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Mechatronics in Medicine

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.

Meet the team

Mr Ayhan Aktas
Casual - Student demonstrator - lower rate

Dr Daniel Bautista Salinas
Research Associate

Dr Kaiwen Chen
Research Associate

Mr Zejian Cui
Research Assistant

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

Citation

BibTex format

@article{Virdyawan:2019:10.1109/JSEN.2019.2934013,
author = {Virdyawan, V and Rodriguez, y Baena F},
doi = {10.1109/JSEN.2019.2934013},
journal = {IEEE Sensors Journal},
pages = {11367--11376},
title = {A long short-term memory network for vessel reconstruction based on laser doppler flowmetry via a steerable needle},
url = {http://dx.doi.org/10.1109/JSEN.2019.2934013},
volume = {19},
year = {2019}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Hemorrhage is one risk of percutaneous intervention in the brain that can be life-threatening. Steerable needles can avoid blood vessels thanks to their ability to follow curvilinear paths, although knowledge of vessel pose is required. To achieve this, we present the deployment of laser Doppler flowmetry (LDF) sensors as an in-situ vessel detection method for steerable needles. Since the perfusion value from an LDF system does not provide positional information directly, we propose the use of a machine learning technique based on a Long Short-term Memory (LSTM) network to perform vessel reconstruction online. Firstly, the LSTM is used to predict the diameter and position of an approaching vessel based on successive measurements of a single LDF probe. Secondly, a "no-go" area is predicted based on the measurement from four LDF probes embedded within a steerable needle, which accounts for the full vessel pose. The network was trained using simulation data and tested on experimental data, with 75 % diameter prediction accuracy and 0.27 mm positional Root Mean Square (RMS) Error for the single probe network, and 77 % vessel volume overlap for the 4-probe setup.
AU  - Virdyawan,V
AU  - Rodriguez,y Baena F
DO  - 10.1109/JSEN.2019.2934013
EP  - 11376
PY  - 2019///
SN  - 1530-437X
SP  - 11367
TI  - A long short-term memory network for vessel reconstruction based on laser doppler flowmetry via a steerable needle
T2  - IEEE Sensors Journal
UR  - http://dx.doi.org/10.1109/JSEN.2019.2934013
UR  - http://hdl.handle.net/10044/1/72319
VL  - 19
ER  -

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