In this section
The MIM Lab develops robotic and mechatronics surgical systems for a variety of procedures.
Twitter/X: @MIMLab_Robotics
Youtube: Mechatronics in Medicine Lab
The Mechatronics in Medicine Laboratory develops robotic and mechatronics surgical systems for a variety of procedures including neuro, cardiovascular, orthopaedic surgeries, and colonoscopies. Examples include bio-inspired catheters that can navigate along complex paths within the brain (such as EDEN2020), soft robots to explore endoluminal anatomies (such as the colon), and virtual reality solutions to support surgeons during knee replacement surgeries.
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Mr Ayhan Aktas
Casual - Student demonstrator - lower rate
Dr Daniel Bautista Salinas
Research Associate
Dr Kaiwen Chen
Research Associate
Mr Zejian Cui
Research Postgraduate
Mr Connor Daly
Research Postgraduate
Emeritus Professor Brian L Davies FREng
Emeritus Professor
Mr Zhaoyang Jacopo Hu
Research Postgraduate
Dr Hisham M Iqbal
Research Associate
Mr Alex Ranne
Research Postgraduate
Professor Ferdinando M Rodriguez y Baena
Co-Director of Hamlyn Centre, Professor of Medical Robotics
Dr Jialei Shi
Research Associate
Mr Spyridon Souipas
Casual - Other work
Ms Emilia Zari
Research Postgraduate
@article{Jianu:2024:10.1109/TMRB.2024.3421256,
author = {Jianu, T and Huang, B and Nhat, Vu M and Abdelaziz, MEMK and Fichera, S and Lee, CY and Berthet-Rayne, P and Rodriguez, y Baena F and Nguyen, A},
doi = {10.1109/TMRB.2024.3421256},
journal = {IEEE Transactions on Medical Robotics and Bionics},
pages = {971--979},
title = {CathSim: An Open-Source Simulator for Endovascular Intervention},
url = {http://dx.doi.org/10.1109/TMRB.2024.3421256},
volume = {6},
year = {2024}
}
TY - JOUR
AB - Autonomous robots in endovascular operations have the potential to navigate circulatory systems safely and reliably while decreasing the susceptibility to human errors. However, there are numerous challenges involved with the process of training such robots, such as long training duration and safety issues arising from the interaction between the catheter and the aorta. Recently, endovascular simulators have been employed for medical training but generally do not conform to autonomous catheterization due to the lack of standardization and RL framework compliance. Furthermore, most current simulators are closed-source, which hinders the collaborative development of safe and reliable autonomous systems through shared learning and community-driven enhancements. In this work, we introduce CathSim, an open-source simulation environment that accelerates the development of machine learning algorithms for autonomous endovascular navigation. We first simulate the high-fidelity catheter and aorta with a state-of-the-art endovascular robot. We then provide the capability of real-time force sensing between the catheter and the aorta in simulation. Furthermore, we validate our simulator by conducting two different catheterization tasks using two popular reinforcement learning algorithms, namely SAC and PPO. The experimental results show that our open-source simulator can mimic the behavior of real-world endovascular robots and facilitate the development of different autonomous catheterization tasks. Our simulator is publicly available at https://github.com/airvlab/cathsim.
AU - Jianu,T
AU - Huang,B
AU - Nhat,Vu M
AU - Abdelaziz,MEMK
AU - Fichera,S
AU - Lee,CY
AU - Berthet-Rayne,P
AU - Rodriguez,y Baena F
AU - Nguyen,A
DO - 10.1109/TMRB.2024.3421256
EP - 979
PY - 2024///
SP - 971
TI - CathSim: An Open-Source Simulator for Endovascular Intervention
T2 - IEEE Transactions on Medical Robotics and Bionics
UR - http://dx.doi.org/10.1109/TMRB.2024.3421256
VL - 6
ER -
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