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.

翻译：超声成像因其实时性和无辐射优势，被广泛用于生物特征测量和内脏器官诊断。然而，由于操作者间存在差异，所得图像高度依赖超声医师的经验。本文提出了一种智能机器人超声医师，通过从专家处学习，自主"探索"目标解剖结构并将超声探头导航至相关二维平面。通过自监督方式的排序成对图像比较方法，利用神经奖励函数推断专家所蕴含的高层生理学知识，该过程可理解为理解"超声语言"。为提升克服患者间差异的泛化能力，网络通过估计互信息显式解耦潜在空间中与任务相关和领域相关的特征。基于B模式图像对应的预测奖励，采用粗到细模式实现机器人定位。为验证所提奖励推断网络的有效性，分别对血管模体（"线"目标）、两类离体动物器官模体（鸡心和羊肾）（"点"目标）及活体人类颈动脉进行了代表性实验。进一步地，为验证自主采集框架的性能，在三种模体（血管、鸡心和羊肾）上完成了物理机器人采集实验。结果表明，所提先进框架可鲁棒地适用于多种已知/未知模体及活体人类颈动脉数据。