Xuhui Liu, Xiaopeng Wang, Runkun Liu, Xiantong Zhen, Pei
  Dong, and Zhen Qian},
    journal={arXiv preprint arXiv:2408.09731},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09731},
    primaryClass={eess.IV cs.CV}
}