Ultrasound (US) acquisition in standard clinical procedures requires manual operation of the probe based on the interpretation of the image. A robotic system for autonomous US acquisitions holds great promise to relieve the workload of sonographers, improve access to care, yield more standardized imaging results, and circumvent the need for direct patient contact. In this paper, we provide an overview of the current status of systems and techniques for autonomous robotic US acquisitions in the literature. Specifically, we first present a framework to standardize the increasing levels of autonomy in robotic ultrasonography and briefly discuss the non-autonomous and semi-autonomous systems. Then, we systematically review the existing autonomous robotic systems developed for extracorporeal US acquisitions based on the hardware components, workflow and applications. Besides, we summarize three important topics related with the autonomous US acquisitions, including the scanning path planning, contact force control and image quality optimization, and introduce current methods and techniques proposed on each topic. Finally, we discuss the challenges and provide some potential directions for future research. We believe that the presented efforts in autonomous robotic US acquisitions will pave the way for a promising future of medical care and benefit a broad spectrum of clinical procedures.

Simultaneous magnetic actuation and localization (SMAL) is a promising technology for accelerating and positioning the capsule in the human intestine for wireless capsule endoscopy (WCE). In this article, we propose a novel system that uses a rotating magnetic actuator and an external sensor array to achieve closed-loop SMAL for a capsule with two embedded magnetic rings. First, the state of the capsule is detected as ``Stuck,'' ``Synchronous,'' or ``Missing'' by studying the relationship between the theoretical actuating magnetic field and the measured total magnetic field. Then, the undesired interference with the localization system caused by the actuator is eliminated with an integral filter-based approach. Different models are proposed to solve the pose of the capsule according to the different states of the capsule, and the localization result is used to update the pose of the actuator to close the loop. Extensive experiments on phantoms and animal organs with different environmental conditions are carried out to validate the proposed framework. The state detection accuracy achieves 96.7%, and the capsule can be located with an accuracy of 5.5 mm and 5.2° in position and orientation, respectively. Experimental results show the feasibility of our proposed system and demonstrate the robustness, accuracy, actuation efficiency, and closed-loop performance of the system.

Simultaneous magnetic actuation and localization is a promising technology to realize active wireless capsule endoscopy. In this article, an adaptive approach is proposed to efficiently propel and precisely locate a magnetically actuated capsule in unknown complex tubular environments. In order to track the capsule in real time in a large workspace, we improve upon our previous external sensor-based magnetic localization strategies by proposing an adaptive method to automatically activate an optimal subset of sensors during the capsule movement and developing a simplified multiple objects tracking algorithm to estimate the six-dimensional pose of the capsule in real time. Moreover, based on a study of capsule locomotion in tubular environments with different shapes, we propose to dynamically adjust the pose of the actuator to achieve efficient and robust propulsion of the capsule in an unknown tubular environment. The effectiveness of our proposed system is validated in extensive experiments on phantoms and ex vivo animal organs. Compared with the state-of-the-art, our method can realize the closed-loop control of a magnetic capsule without requiring sensing modules to be placed inside the capsule, which can reduce the space constraints and energy consumption of the capsule. Moreover, the system and algorithms for adaptive actuation and localization can provide an improved propulsion speed, workspace range, and localization update frequency as well as a comparable localization accuracy.

Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers. In this work, we propose a novel dual-agent framework that integrates a reinforcement learning (RL) agent and a deep learning (DL) agent to jointly determine the movement of the US probe based on the real-time US images, in order to mimic the decision-making process of an expert sonographer to achieve autonomous standard view acquisitions in spinal sonography. Moreover, inspired by the nature of US propagation and the characteristics of the spinal anatomy, we introduce a view-specific acoustic shadow reward to utilize the shadow information to implicitly guide the navigation of the probe toward different standard views of the spine. Our method is validated in both quantitative and qualitative experiments in a simulation environment built with US data acquired from 17 volunteers. The average navigation accuracy toward different standard views achieves $5.18mm/5.25^{\circ }$ and $12.87mm/17.49^{\circ }$ in the intra- and inter-subject settings, respectively. The results demonstrate that our method can effectively interpret the US images and navigate the probe to acquire multiple standard views of the spine.

Ultrasound image acquisition in conventional transesophageal echocardiography (TEE) requires complex manual operation of the probe in the esophagus based on the interpretation of ultrasound images and in-depth knowledge of the cardiac anatomy. In this work, we formulate the TEE probe guidance task as a reinforcement learning (RL) problem, and present the first learning-based solution to 3-DOF control of a TEE probe based on the ultrasound image feedback, named RL-TEE, in order to mimic the visual search and navigation strategies of expert echocardiographers. The probe-tissue interaction in TEE is carefully modeled in our framework by considering both the requirements for navigation towards the standard views and compliance in the esophageal environment. Furthermore, we propose a hybrid deep Q-network model that augments a convolutional neural network backbone with self-attention mechanisms to better capture spatial information in ultrasound images to guide navigation decisions. The presented methods are preliminarily validated in a TEE simulation environment built with data from 25 subjects to acquire four standard views of the heart. Our results show that the proposed method can effectively learn to accurately and compliantly guide the probe movement for TEE standard view acquisition tasks and has a good generalization ability to unseen patient data. Note to Practitioners —The motivation of this paper is to realize 3-DOF movement guidance of a TEE probe to acquire the standard views of the heart based on the real-time images, which can be applied to existing robotic control systems or used to assist novice echocardiographers in TEE examination, thereby relieving operator workload and improving ease of use. This paper suggests a novel approach that uses the deep RL technique to achieve automatic interpretation of TEE images and intelligent guidance of the probe movement. The RL framework is designed to take into account both the navigation efficiency and compliance with the esophageal environment for the targeted intracorporeal application. A hybrid deep Q-network model that augments a convolutional neural network with attention mechanisms is designed to better capture spatial information from ultrasound images to predict the probe movement. The effectiveness of the framework is preliminarily validated in extensive experiments in a simulation environment built with real patient data. The proposed method can be applied in clinical use to provide real-time TEE probe guidance for novice echocardiographers, and can be integrated with a robotic system to fully automate the TEE acquisition, thereby relieving the doctors from tedious manual operation to focus on the diagnosis and treatment.

This paper introduces a novel localization approach for active capsule endoscopy that, for the first time, combines external magnetic field sensing and internal inertial sensing to realize 6-DOF pose estimation of a magnetic capsule robot. It utilizes an inertial measurement unit embedded in the capsule with an external magnetic sensor array to estimate the 6-DOF pose of the capsule, which does not require complicated structures of the capsule and the actuator or the implementation of specific motions of the magnets, and can achieve accurate and real-time localization of the capsule in a large workspace. We formulate the localization model and analyze the singularities of the method, and present the design approach to determine the configuration of the localization system for efficient and accurate localization in a 0.5 × 0.5 × 0.2 m $^{3}$ workspace. Simulation and real-wold experiments are conducted to validate the effectiveness of the proposed localization strategy. Our results show that the proposed method can achieve a localization accuracy of 5.35 $\pm$ 2.89 mm and 1.46 $\pm$ 1.09 $^\circ$ in position and orientation in the real-time tracking task at an update rate of $60 \,\mathrm{Hz}$ . The presented method can be integrated with any magnetic actuation method to achieve closed-loop control of a magnetic capsule robot in the human body.