Robot Assisted Electrical Impedance Tomography System for Minimally Invasive Surgery

Zhuoqi Cheng, Diego Dall'Alba, Paolo Fiorini, Thiusius Rajeeth Savarimuthu

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

This study presents the development of a robot assisted electrical impedance scanning system able to reconstruct Electrical Impedance Tomography (EIT) during surgical inspection. This system can be directly applied on most existing minimally invasive surgical robots without introducing additional sensor probes to the operating site or modifying the existing surgical tools. By positioning two robotic forceps as electrodes to several positions on the tissue surface and performing electrical measurements, the system is able to obtain electrical information and use them for reconstructing the conductivity distribution using the EIT algorithm. This paper describes the system construction, sensing pattern and reconstruction algorithm in detail. In addition, the developed system is optimized using finite element simulation and evaluated through two realistic experiments. The generated EIT images are able to show the location of the non-homogeneous structure from the surrounding tissue effectively. These results demonstrate the great potential of the proposed system to assist surgeons in detecting subsurface target area of interest.

OriginalsprogEngelsk
Titel2021 20th International Conference on Advanced Robotics (ICAR)
ForlagIEEE
Publikationsdato2021
Sider234-240
ISBN (Elektronisk)9781665436847
DOI
StatusUdgivet - 2021
Begivenhed20th International Conference on Advanced Robotics, ICAR 2021 - Ljubljana, Slovenien
Varighed: 6. dec. 202110. dec. 2021

Konference

Konference20th International Conference on Advanced Robotics, ICAR 2021
Land/OmrådeSlovenien
ByLjubljana
Periode06/12/202110/12/2021

Bibliografisk note

Funding Information:
ACKNOWLEDGMENTS This study has been partially supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 742671 ”ARS”).

Funding Information:
This study has been partially supported by the European Research Council (ERC) under the European Union s Horizon 2020 research and innovation programme (grant agreement No. 742671 "ARS").

Publisher Copyright:
© 2021 IEEE.

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