AI 中文总结
提出LiftXR框架，通过双平面X射线生成解剖布局引导CT重建，在公开数据集上优于现有方法，提升了重建CT的解剖保真度。
AI 中文摘要
X射线成像可近似建模为对 underlying 体衰减场的投影，每次测量记录对应射线路径上的累积衰减值。仅用少量X射线视图重建CT体积属于严重不适定问题，因为投影会丢失深度信息，使解剖区域的三维位置及其对应的强度分布高度纠缠且模糊。我们观察到，一旦建立了解剖区域的空间结构，估计其CT强度会显著更易处理。受此启发，我们提出LiftXR，一种交错式、几何引导的框架，将空间布局恢复明确融入CT重建。具体而言，布局提升器首先从双平面X射线生成三维解剖布局，为强度渲染器重建CT体积提供空间引导；解剖解析器随后对重建结果进行体积感知，利用其空间解析的边界和强度线索恢复更精细的解剖布局。这种从投影条件布局生成到重建条件解剖感知的过渡，使解析出的布局能为区域特定强度校准提供反馈。在两个公开数据集上的大量实验表明，LiftXR始终优于近期X射线到CT重建方法，达到新的最优水平；此外，重建的CT在外部下游分割任务中表现更优，说明其解剖保真度得到提升，代码将公开。
英文摘要
X-ray imaging can be approximately modeled as the projection of an underlying volumetric attenuation field, with each measurement recording the accumulated attenuation along a corresponding ray path. Reconstructing a CT volume from only a few X-ray views is therefore severely ill-posed, as the projections collapse depth information and leave 3D locations of anatomical regions and their corresponding intensity distributions highly entangled and ambiguous. We observe that once the spatial organization of anatomical regions is established, estimating their CT intensities becomes substantially more tractable. Motivated by this, we propose LiftXR, an interleaved, geometry-guided framework that explicitly incorporates spatial layout recovery into CT reconstruction. Specifically, a layout lifter first generates a 3D anatomical layout from bi-planar X-rays, providing spatial guidance for an intensity renderer to reconstruct a CT volume. An anatomical parser then performs volumetric perception on the reconstruction, exploiting its spatially resolved boundary and intensity cues to recover a refined anatomical layout. This transition from projection-conditioned layout generation to reconstruction-conditioned anatomical perception allows the parsed layout to provide feedback for region-specific intensity calibration. Extensive experiments on two public datasets demonstrate that LiftXR consistently outperforms recent X-ray-to-CT reconstruction methods, establishing a new state of the art. Moreover, the reconstructed CT achieves superior performance in external downstream segmentation, indicating improved anatomical fidelity. Code will be released.
