Bioengineering, augmented reality, and robotic surgery in vascular surgery: A literature review

Biomedical engineering integrates a variety of applied sciences with life sciences to improve human health and reduce the invasiveness of surgical procedures. Technological advances, achieved through biomedical engineering, have contributed to significant improvements in the field of vascular and endovascular surgery. This paper aims to review the most cutting-edge technologies of the last decade involving the use of augmented reality devices and robotic systems in vascular surgery, highlighting benefits and limitations. Accordingly, two distinct literature surveys were conducted through the PubMed database: the first review provides a comprehensive assessment of augmented reality technologies, including the different techniques available for the visualization of virtual content (11 papers revised); the second review collects studies with bioengineering content that highlight the research trend in robotic vascular surgery, excluding works focused only on the clinical use of commercially available robotic systems (15 papers revised). Technological flow is constant and further advances in imaging techniques and hardware components will inevitably bring new tools for a clinical translation of innovative therapeutic strategies in vascular surgery

End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention

Accurate real-time catheter segmentation is an important pre-requisite for robot-assisted endovascular intervention. Most of the existing learning-based methods for catheter segmentation and tracking are only trained on small-scale datasets or synthetic data due to the difficulties of ground-truth annotation. Furthermore, the temporal continuity in intraoperative imaging sequences is not fully utilised. In this paper, we present FW-Net, an end-to-end and real-time deep learning framework for endovascular intervention. The proposed FW-Net has three modules: a segmentation network with encoder-decoder architecture, a flow network to extract optical flow information, and a novel flow-guided warping function to learn the frame-to-frame temporal continuity. We show that by effectively learning temporal continuity, the network can successfully segment and track the catheters in real-time sequences using only raw ground-truth for training. Detailed validation results confirm that our FW-Net outperforms state-of-the-art techniques while achieving real-time performance.Comment: ICRA 202

A Survey on the Current Status and Future Challenges Towards Objective Skills Assessment in Endovascular Surgery

Minimally-invasive endovascular interventions have evolved rapidly over the past decade, facilitated by breakthroughs in medical imaging and sensing, instrumentation and most recently robotics. Catheter based operations are potentially safer and applicable to a wider patient population due to the reduced comorbidity. As a result endovascular surgery has become the preferred treatment option for conditions previously treated with open surgery and as such the number of patients undergoing endovascular interventions is increasing every year. This fact coupled with a proclivity for reduced working hours, results in a requirement for efficient training and assessment of new surgeons, that deviates from the “see one, do one, teach one” model introduced by William Halsted, so that trainees obtain operational expertise in a shorter period. Developing more objective assessment tools based on quantitative metrics is now a recognised need in interventional training and this manuscript reports the current literature for endovascular skills assessment and the associated emerging technologies. A systematic search was performed on PubMed (MEDLINE), Google Scholar, IEEXplore and known journals using the keywords, “endovascular surgery”, “surgical skills”, “endovascular skills”, “surgical training endovascular” and “catheter skills”. Focusing explicitly on endovascular surgical skills, we group related works into three categories based on the metrics used; structured scales and checklists, simulation-based and motion-based metrics. This review highlights the key findings in each category and also provides suggestions for new research opportunities towards fully objective and automated surgical assessment solutions

Evaluating EPflex MRline Guidewire for Endovascular Interventions Guided by MRI at 3T vs. X-ray Fluoroscopy

This project sought to evaluate the efficacy of using the EPflex MRline guidewire for endovascular treatment guided by real-time Magnetic Resonance Imaging (MRI). MRI theoretically has numerous advantages over x-ray, the current clinical standard imaging modality for endovascular procedures. The most profound advantage of MRI is the ability to acquire physiological functional information, via perfusion and diffusion measures, for better intervention planning before and during the procedure. The EPflex guidewire was selectedbecause of its ability to perform in an MRI and x-ray fluoroscopy environment, allowing for relevant and useful comparisons of the guiding imaging modality. An abdominal aorta phantom was used to assess the ability of experienced and inexperienced operators to successfullynavigate the wire under each imaging modality. It was found that x-ray guidance provided statistically faster and more successful navigation attempts than MRI guidance; however, more clinical tests need to be performed in order to assess the clinical significance of these results.This study represents an important step in the direction of developing safer and more effective imaging systems for guiding endovascular procedures

A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts

This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multi-robot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial Informatics, Key words: modularity, medical device customization, multi-robot system, robot learning, visual servoing, robot sewin

A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts

This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multi-robot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial Informatics, Key words: modularity, medical device customization, multi-robot system, robot learning, visual servoing, robot sewin

MR fluoroscopy in vascular and cardiac interventions (review)

Vascular and cardiac disease remains a leading cause of morbidity and mortality in developed and emerging countries. Vascular and cardiac interventions require extensive fluoroscopic guidance to navigate endovascular catheters. X-ray fluoroscopy is considered the current modality for real time imaging. It provides excellent spatial and temporal resolution, but is limited by exposure of patients and staff to ionizing radiation, poor soft tissue characterization and lack of quantitative physiologic information. MR fluoroscopy has been introduced with substantial progress during the last decade. Clinical and experimental studies performed under MR fluoroscopy have indicated the suitability of this modality for: delivery of ASD closure, aortic valves, and endovascular stents (aortic, carotid, iliac, renal arteries, inferior vena cava). It aids in performing ablation, creation of hepatic shunts and local delivery of therapies. Development of more MR compatible equipment and devices will widen the applications of MR-guided procedures. At post-intervention, MR imaging aids in assessing the efficacy of therapies, success of interventions. It also provides information on vascular flow and cardiac morphology, function, perfusion and viability. MR fluoroscopy has the potential to form the basis for minimally invasive image–guided surgeries that offer improved patient management and cost effectiveness

A Robotic Platform for Endovascular Aneurysm Repair

International audienceAn EndoVascular Aneurysm Repair (EVAR) isa procedure used to fix an aneurysm of the aorta. In thisprocedure, a guide is inserted by the femoral artery. This guidegoes through to the height of the aneurysm and then a catheterfollows the guide. Next, a stent graft is deployed in order torepair the aortic aneurysm. The objectives of our work is todevelop a low-cost robotic system and implement a programthat helps the trajectory planning during an endovascularoperation. More precisely, this program can predict if the aortawill break or not depending on the guide used. Such a roboticplatform could serve as a teaching instrument by creating anenvironment for young surgeons in which they will be able topractice their skills to perform an EVAR. This paper describesthe different components of this platform and provides someexperimental results

Context-aware learning for robot-assisted endovascular catheterization

Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces