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。