Embolization, stroke, ischaemic lesion, and perforation remain significant concerns in endovascular interventions. Intravascular sensing of tool interaction with the arteries is advantageous to minimize such complications and enhance navigation safety. Intraluminal information is currently limited due to the lack of intravascular contact sensing technologies. We present monitoring of the intraluminal tool interaction with the arterial wall using an image-based estimation approach within vascular robotic navigation. The proposed image-based method employs continuous finite element simulation of the tool using imaging data to estimate multi-point forces along tool-vessel wall interaction. We implemented imaging algorithms to detect and track contacts, and compute pose measurements. The model is constructed based on the nonlinear beam element and flexural rigidity profile over the tool length. During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building a contour map of force on the arterial wall and estimating tool structural stress. Results suggest that high risk intraluminal forces may happen even with low insertion force. The presented online monitoring system delivers insight into the intraluminal behavior of endovascular tools and is well suited for intraoperative visual guidance for the clinician, robotic control of vascular procedures and research on interventional device design.
Autonomous Navigation for Robot-assisted Intraluminal and Endovascular Procedures: A Systematic Review
University of Verona（维罗纳大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Politecnico di Milano（米兰理工大学）；Delft University of Technology（代尔夫特理工大学）；Scuola Superiore Sant’Anna（圣安娜高等研究院）；KU Leuven（鲁汶大学）
Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing
Politecnico di Milano（米兰理工大学）；CNRS-Université de Strasbourg（法国国家科学研究中心-斯特拉斯堡大学）；Delft University of Technology（代尔夫特理工大学）；Scuola Superiore Sant’Anna（圣安娜高等学校）；European Institute of Oncology (IRCCS)（欧洲肿瘤研究所（IRCCS））
Reinforcement Learning for Follow-the-Leader Robotic Endoscopic Navigation via Synthetic Data
School of Computer Science and Engineering, The University of New South Wales（计算机科学与工程学院，新南威尔士大学）；School of Mechanical and Manufacturing Engineering, The University of New South Wales（机械与制造工程学院，新南威尔士大学）
Sample-Efficient Learning with Online Expert Correction for Autonomous Catheter Steering in Endovascular Bifurcation Navigation
Tongji University（同济大学）；College of Electronics and Information Engineering（电子信息工程学院）；Shanghai Institute of Intelligent Science and Technology（上海智能科学与技术研究院）；The Chinese University of Hong Kong（香港中文大学）；School of Mechanical and Electric Engineering（机械电子工程学院）；Soochow University（苏州大学）；Shanghai Operation Robot Co., Ltd.（上海操作机器人有限公司）；State Key Laboratory of Cardiovascular Diseases（心血管疾病国家重点实验室）
Vision-Language Procedural Reasoning for Context-Aware Reward Modeling of Robotic Endovascular Guidewire Navigation
Department of Control Science and Engineering, College of Electronic and Information Engineering, and Shanghai Institute of Intelligent Science and Technology, Tongji University（同济大学电子与信息工程学院控制科学与工程系，以及上海智能科学与技术研究院）；Department of Electronic Engineering, Faculty of Engineering, The Chinese University of Hong Kong（香港中文大学工程学院电子工程系）；Shanghai Operation Robot Co., Ltd.（上海微创医疗机器人（集团）股份有限公司）
World Model for AI Autonomous Navigation in Mechanical Thrombectomy
School of Biomedical Engineering & Imaging Sciences（生物医学工程与成像科学学院）；Kings College London（伦敦国王学院）；Nuffield Department of Primary Care Health Sciences（初级卫生保健科学纽菲尔德部门）；University of Oxford（牛津大学）
