As medical ultrasound is becoming a prevailing examination approach nowadays, robotic ultrasound systems can facilitate the scanning process and prevent professional sonographers from repetitive and tedious work. Despite the recent progress, it is still a challenge to enable robots to autonomously accomplish the ultrasound examination, which is largely due to the lack of a proper task representation method, and also an adaptation approach to generalize learned skills across different patients. To solve these problems, we propose the latent task representation and the robotic skills adaptation for autonomous ultrasound in this paper. During the offline stage, the multimodal ultrasound skills are merged and encapsulated into a low-dimensional probability model through a fully self-supervised framework, which takes clinically demonstrated ultrasound images, probe orientations, and contact forces into account. During the online stage, the probability model will select and evaluate the optimal prediction. For unstable singularities, the adaptive optimizer fine-tunes them to near and stable predictions in high-confidence regions. Experimental results show that the proposed approach can generate complex ultrasound strategies for diverse populations and achieve significantly better quantitative results than our previous method.

翻译：随着医疗超声检查成为当今主流的诊断手段，机器人超声系统可显著优化扫描流程，减轻专业超声医师重复性劳动。尽管近年来取得了进展，但如何使机器人自主完成超声检查仍面临挑战，主要问题在于缺乏合适的任务表征方法，以及缺乏将习得技能迁移至不同患者的自适应策略。针对上述问题，本文提出基于潜在任务表征与机器人技能自适应机制的自主超声方法。在离线阶段，通过完全自监督框架将多模态超声技能融合并封装为低维概率模型，该模型综合考量临床演示的超声图像、探头方位及接触力参数。在线阶段，概率模型将筛选并评估最优预测值。针对不稳定奇点，自适应优化器可将其微调至高置信度区域的近稳定预测值。实验结果表明，所提方法能为不同人群生成复杂的超声策略，且定量指标显著优于我们先前的方法。