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.

通过潜在任务表示和机器人技能适应性，该研究提出了自主超声的解决方案，以便实现机器人自动完成超声检查的挑战。该方法通过融合多模式超声技能并将其封装为低维概率模型来实现，实验结果显示其在不同人群中可以生成复杂的超声策略，并且相较于先前方法取得显著更好的定量结果。

自主超声学习：隐含任务表示与机器人技能适应

Learning Autonomous Ultrasound via Latent Task Representation and  Robotic Skills Adaptation

Autonomous ultrasound (US) scanning has attracted increased attention, and it
has been seen as a potential solution to overcome the limitations of
conventional US examinations, such as inter-operator variations. However, it is
still challenging to autonomously and accurately transfer a planned scan
trajectory on a generic atlas to the current setup for different patients,
particularly for thorax applications with limited acoustic windows. To address
this challenge, we proposed a skeleton graph-based non-rigid registration to
adapt patient-specific properties using subcutaneous bone surface features
rather than the skin surface. To this end, the self-organization mapping is
successively used twice to unify the input point cloud and extract the key
points, respectively. Afterward, the minimal spanning tree is employed to
generate a tree graph to connect all extracted key points. To appropriately
characterize the rib cartilage outline to match the source and target point
cloud, the path extracted from the tree graph is optimized by maximally
maintaining continuity throughout each rib. To validate the proposed approach,
we manually extract the US cartilage point cloud from one volunteer and seven
CT cartilage point clouds from different patients. The results demonstrate that
the proposed graph-based registration is more effective and robust in adapting
to the inter-patient variations than the ICP (distance error mean/SD: 5.0/1.9
mm vs 8.6/6.7 mm on seven CTs).

通过基于骨骼图的非刚性配准方法，利用皮下骨表面特征代替皮肤表面实现自动化超声波（US）扫描，为解决常规 US 检查中的人为误差提供了可能，验证结果表明其能有效适应不同患者间的差异。