Ultrasound-Guided Robotic Blood Drawing and In Vivo Studies on Submillimetre Vessels of Rats
Tongji University（同济大学）；Chengdu Aixam Medical Technology Co., Ltd.（成都 Aixam 医疗科技有限公司）；Katholieke Universiteit Leuven（鲁汶大学）；Scuola Superiore Sant’Anna（圣安娜高等学校）；Shanghai East Hospital, School of Medicine, Tongji University（同济大学医学院附属上海东方医院）；State Key Laboratory of Cardiovascular Diseases（心血管疾病国家重点实验室）
Toward Safe Autonomous Robotic Endovascular Interventions using World Models
Surgical & Interventional Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London（外科与介入工程系，生物医学工程与成像科学学院，伦敦国王学院）；Department of Radiology, National Taiwan University Hospital（放射科，台湾大学医院）；Department of Neuroradiology, King’s College Hospital（神经放射科，伦敦国王医院）
Toward Autonomous Soft Robotic Endovascular Navigation via Imitation Learning
Johns Hopkins University（约翰霍普金斯大学）；Stanford University（斯坦福大学）；McGill University（麦吉尔大学）；University of Maryland（马里兰大学）；Swiss Federal Institute of Technology in Lausanne (EPFL)（日内瓦联邦理工学院（EPFL））；ETH Zurich（苏黎世联邦理工学院）
DINO-CVA: A Multimodal Goal-Conditioned Vision-to-Action Model for Autonomous Catheter Navigation
Gina Cody School of Engineering and Computer Science, Concordia University（甘娜·柯迪工程与计算机科学学院，康科迪亚大学）；The Wilfred and Joyce Posluns Centre for Image Guided Innovation & Therapeutic Intervention (PCIGITI) at the Hospital for Sick Children (SickKids)（威廉与乔伊斯·波斯卢斯影像引导创新与治疗干预中心（PCIGITI）（SickKids医院））；Electrical and Computer Engineering Department, Kettering University（电气与计算机工程系，凯特林大学）
Autonomous Guidewire Navigation for Robot-assisted Endovascular Interventions: A Knowledge-Driven Visual Guidance Approach
Wandong Medical Technology Co., Ltd（万东医疗科技有限公司）；Beijing Police College（北京警察学院）；School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications（北京邮电大学智能工程与自动化学院）；Department of Vascular Surgery, Shanghai Changhai Hospital, Naval Medical University（海军军医大学上海长海医院血管外科）；Beijing Institute of Satellite Information Engineering（北京卫星信息工程研究所）
Vision-Based Reasoning with Topology-Encoded Graphs for Anatomical Path Disambiguation in Robot-Assisted Endovascular Navigation
Department of Control Science and Engineering, College of Electronics and Information Engineering, and Shanghai Institute of Intelligent Science and Technology, Tongji University（控制科学与工程系，电子与信息工程学院和智能科学与技术上海研究院，同济大学）；Department of Electronic Engineering, Faculty of Engineering, The Chinese University of Hong Kong（电子工程系，工程学院，香港中文大学）；Shanghai Operation Robot Co., Ltd.（上海操作机器人有限公司）；Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine（心内科，复旦大学中山医院，上海心血管疾病研究所，介入医学临床研究中心）；State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University（心血管疾病国家重点实验室和医学创新中心，上海东医院，同济大学医学院）
Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions
Department of Control Science and Engineering, College of Electronics and Information Engineering, and Shanghai Institute of Intelligent Science and Technology, Tongji University（控制科学与工程系，电子信息工程学院，上海智能科学技术研究院，同济大学）；Department of Electronic Engineering, Faculty of Engineering, The Chinese University of Hong Kong（电子工程系，工程学院，香港中文大学）；Robotics and Microsystems Center, School of Mechanical and Electrical Engineering, Soochow University（机器人与微系统中心，机械与电气工程学院，苏州大学）；Siemens Healthineers Advanced Therapies (AT), Forchheim, Bavaria（西门子医疗先进治疗（AT）, Forchheim, 巴伐利亚）；HUB of Intelligent Neuro-Engineering (HUBIN), CREATe, Division of Surgery & Interventional Science, University College London（智能神经工程中心（HUBIN），CREATE，外科与介入科学系，伦敦大学学院）；School of Biomedical Engineering & Imaging Sciences, King’s College London（生物医学工程与成像科学学院，伦敦国王学院）
