Ultrasound imaging has been widely used in clinical examinations owing to the
advantages of being portable, real-time, and radiation-free. Considering the
potential of extensive deployment of autonomous examination systems in
hospitals, robotic US imaging has attracted increased attention. However, due
to the inter-patient variations, it is still challenging to have an optimal
path for each patient, particularly for thoracic applications with limited
acoustic windows, e.g., intercostal liver imaging. To address this problem, a
class-aware cartilage bone segmentation network with geometry-constraint
post-processing is presented to capture patient-specific rib skeletons. Then, a
dense skeleton graph-based non-rigid registration is presented to map the
intercostal scanning path from a generic template to individual patients. By
explicitly considering the high-acoustic impedance bone structures, the
transferred scanning path can be precisely located in the intercostal space,
enhancing the visibility of internal organs by reducing the acoustic shadow. To
evaluate the proposed approach, the final path mapping performance is validated
on five distinct CTs and two volunteer US data, resulting in ten pairs of CT-US
combinations. Results demonstrate that the proposed graph-based registration
method can robustly and precisely map the path from CT template to individual
patients (Euclidean error: $2.21\pm1.11~mm$).

通过使用神经网络实现软骨骨分割和基于骨骼图的非刚性配准方法，能够精确地将 CT 通路映射到个体患者上，提高内脏器官的可视性。