用GAN从单/双视角X光重建3D胸部影像，更准更真。
DuoLift-GAN:Reconstructing CT from Single-view and Biplanar X-Rays with Generative Adversarial Networks
- 双分支结构分别提升2D图像与特征到3D，再融合解码
- 新设计掩码损失使关键解剖区域重建精度提升32%
- 适合术中快速获取3D影像的临床场景
计算机断层扫描（CT）虽能提供高分辨率三维医学影像，但成本高、耗时长，在术中场景常难以获取（Organization et al. 2011）。近年来研究尝试从稀疏二维X光片（如单视图或正交双视图）重建三维胸腔体积。然而现有模型多以平面方式处理2D图像，侧重视觉真实感而忽视结构准确性。本文提出双路径生成对抗网络（DuoLift-GAN），其双分支独立将2D图像及其特征升维至3D表示，再融合为统一3D特征图并解码生成完整3D胸腔体积，实现更丰富的三维信息捕捉。同时提出掩码损失函数，引导重建聚焦于关键解剖区域，显著提升结构准确性和视觉质量。实验表明，DuoLift-GAN在重建精度上优于现有方法，且视觉真实性更佳。
原文摘要 · Abstract (English)
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.
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