Ultrasound (US) imaging is widely used for biometric measurement and
diagnosis of internal organs due to the advantages of being real-time and
radiation-free. However, due to high inter-operator variability, resulting
images highly depend on operators' experience. In this work, an intelligent
robotic sonographer is proposed to autonomously "explore" target anatomies and
navigate a US probe to a relevant 2D plane by learning from expert. The
underlying high-level physiological knowledge from experts is inferred by a
neural reward function, using a ranked pairwise image comparisons approach in a
self-supervised fashion. This process can be referred to as understanding the
"language of sonography". Considering the generalization capability to overcome
inter-patient variations, mutual information is estimated by a network to
explicitly extract the task-related and domain features in latent space.
Besides, a Gaussian distribution-based filter is developed to automatically
evaluate and take the quality of the expert's demonstrations into account. The
robotic localization is carried out in coarse-to-fine mode based on the
predicted reward associated to B-mode images. To demonstrate the performance of
the proposed approach, representative experiments for the "line" target and
"point" target are performed on vascular phantom and two ex-vivo animal organ
phantoms (chicken heart and lamb kidney), respectively. The results
demonstrated that the proposed advanced framework can robustly work on
different kinds of known and unseen phantoms.

提出了一种智能机器人超声检查师，能够从专家的经验中自主学习，理解超声检查的语言，通过估计互信息和开发高斯分布滤波器来提高模型的泛化能力，并在血管和动物器官模型中进行了演示实验，证明了该方法的稳健性。