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.

翻译：计算机断层扫描（CT）能提供高度精细的三维（3D）医学图像，但其成本高昂、耗时较长，且在术中环境中往往难以获取（Organization等人，2011）。近期研究探索了从稀疏二维（2D）X射线（如单视角或正交双视角图像）重建3D胸部容积的方法。然而，现有模型倾向于以平面方式处理2D图像，将视觉逼真度置于结构准确性之上。本文提出DuoLift生成对抗网络（DuoLift-GAN），该新颖架构采用双分支结构，可分别将2D图像及其特征提升至3D表示。这些3D输出被融合为统一的3D特征图，并解码为完整的3D胸部容积，从而实现对更丰富3D信息的捕获。我们还提出一种掩码损失函数，可将重建过程导向关键解剖区域，以提升结构准确性与视觉质量。本文证明，相较于现有方法，DuoLift-GAN在实现卓越视觉逼真度的同时，能显著提高重建精度。