PolyPose: Localizing Deformable Anatomy in 3D from Sparse 2D X-ray Images using Polyrigid Transforms.

ArXiv Pub Date : 2025-05-28
Vivek Gopalakrishnan, Neel Dey, Polina Goll
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Abstract

Determining the 3D pose of a patient from a limited set of 2D X-ray images is a critical task in interventional settings. While preoperative volumetric imaging (e.g., CT and MRI) provides precise 3D localization and visualization of anatomical targets, these modalities cannot be acquired during procedures, where fast 2D imaging (X-ray) is used instead. To integrate volumetric guidance into intraoperative procedures, we present PolyPose, a simple and robust method for deformable 2D/3D registration. PolyPose parameterizes complex 3D deformation fields as a composition of rigid transforms, leveraging the biological constraint that individual bones do not bend in typical motion. Unlike existing methods that either assume no inter-joint movement or fail outright in this under-determined setting, our polyrigid formulation enforces anatomically plausible priors that respect the piecewise rigid nature of human movement. This approach eliminates the need for expensive deformation regularizers that require patient- and procedure-specific hyperparameter optimization. Across extensive experiments on diverse datasets from orthopedic surgery and radiotherapy, we show that this strong inductive bias enables PolyPose to successfully align the patient's preoperative volume to as few as two X-rays, thereby providing crucial 3D guidance in challenging sparse-view and limited-angle settings where current registration methods fail.

PolyPose:使用Polyrigid变换从稀疏的2D x射线图像在3D中定位可变形的解剖结构。
从一组有限的2D x射线图像中确定患者的3D姿势是介入设置的关键任务。虽然术前体积成像(如CT和MRI)提供了精确的三维定位和解剖目标的可视化,但在手术过程中无法获得这些模式,而使用快速二维成像(x射线)代替。为了将体积引导整合到术中过程中,我们提出了PolyPose,一种简单而稳健的可变形2D/3D配准方法。PolyPose将复杂的3D变形场参数化为刚性变换的组合,利用个体骨骼在典型运动中不会弯曲的生物约束。不像现有的方法,要么假设没有关节间运动,要么在这种不确定的环境下完全失败,我们的多刚性公式强制执行解剖学上合理的先验,尊重人类运动的分段刚性本质。这种方法消除了对昂贵的变形正则化器的需要,这些变形正则化器需要针对患者和手术特定的超参数优化。通过对来自骨科手术和放疗的各种数据集的广泛实验,我们表明,这种强感应偏置使PolyPose能够成功地将患者的术前体积与两张x射线图像对齐,从而在具有挑战性的稀疏视图和有限角度设置中提供关键的3D指导,而当前的配准方法无法实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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