多视角影像配准新方法，提升手术导航精度与鲁棒性。
Robust and Accurate Multi-view 2D/3D Image Registration with Differentiable X-ray Rendering and Dual Cross-view Constraints
- 用可微分X射线渲染+跨视角约束损失，增强配准稳定性。
- 在DeepFluoro数据集上达0.79±2.17mm的均值目标配准误差。
- 适合需要高精度3D重建的术中导航场景，如骨科手术。
稳健准确的2D/3D配准对术中导航至关重要，需将术前模型与同一解剖结构的术中影像对齐。为应对单张影像视野受限的问题，需借助多张术中影像实现多视角2D/3D配准。本文提出一种两阶段的多视角刚体配准方法：第一阶段设计联合损失函数，同时考虑预测姿态与真实姿态的差异，以及模拟影像与实际观测影像间的不相似性（如归一化互相关）；更重要的是，引入针对姿态和图像损失的跨视角训练损失项，显式施加跨视角约束。第二阶段进行测试时优化，以精修粗略估计的姿态。该方法利用多视角投影姿态的相互约束，提升注册鲁棒性。在DeepFluoro数据集六个样本上，本方法实现0.79±2.17mm的均值目标注册误差（mTRE），优于现有最先进算法。
原文摘要 · Abstract (English)
Robust and accurate 2D/3D registration, which aligns preoperative models with intraoperative images of the same anatomy, is crucial for successful interventional navigation. To mitigate the challenge of a limited field of view in single-image intraoperative scenarios, multi-view 2D/3D registration is required by leveraging multiple intraoperative images. In this paper, we propose a novel multi-view 2D/3D rigid registration approach comprising two stages. In the first stage, a combined loss function is designed, incorporating both the differences between predicted and ground-truth poses and the dissimilarities (e.g., normalized cross-correlation) between simulated and observed intraoperative images. More importantly, additional cross-view training loss terms are introduced for both pose and image losses to explicitly enforce cross-view constraints. In the second stage, test-time optimization is performed to refine the estimated poses from the coarse stage. Our method exploits the mutual constraints of multi-view projection poses to enhance the robustness of the registration process. The proposed framework achieves a mean target registration error (mTRE) of $0.79 \pm 2.17$ mm on six specimens from the DeepFluoro dataset, demonstrating superior performance compared to state-of-the-art registration algorithms.
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