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).

翻译：自主超声扫描技术近年来受到广泛关注，被认为有望克服传统超声检查中操作者间差异等局限性。然而，如何将规划好的通用图谱扫描轨迹自主且精准地迁移至不同患者的当前设置仍具挑战性，尤其在声窗受限的胸部应用中。为解决此问题，我们提出一种基于骨架图的非刚性配准方法，利用皮下骨表面特征（而非皮肤表面）适配患者特异性属性。为此，自组织映射被先后两次用于输入点云统一与关键点提取。随后采用最小生成树生成连接所有关键点的树状图。为准确表征肋软骨轮廓以实现源点云与目标点云匹配，通过最大化每根肋骨路径连续性对树状图提取路径进行优化。为验证该方法，我们从一名志愿者的超声数据中手动提取软骨点云，并从七名不同患者的CT数据中提取软骨点云。结果表明，所提基于图的配准方法在适应跨患者差异方面比ICP算法更有效且鲁棒（7例CT上距离误差均值/标准差：5.0/1.9 mm vs 8.6/6.7 mm）。