发表机构
University of Texas at Arlington; Southeast University（德克萨斯大学阿灵顿分校; 东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究术中二维/三维配准问题，提出基于患者无关合成预训练和球面相似性学习的框架，先预训练模型学习可转移表示，再用目标CT投影适应新患者，引入无分割域随机化策略，实验证明该方法能降低训练需求并保持配准精度。
AI 中文摘要
术中二维/三维配准将术前CT体积与术中X射线或荧光透视图像对齐，对图像引导干预至关重要。近期基于学习和可微配准方法显示出良好准确性，尤其是在有大量数字重建射线照片（DRR）的患者特异性设置中。但为每个新患者从头训练单独模型计算效率低且限制实际应用。本文提出基于患者无关合成预训练和球面相似性学习的高效患者特异性二维/三维配准框架。模型先在多个CT体积生成的合成DRR上预训练以学习可转移的姿态敏感表示，再用目标CT的有限数量合成投影适应新患者。引入无分割域随机化策略提高合成到真实的鲁棒性。适应后的模型提供初始姿态估计，通过球面相似性学习和可微Levenberg-Marquardt优化进一步细化。实验评估患者无关合成预训练能否提高患者特异性配准效率，结果表明其可显著降低患者特异性训练需求并保持准确的术中二维/三维配准。
英文摘要
Intraoperative 2D/3D registration aligns preoperative CT volumes with intraoperative X-ray or fluoroscopic images and is essential for image-guided interventions. Recent learning-based and differentiable registration methods have shown promising accuracy, especially in patient-specific settings where abundant digitally reconstructed radiographs (DRRs) can be synthesized from the target CT. However, training a separate patient-specific model from scratch for every new patient is computationally inefficient and limits practical deployment. In this work, we propose an efficient patient-specific 2D/3D registration framework based on patient-agnostic synthetic pretraining and spherical similarity learning. The model is first pretrained on synthetic DRRs generated from multiple CT volumes to learn transferable pose-sensitive representations, and is then adapted to a new patient using only a limited number of synthetic projections from the target CT. To improve synthetic-to-real robustness without requiring anatomical labels, we introduce a segmentation-free domain randomization strategy that perturbs image intensity, projection physics, field-of-view, occlusion, and fluoroscopic artifacts. The adapted model provides an initial pose estimate, which is further refined using spherical similarity learning and differentiable Levenberg-Marquardt optimization. Experiments on multiple anatomical datasets evaluate whether patient-agnostic synthetic pretraining can improve the efficiency of patient-specific registration, with particular focus on the trade-off between adaptation cost and registration accuracy. The results demonstrate that patient-agnostic synthetic pretraining can significantly reduce patient-specific training requirements while preserving accurate intraoperative 2D/3D registration.
