发表机构
Weill Cornell Medicine, Cornell Tech; Technical University of Munich（威尔康奈尔医学院、康奈尔理工学院; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出经人群训练的GeoPose框架，通过投影空间校准实现患者无关的CTA到DSA配准，无需患者特定适配，在配准精度和速度上均优于基线方法，可为下游血管重建提供几何对应关系。
AI 中文摘要
将术中双平面数字减影血管造影（DSA）与术前计算机断层血管造影（CTA）对齐需要快速准确的3D到2D配准。基于优化的方法对初始化敏感，可能需要数百次迭代，而基于学习的方法通常依赖患者特定的训练。我们提出GeoPose，这是一种经过人群训练的框架，可在学习到的规范坐标系中估计C臂位姿，并通过投影空间校准和变换组合将其转换为未见过的CTA的原始坐标系。一个经过人群训练的残差网络会优化位姿，之后可选择进行低开销的图像驱动优化。GeoPose既不需要患者特定的适配，也不需要明确的体积间预配准。在来自20名保留患者的80次DSA观测中，无需优化的GeoPose实现了颈动脉平均投影中心线距离（mPCD）为5.8 mm，clDice为0.45，而表现最佳的基线方法分别为14.5 mm和0.28，耗时仅0.15秒。经过25次优化迭代后，GeoPose在约2秒内达到了4.6 mm的mPCD和0.58的clDice。在相同的计算开销下，以原始坐标系初始化的优化方法分别达到了14.6 mm和0.15。因此，GeoPose提供了具有固定人群级权重的快速原始坐标系配准，以及下游双平面3D血管重建所需的几何对应关系。
英文摘要
Aligning intraoperative biplanar digital subtraction angiography (DSA) to pre-procedural computed tomography angiography (CTA) requires rapid and accurate 3D-to-2D registration. Optimization-based methods are sensitive to initialization and may require hundreds of iterations, whereas learning-based approaches commonly rely on patient-specific training. We propose GeoPose, a population-trained framework that estimates the C-arm pose in a learned canonical frame and transfers it to the native frame of an unseen CTA through projection-space calibration and transform composition. A population-trained residual network refines the pose, followed optionally by low-budget image-driven optimization. GeoPose requires neither patient-specific adaptation nor explicit inter-volume preregistration. On 80 DSA observations from 20 held-out patients, optimization-free GeoPose achieved a carotid mean projected centerline distance (mPCD) of 5.8 mm and a clDice of 0.45, compared with 14.5 mm and 0.28 for the best-performing baseline, while requiring only 0.15 s. After 25 optimization iterations, GeoPose reached an mPCD of 4.6 mm and a clDice of 0.58 in approximately two seconds. Under the same budget, native-initialized optimization achieved 14.6 mm and 0.15, respectively. GeoPose thus provides rapid native-frame registration with fixed population-level weights and the geometric correspondence required for downstream biplanar 3D vascular reconstruction.
