DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays

• URL: http://arxiv.org/abs/2503.17804v1
• Date: Sat, 22 Mar 2025 16:03:18 GMT
• Title: DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays
• Authors: Xing Xie, Jiawei Liu, Huijie Fan, Zhi Han, Yandong Tang, Liangqiong Qu,
• Abstract summary: We facilitate complex 2D X-ray image to 3D CT mapping by incorporating new view synthesis, and reduce the learning difficulty through view-guided feature alignment.<n>Specifically, we propose a dual-view guided diffusion model (DVG-Diffusion), which couples a real input X-ray view and a synthesized new X-ray view to jointly guide CT reconstruction.
• Score: 32.55527512602604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Directly reconstructing 3D CT volume from few-view 2D X-rays using an end-to-end deep learning network is a challenging task, as X-ray images are merely projection views of the 3D CT volume. In this work, we facilitate complex 2D X-ray image to 3D CT mapping by incorporating new view synthesis, and reduce the learning difficulty through view-guided feature alignment. Specifically, we propose a dual-view guided diffusion model (DVG-Diffusion), which couples a real input X-ray view and a synthesized new X-ray view to jointly guide CT reconstruction. First, a novel view parameter-guided encoder captures features from X-rays that are spatially aligned with CT. Next, we concatenate the extracted dual-view features as conditions for the latent diffusion model to learn and refine the CT latent representation. Finally, the CT latent representation is decoded into a CT volume in pixel space. By incorporating view parameter guided encoding and dual-view guided CT reconstruction, our DVG-Diffusion can achieve an effective balance between high fidelity and perceptual quality for CT reconstruction. Experimental results demonstrate our method outperforms state-of-the-art methods. Based on experiments, the comprehensive analysis and discussions for views and reconstruction are also presented.

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