Autonomous ultrasound (US) imaging has gained increased interest recently,
and it has been seen as a potential solution to overcome the limitations of
free-hand US examinations, such as inter-operator variations. However, it is
still challenging to accurately map planned paths from a generic atlas to
individual patients, particularly for thoracic applications with high
acoustic-impedance bone structures under the skin. To address this challenge, a
graph-based non-rigid registration is proposed to enable transferring planned
paths from the atlas to the current setup by explicitly considering
subcutaneous bone surface features instead of the skin surface. To this end,
the sternum and cartilage branches are segmented using a template matching to
assist coarse alignment of US and CT point clouds. Afterward, a directed graph
is generated based on the CT template. Then, the self-organizing map using
geographical distance is successively performed twice to extract the optimal
graph representations for CT and US point clouds, individually. To evaluate the
proposed approach, five cartilage point clouds from distinct patients are
employed. The results demonstrate that the proposed graph-based registration
can effectively map trajectories from CT to the current setup for displaying US
views through limited intercostal space. The non-rigid registration results in
terms of Hausdorff distance (Mean$\pm$SD) is 9.48$\pm$0.27 mm and the path
transferring error in terms of Euclidean distance is 2.21$\pm$1.11 mm.

利用基于图的非刚性配准将计划的路径从通用图谱准确地映射到个体患者，尤其对于具有高声阻抗骨结构的胸部应用。结果表明，该方法能够在有限的肋间空格内有效地将 CT 到当前设置的轨迹映射为超声成像视图。

胸腔软骨超声 - CT 配准基于密集骨架图

Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph

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。