Ultrasound (US) has been widely used in daily clinical practice for screening
internal organs and guiding interventions. However, due to the acoustic shadow
cast by the subcutaneous rib cage, the US examination for thoracic application
is still challenging. To fully cover and reconstruct the region of interest in
US for diagnosis, an intercostal scanning path is necessary. To tackle this
challenge, we present a reinforcement learning (RL) approach for planning
scanning paths between ribs to monitor changes in lesions on internal organs,
such as the liver and heart, which are covered by rib cages. Structured
anatomical information of the human skeleton is crucial for planning these
intercostal paths. To obtain such anatomical insight, an RL agent is trained in
a virtual environment constructed using computational tomography (CT) templates
with randomly initialized tumors of various shapes and locations. In addition,
task-specific state representation and reward functions are introduced to
ensure the convergence of the training process while minimizing the effects of
acoustic attenuation and shadows during scanning. To validate the effectiveness
of the proposed approach, experiments have been carried out on unseen CTs with
randomly defined single or multiple scanning targets. The results demonstrate
the efficiency of the proposed RL framework in planning non-shadowed US
scanning trajectories in areas with limited acoustic access.

通过强化学习规划扫描路径，以实现在具有受限声学访问的区域中进行无阴影超声扫描轨迹的有效性验证。