Abstract
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.
Abstract (translated)
脊柱手术是一项高风险的干预措施，要求精确执行，并且通常需要图像导航系统的支持。最近，监督学习方法因能从稀疏的荧光透视数据中重建三维脊柱解剖结构而受到关注，这大大减少了对放射强度高的3D成像系统的依赖。然而，这些方法通常需要大量的注释训练数据，并可能难以在不同的患者解剖结构或成像条件下进行泛化。实例学习方法（如高斯点阵）通过避免大量标注要求提供了一种替代方案。尽管高斯点阵法在新颖视图合成方面显示出潜力，但将其应用于稀疏、任意角度的实时手术X射线的应用尚属鲜有探索。 本研究旨在解决这一限制，扩展了$R^2$-高斯点阵框架以在这种具有挑战性的条件下重建解剖一致的三维体积。我们引入了一步基于解剖引导的放射学标准化步骤，通过样式转换改善跨视图的一致性并提高重建质量。值得注意的是，我们的框架不需要预训练，从而使其能够适应新的患者和解剖结构。 我们在一个离体数据集上评估了这种方法，并由专家手术评价确认3D重建在导航中的临床实用性，尤其是在使用20到30个视角时，特别强调标准化对解剖清晰度的益处。通过定量二维指标（PSNR/SSIM）进行基准测试证实，在理想设置中存在性能折衷，但也验证了从原始输入到标准化改进的有效性。 这项工作展示了基于实例的体积重建技术从任意稀疏视图X射线获取中的可行性，并推动了手术导航中的实时3D成像技术。
URL
https://arxiv.org/abs/2504.14699
