Computed tomography (CT) provides highly detailed three-dimensional (3D) medical images but is costly, time-consuming, and often inaccessible in intraoperative settings (Organization et al. 2011). Recent advancements have explored reconstructing 3D chest volumes from sparse 2D X-rays, such as single-view or orthogonal double-view images. However, current models tend to process 2D images in a planar manner, prioritizing visual realism over structural accuracy. In this work, we introduce DuoLift Generative Adversarial Networks (DuoLift-GAN), a novel architecture with dual branches that independently elevate 2D images and their features into 3D representations. These 3D outputs are merged into a unified 3D feature map and decoded into a complete 3D chest volume, enabling richer 3D information capture. We also present a masked loss function that directs reconstruction towards critical anatomical regions, improving structural accuracy and visual quality. This paper demonstrates that DuoLift-GAN significantly enhances reconstruction accuracy while achieving superior visual realism compared to existing methods.

本研究解决了通过稀疏2D X射线（如单视图或正交双视图）重建3D胸部体积时，现有模型在结构准确性和视觉真实性之间的权衡问题。提出的DuoLift-GAN模型通过独立提升2D图像及其特征为3D表示，结合掩蔽损失函数，使得重建更加关注关键解剖区域，从而显著提高了重建的准确性和视觉效果。

双提升生成对抗网络：通过单视图和双平面X射线重建CT