Stereoscopic observation is a common foundation of medical image analysis and is generally achieved by 3D medical imaging based on settled scanners, such as CT and MRI, that are not as convenient as X-ray machines in some flexible scenarios. However, X-ray images can only provide perspective 2D observation and lack view in the third dimension. If 3D information can be deduced from X-ray images, it would broaden the application of X-ray machines. Focus on the above objective, this paper dedicates to the generation of pseudo 3D CT scans from non-parallel 2D perspective X-ray (PXR) views and proposes the Draw Sketch and Draw Flesh (DSDF) framework to first roughly predict the tissue distribution (Sketch) from PXR views and then render the tissue details (Flesh) from the tissue distribution and PXR views. Different from previous studies that focus only on partial locations, e.g., chest or neck, this study theoretically investigates the feasibility of head-to-leg reconstruction, i.e., generally applicable to any body parts. Experiments on 559 whole-body samples from 4 cohorts suggest that our DSDF can reconstruct more reasonable pseudo CT images than state-of-the-art methods and achieve promising results in both visualization and various downstream tasks. The source code and well-trained models are available a https://github.com/YongshengPan/WholeBodyXraytoCT.

立体观察是医学影像分析的常见基础，一般是通过基于固定扫描仪的 3D 医学成像来实现的，比如 CT 和 MRI，在一些灵活的场景中不如 X 光机方便。然而，X 射线图像只能提供透视 2D 观察，而缺乏三维视野。如果可以从 X 射线图像中推断出 3D 信息，它将拓宽 X 射线机的应用。围绕上述目标，本文致力于从非平行 2D 透视 X 射线 （PXR） 视图生成伪 3D CT 扫描，并提出 Draw Sketch 和 Draw Flesh （DSDF） 框架，首先从 PXR 视图粗略预测组织分布 （Sketch），然后从组织分布和 PXR 视图渲染组织细节 （Flesh）。与以前只关注部分位置（例如胸部或颈部）的研究不同，本研究从理论上调查了头到腿重建的可行性，即通常适用于任何身体部位。对来自 4 个队列的 559 个全身样本的实验表明，我们的 DSDF 可以重建比最先进的方法更合理的伪 CT 图像，并在可视化和各种下游任务中取得有希望的结果。源代码和训练有素的模型 https://github.com/YongshengPan/WholeBodyXraytoCT 可用。