全局观察，局部细化：用于稳健徒手三维超声重建的全局视觉引导和局部超声线索

中文摘要 AI 辅助

徒手三维超声成像因其直观的体积可视化、易用性和低成本而受到越来越多关注。然而，精确的三维重建严重依赖稳定的探头姿态估计，现有无跟踪方法仍易受累积姿态误差影响，尤其是在长扫描轨迹上。为解决此局限，我们提出一个全局到局部的姿态估计框架，利用外部相机观测进行全局稳定定位，利用B模式超声图像进行解剖学感知的局部细化。具体包括一个双相机分支通过跨相机视图和时间观测进行上下文特征聚合以估计全局一致的探头轨迹，一个B模式分支从连续超声图像进行解剖特征聚合以捕捉组织相关局部运动线索。一个跨模态融合模块随后整合上下文相机特征和解剖超声特征以预测姿态残差并在变换空间细化相机衍生估计。此外，一个多尺度姿态损失约束多个时间范围内的相对运动以抑制长时间扫描中的累积漂移。该框架在体模和体内数据集上得到验证。在两个使用不同机器收集的内部数据集（FUSION-J和FUSION-L）上，所提的US + Dual-Cam模型将平均轨迹漂移分别降至1.67毫米和1.29毫米，比强大的双相机基线分别提高了16.50%和27.12%，同时大幅优于仅超声姿态估计（漂移>13毫米）。在体内前臂动脉重建中，它实现了1.58毫米的豪斯多夫距离，证明了该方法在实际临床场景中的有效性。

英文摘要

Freehand 3-D ultrasound (US) imaging has attracted increasing attention owing to its intuitive volumetric visualization, ease of use, and low cost. However, accurate 3-D reconstruction critically depends on stable probe pose estimation, yet existing trackerless methods remain susceptible to accumulated pose errors, particularly over long scanning trajectories. To address this limitation, we propose a global-to-local pose estimation framework that exploits external camera observations for globally stable localization and B-mode US images for anatomy-aware local refinement. Specifically, the framework comprises a dual-camera branch that performs contextual feature aggregation across camera views and temporal observations to estimate a globally consistent probe trajectory, and a B-mode branch that performs anatomical feature aggregation from sequential US images to capture tissue-dependent local motion cues. A cross-modal fusion module subsequently integrates the contextual camera features and anatomical US features to predict pose residuals and refine the camera-derived estimates in the transformation space. Furthermore, a multi-scale pose loss constrains relative motion over multiple temporal horizons to suppress accumulated drift during extended scans. The proposed framework is validated on phantom and in vivo datasets. On two in-house datasets (FUSION-J and FUSION-L) collected using different machines, the proposed US + Dual-Cam model reduces average trajectory drift to 1.67 mm and 1.29 mm, representing improvement of 16.50% and 27.12%, respectively, over a strong dual-camera baseline, while substantially outperforming US-only pose estimation (>13 mm drift). In in vivo forearm arteries reconstruction, it achieves Hausdorff distances of 1.58 mm, demonstrating the effectiveness of the proposed method on real clinical scenarios.

发表机构

• Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）
• Multi-Scale Medical Robotics Center（多尺度医疗机器人中心）
• Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong（香港中文大学机械与自动化工程系）

机构由 AI 辅助整理，请以论文原文为准。