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Mechatronics in Medicine | MiM Lab

The MIM Lab develops robotic and mechatronics surgical systems for a variety of procedures.

Head of Group

B415C Bessemer Building
South Kensington Campus

+44 (0)20 7594 7046

What we do

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.

Why is it important

The integration of mechatronics into medicine addresses critical challenges in modern healthcare by enhancing the precision, safety, and efficiency of surgical procedures. Traditional surgeries often involve significant risks and extended recovery times. By developing robotic systems that offer greater accuracy and control, we aim to minimise these risks and reduce invasiveness. Our research contributes to the advancement of minimally invasive techniques, which are essential for improving patient outcomes and optimising healthcare resources. Furthermore, our work supports the training of the next generation of surgeons, equipping them with cutting-edge tools and methodologies that reflect the evolving landscape of medical technology.

How can it benefit patients

Patients stand to gain significantly from the innovations developed at the Mechatronics in Medicine Laboratory. Our robotic systems are designed to perform surgeries with enhanced precision, leading to fewer complications and faster recovery times. Minimally invasive procedures facilitated by our technologies result in less postoperative pain and reduced scarring, improving the overall patient experience. Additionally, the increased accuracy of our systems can lead to better surgical outcomes, such as more complete tumour removals or more precise joint replacements, thereby improving long-term health prospects. By pushing the boundaries of medical robotics, we strive to make advanced surgical care more accessible and effective for patients worldwide.

Meet the team

Dr Daniel Bautista Salinas
Research Associate

Dr Kaiwen Chen
Research Associate

Dr Zejian Cui
Research Associate

Mr Connor Daly
Research Postgraduate

Emeritus Professor Brian L Davies FREng
Emeritus Professor

Dr Hisham M Iqbal
Honorary Research Associate

Mr Alex Tian Jie Ranne
Casual - Lib. Ass, Clerks & Gen. Admin Assistants

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

Citation

BibTex format

@article{Rodriguez:2013:10.1177/0954411913500948,
author = {Rodriguez, Y Baena F and Hawke, T and Jakopec, M},
doi = {10.1177/0954411913500948},
journal = {Proc Inst Mech Eng H},
pages = {1135--1144},
title = {A bounded iterative closest point method for minimally invasive registration of the femur.},
url = {http://dx.doi.org/10.1177/0954411913500948},
volume = {227},
year = {2013}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This article describes a novel method for image-based, minimally invasive registration of the femur, for application to computer-assisted unicompartmental knee arthroplasty. The method is adapted from the well-known iterative closest point algorithm. By utilising an estimate of the hip centre on both the preoperative model and intraoperative patient anatomy, the proposed 'bounded' iterative closest point algorithm robustly produces accurate varus-valgus and anterior-posterior femoral alignment with minimal distal access requirements. Similar to the original iterative closest point implementation, the bounded iterative closest point algorithm converges monotonically to the closest minimum, and the presented case includes a common method for global minimum identification. The bounded iterative closest point method has shown to have exceptional resistance to noise during feature acquisition through simulations and in vitro plastic bone trials, where its performance is compared to a standard form of the iterative closest point algorithm.
AU  - Rodriguez,Y Baena F
AU  - Hawke,T
AU  - Jakopec,M
DO  - 10.1177/0954411913500948
EP  - 1144
PY  - 2013///
SP  - 1135
TI  - A bounded iterative closest point method for minimally invasive registration of the femur.
T2  - Proc Inst Mech Eng H
UR  - http://dx.doi.org/10.1177/0954411913500948
UR  - http://www.ncbi.nlm.nih.gov/pubmed/23959859
VL  - 227
ER  -

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