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.

翻译：超声（US）成像因其实时、无辐射的优势，广泛应用于生物特征测量和内脏器官诊断。然而，由于操作者之间的高度差异性，所成图像高度依赖于操作者的经验。本研究提出了一种智能机器人超声医生，通过向专家学习，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。通过自监督方式下的排序成对图像比较方法，利用神经奖励函数推断专家潜在的底层生理学知识，该过程可视为理解"超声语言"。为提升跨患者差异的泛化能力，网络通过估计互信息以显式提取隐空间中的任务相关特征与领域特征。此外，开发了基于高斯分布的滤波器，用于自动评估并考虑专家示范的质量。基于与B模式图像相关的预测奖励，机器人定位采用由粗到精的模式进行。为验证所提方法的性能，分别针对"线"目标与"点"目标，在血管体模及离体动物器官体模（鸡心和羊肾）上开展了代表性实验。结果表明，该先进框架可稳健适用于不同类型已知与未知体模。