Registration of intraoperative ultrasound (iUS) with preoperative CT represents a significant yet challenging task in the context of radiation-free spinal surgical navigation. The presence of thickness response artifacts in US images poses a considerable obstacle to the accurate extraction of bone boundaries. Furthermore, US-CT registration typically necessitates the detection and correspondence of high-quality landmarks at the initial stage. This can be accomplished by surgeons who have undergone extensive training in the localization of standard spinal US views, enabling them to identify key vertebral landmarks for subsequent precise registration. In this paper, we propose a fully automated iUS registration method that employs a limited number of spinal US views as observation objects. Specifically, three-dimensional vertebral meshes segmented from the preoperative CT images are superimposed on the US images and then fed to the reinforcement learning (RL) agent for sequential decision-making. The proposed method achieves fully automatic US-CT registration without relying on prespecified initialization. This is achieved by training the agent to approach bone surfaces on several randomly selected 2D US views. The instability of RL-based iUS registration is primarily attributable to the difficulty of correlating long-range information within the neural network. To address this issue, we propose a Field of View Separation (FoVS) module. The proposed approach employs separate encoders for US and mesh images, followed by cross-attention aggregation, which facilitates information flow between non-adjacent pixels. This approach enables pretraining of feature extraction on distinct encoders and the application of supplementary loss for enhanced feature matching precision, thereby significantly improving the learning capability and stability of the network. Furthermore, a refinement module is introduced to correct the results of the RL registration, which improves the stability of the registration process. To ascertain the efficacy of each module, action, and auxiliary task, comprehensive experiments are conducted. The results demonstrate that the performance of the RL agent is enhanced by the associated modules and auxiliary tasks. The registration exhibited an angular error of$8.83 \; \pm \; 4.69$degrees and a translational error of$3.34 \; \pm \; 1.42$mm, achieving the state-of-the-art (SOTA) results. It is noteworthy that fine-tuning the model prior to the surgical phase can significantly reduce the registration error, which is a promising outcome for its clinical translation.Note to Practitioners—The objective of this study is to address the issue of image registration using iUS in conjunction with preoperative CT scans in the context of spine surgery. The current 2D/3D image registration methods are constrained by several limitations. Firstly, they often exhibit reduced accuracy, and require high-quality images in substantial quantities. Secondly, there is a lack of effective mechanisms to rectify errors identified after the registration process. This paper proposes a fully automated registration framework based on RL, which incorporates image rendering and mesh clipping to enable continuous adjustment of the pose of 3D data, thereby facilitating 2D/3D registration. The framework employs the distinctive attributes of iUS images and incorporates a refinement module to evaluate registration accuracy, thereby facilitating the rectification of any registration issues. The proposed framework was tested on both sheep lumbar subjects and human lumbar phantoms, demonstrating the highest level of performance to date and indicating its potential for integration into surgical navigation systems.

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 article presents a closed-loop magnetic manipulation framework for robotic transesophageal echocardiography (TEE) acquisitions. Different from previous work on intracorporeal robotic ultrasound acquisitions that focus on continuum robot control, we first investigate the use of magnetic control methods for more direct, intuitive, and accurate manipulation of the distal tip of the probe. We modify a standard TEE probe by attaching a permanent magnet and an inertial measurement unit (IMU) sensor to the probe tip and replacing the flexible gastroscope with a soft tether containing only wires for transmitting ultrasound and IMU data and show that six-degree-of-freedom (DOF) localization and five-DOF closed-loop control of the probe can be achieved with an external permanent magnet based on the fusion of internal inertial measurement and external magnetic field sensing data. The proposed method does not require complex structures or motions of the actuator and the probe compared with existing magnetic manipulation methods. We have conducted extensive experiments to validate the effectiveness of the framework in terms of localization accuracy, update rate, workspace size, and tracking accuracy. In addition, our results obtained on a realistic cardiac tissue-mimicking phantom show that the proposed framework is applicable in real conditions and can generally meet the requirements for teleoperated TEE acquisitions.