机器人超声探头自动贴合皮肤，提升成像质量与操作一致性。
A-SEE2.0: Active-Sensing End-Effector for Robotic Ultrasound Systems with Dense Contact Surface Perception Enabled Probe Orientation Adjustment
- 双RGB-D相机实时感知皮肤曲面，动态调整探头角度保持垂直
- 平坦表面法向误差仅2.47±1.25度，人体模型上为12.19±5.81度
- 适用于临床实测，减轻操作依赖，适合资源有限场景
传统自由手超声依赖操作者技能，易导致结果不一致且增加人员负担。机器人超声系统（RUSS）通过标准化与自动化方案应对这一问题，尤其在专业人员匮乏的环境中具有价值。本文提出一种新型RUSS系统，采用双RGB-D深度相机实现探头与皮肤表面的实时法向对齐，关键在于无需术前数据即可在不规则表面保持正交。实验验证表明，该系统在仿体模型上实现稳定法向定位精度：平坦表面误差为2.47±1.25度，人体模型表面为12.19±5.81度，所获超声图像质量与人工扫描相当。本工作进一步通过活体前臂超声检查测试了A-SEE2.0的实际表现，验证其在临床应用中的可行性。
原文摘要 · Abstract (English)
Conventional freehand ultrasound (US) imaging is highly dependent on the skill of the operator, often leading to inconsistent results and increased physical demand on sonographers. Robotic Ultrasound Systems (RUSS) aim to address these limitations by providing standardized and automated imaging solutions, especially in environments with limited access to skilled operators. This paper presents the development of a novel RUSS system that employs dual RGB-D depth cameras to maintain the US probe normal to the skin surface, a critical factor for optimal image quality. Our RUSS integrates RGB-D camera data with robotic control algorithms to maintain orthogonal probe alignment on uneven surfaces without preoperative data. Validation tests using a phantom model demonstrate that the system achieves robust normal positioning accuracy while delivering ultrasound images comparable to those obtained through manual scanning. A-SEE2.0 demonstrates 2.47 ${\pm}$ 1.25 degrees error for flat surface normal-positioning and 12.19 ${\pm}$ 5.81 degrees normal estimation error on mannequin surface. This work highlights the potential of A-SEE2.0 to be used in clinical practice by testing its performance during in-vivo forearm ultrasound examinations.
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