IXGS-术中稀疏任意姿态真实X射线的三维重建
IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays
AI总结：
本研究扩展R^2-高斯溅射框架，无需预训练即可从稀疏任意姿态真实X射线重建解剖一致的三维体积，引入风格迁移标准化提升重建质量，离体实验和专家评估证实其在手术导航中的临床价值。
摘要：
脊柱外科是一种高风险干预，需要精确执行，通常依赖于基于图像的导航系统。近年来，监督学习方法在从稀疏荧光透视数据重建三维脊柱解剖结构方面受到关注，显著减少了对高辐射三维成像系统的依赖。然而，这些方法通常需要大量带注释的训练数据，并且可能难以在不同患者解剖结构或成像条件下泛化。实例学习方法如高斯溅射可提供一种替代方案，避免大量的注释需求。虽然高斯溅射在新视图合成方面显示出潜力，但其在稀疏、任意姿态的真实术中X射线上的应用在很大程度上仍未探索。本研究通过扩展$R^2$-高斯溅射框架来解决这一局限，在这些挑战性条件下重建解剖一致的三维体积。我们引入了一种基于解剖引导的放射影像标准化步骤，使用风格迁移，提高跨视图的视觉一致性，并增强重建质量。值得注意的是，我们的框架无需预训练，因此固有地适应新患者和解剖结构。我们使用离体数据集评估了我们的方法。专家手术评估确认了三维重建在导航中的临床实用性，尤其是使用20至30个视图时，并强调了标准化对解剖清晰度的益处。通过定量二维指标（PSNR/SSIM）的基准测试确认了与理想化设置相比的性能权衡，但也验证了标准化相对于原始输入的改进。这项工作证明了从任意稀疏视图X射线进行基于实例的体积重建的可行性，推进了术中三维成像在外科导航中的应用。
英文摘要：
Spine surgery is a high-risk intervention demanding precise execution, often supported by image-based navigation systems. Recently, supervised learning approaches have gained attention for reconstructing 3D spinal anatomy from sparse fluoroscopic data, significantly reducing reliance on radiation-intensive 3D imaging systems. However, these methods typically require large amounts of annotated training data and may struggle to generalize across varying patient anatomies or imaging conditions. Instance-learning approaches like Gaussian splatting could offer an alternative by avoiding extensive annotation requirements. While Gaussian splatting has shown promise for novel view synthesis, its application to sparse, arbitrarily posed real intraoperative X-rays has remained largely unexplored. This work addresses this limitation by extending the $R^2$-Gaussian splatting framework to reconstruct anatomically consistent 3D volumes under these challenging conditions. We introduce an anatomy-guided radiographic standardization step using style transfer, improving visual consistency across views, and enhancing reconstruction quality. Notably, our framework requires no pretraining, making it inherently adaptable to new patients and anatomies. We evaluated our approach using an ex-vivo dataset. Expert surgical evaluation confirmed the clinical utility of the 3D reconstructions for navigation, especially when using 20 to 30 views, and highlighted the standardization's benefit for anatomical clarity. Benchmarking via quantitative 2D metrics (PSNR/SSIM) confirmed performance trade-offs compared to idealized settings, but also validated the improvement gained from standardization over raw inputs. This work demonstrates the feasibility of instance-based volumetric reconstruction from arbitrary sparse-view X-rays, advancing intraoperative 3D imaging for surgical navigation.
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