  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 inter-operator variations, resulting images
highly depend on the experience of sonographers. This work proposes an
intelligent robotic sonographer to autonomously "explore" target anatomies and
navigate a US probe to a relevant 2D plane by learning from the 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 disentangle the task-related and domain features in latent space.
The robotic localization is carried out in coarse-to-fine mode based on the
predicted reward associated with B-mode images. To validate the effectiveness
of the proposed reward inference network, representative experiments were
performed on vascular phantoms ("line" target), two types of ex-vivo animal
organs (chicken heart and lamb kidney) phantoms ("point" target) and in-vivo
human carotids, respectively. To further validate the performance of the
autonomous acquisition framework, physical robotic acquisitions were performed
on three phantoms (vascular, chicken heart, and lamb kidney). The results
demonstrated that the proposed advanced framework can robustly work on a
variety of seen and unseen phantoms as well as in-vivo human carotid data.
