用普通摄像头实现无标记3D超声重建,抗漂移且精度达0.88mm。
MLRecon: Robust Markerless Freehand 3D Ultrasound Reconstruction via Coarse-to-Fine Pose Estimation
- 基于视觉大模型与双阶段精修网络,分离高频抖动与低频偏差。
- 在复杂轨迹上位置误差仅0.88mm,表面重建精度亚毫米级。
- 适合资源有限的临床环境,无需额外传感器或昂贵设备。
自由手3D超声成像可灵活使用标准2D探头实现体积成像,但现有追踪方式面临三重困境:有标记系统成本过高,内源式方法需侵入性传感器附加,而无传感器方法则存在严重累积漂移。为此,我们提出MLRecon,一种基于单个通用RGB-D相机的鲁棒无标记3D超声重建框架,实现抗漂移的6维探头姿态追踪。利用视觉基础模型的泛化能力,其流程支持连续无标记探头追踪,并引入视觉引导的发散检测器,自主监控追踪完整性并触发故障恢复,确保扫描不间断。关键的是,我们进一步提出双阶段姿态精修网络,显式分离高频抖动与低频偏移,有效去噪轨迹同时保持操作动作的运动学保真度。实验表明,MLRecon显著优于现有无传感器与传感器辅助方法,在复杂轨迹上平均位置误差低至0.88 mm,生成的3D重建质量高,平均表面精度达亚毫米级。该工作为资源受限临床场景下的低成本、易获取体积超声成像树立了新基准。
原文摘要 · Abstract (English)
Freehand 3D ultrasound (US) reconstruction promises volumetric imaging with the flexibility of standard 2D probes, yet existing tracking paradigms face a restrictive trilemma: marker-based systems demand prohibitive costs, inside-out methods require intrusive sensor attachment, and sensorless approaches suffer from severe cumulative drift. To overcome these limitations, we present MLRecon, a robust markerless 3D US reconstruction framework delivering drift-resilient 6D probe pose tracking using a single commodity RGB-D camera. Leveraging the generalization power of vision foundation models, our pipeline enables continuous markerless tracking of the probe, augmented by a vision-guided divergence detector that autonomously monitors tracking integrity and triggers failure recovery to ensure uninterrupted scanning. Crucially, we further propose a dual-stage pose refinement network that explicitly disentangles high-frequency jitter from low-frequency bias, effectively denoising the trajectory while maintaining the kinematic fidelity of operator maneuvers. Experiments demonstrate that MLRecon significantly outperforms competing sensorless and sensor-aided methods, achieving average position errors as low as 0.88 mm on complex trajectories and yielding high-quality 3D reconstructions with sub-millimeter mean surface accuracy. This establishes a new benchmark for low-cost, accessible volumetric US imaging in resource-limited clinical settings.
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