Ultrasound is a vital imaging modality utilized for a variety of diagnostic and interventional procedures. However, an expert sonographer is required to make accurate maneuvers of the probe over the human body while making sense of the ultrasound images for diagnostic purposes. This procedure requires a substantial amount of training and up to a few years of experience. In this paper, we propose an autonomous robotic ultrasound system that uses Bayesian Optimization (BO) in combination with the domain expertise to predict and effectively scan the regions where diagnostic quality ultrasound images can be acquired. The quality map, which is a distribution of image quality in a scanning region, is estimated using Gaussian process in BO. This relies on a prior quality map modeled using expert's demonstration of the high-quality probing maneuvers. The ultrasound image quality feedback is provided to BO, which is estimated using a deep convolution neural network model. This model was previously trained on database of images labelled for diagnostic quality by expert radiologists. Experiments on three different urinary bladder phantoms validated that the proposed autonomous ultrasound system can acquire ultrasound images for diagnostic purposes with a probing position and force accuracy of 98.7% and 97.8%, respectively.
USPilot: An Embodied Robotic Assistant Ultrasound System with Large Language Model Enhanced Graph Planner
Department of Biomedical Engineering, City University of Hong Kong（城市大学生物医学工程系）；Department of Mechanical and Automation Engineering, Chinese University of Hong Kong（香港中文大学机械与自动化工程系）；Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港科学与创新研究院人工智能与机器人中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Department of Surgical and Interventional Engineering, King’s College London（伦敦国王学院外科与介入工程系）
Mixed Reality Tele-Ultrasound over 750 km: A Feasibility Study
School of Biomedical Engineering, The University of British Columbia（生物医学工程学院，不列颠哥伦比亚大学）；Department of Electrical and Computer Engineering, The University of British Columbia（电气与计算机工程系，不列颠哥伦比亚大学）；The University of British Columbia（不列颠哥伦比亚大学）；Department of Radiology, The University of British Columbia（放射学系，不列颠哥伦比亚大学）
Intelligent Virtual Sonographer (IVS): Enhancing Physician-Robot-Patient Communication
Chair for Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany（计算机辅助医疗程序与增强现实主任，慕尼黑技术大学，慕尼黑，德国）；Munich Center for Machine Learning, Munich, Germany（慕尼黑机器学习中心，慕尼黑，德国）；Department for Vascular and Endovascular Surgery, Rechts der Isar University Hospital, Technical University of Munich, Munich, Germany（血管和内血管外科部门，Rechts der Isar大学医院，慕尼黑技术大学，慕尼黑，德国）；Clinic for Vascular Surgery, Helios Klinikum München West, Munich, Germany（血管外科诊所，Helios克林克慕尼黑西，慕尼黑，德国）
Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives
Technische Universität München（慕尼黑工业大学）；Technical University of Munich（慕尼黑工业大学）；University of British Columbia（不列颠哥伦比亚大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework
The Chinese University of Hong Kong（香港中文大学）；Shenzhen University（深圳大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Research Institute of the Chinese University of Hong Kong（香港中文大学深圳研究院）
Enhancing Surgical Robots with Embodied Intelligence for Autonomous Ultrasound Scanning
Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
