Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations

TL;DR

This paper introduces an intelligent robotic sonographer that learns to autonomously explore and navigate ultrasound imaging by inferring a neural reward function from expert demonstrations, improving robustness across various targets and subjects.

Contribution

It proposes a mutual information-based disentangled reward learning framework for autonomous ultrasound probe navigation from few demonstrations, enhancing generalization and robustness.

Findings

Effective in diverse phantom and in-vivo data

Robust autonomous probe localization achieved

Generalizes well to unseen targets and subjects

Abstract

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.