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An Electrical Bioimpedance Scanning System for Subsurface Tissue Detection in Robot Assisted Minimally Invasive Surgery.

Authors :
Cheng, Zhuoqi
Lindberg Schwaner, Kim
Dall'Alba, Diego
Fiorini, Paolo
Savarimuthu, Thiusius Rajeeth
Source :
IEEE Transactions on Biomedical Engineering. Jan2022, Vol. 69 Issue 1, p209-219. 11p.
Publication Year :
2022

Abstract

In Robot Assisted Minimally Invasive Surgery, discriminating critical subsurface structures is essential to make the surgical procedure safer and more efficient. In this paper, a novel robot assisted electrical bio-impedance scanning (RAEIS) system is developed and validated using a series of experiments. The proposed system constructs a tri-polar sensing configuration for tissue homogeneity inspection. Specifically, two robotic forceps are used as electrodes for applying electric current and measuring reciprocal voltages relative to a ground electrode which is placed distal from the measuring site. Compared to existing electrical bioimpedance sensing technology, the proposed system is able to use miniaturized electrodes to measure a site flexibly with enhanced subsurfacial detection capability. This paper presents the concept, the modeling of the sensing method, the hardware design, and the system calibration. Subsequently, a series of experiments are conducted for system evaluation including finite element simulation, saline solution bath experiments and experiments based on ex vivo animal tissues. The experimental results demonstrate that the proposed system can measure the resistivity of the material with high accuracy, and detect a subsurface non-homogeneous object with 100% success rate. The proposed parameters estimation algorithm is able to approximate the resistivity and the depth of the subsurface object effectively with one fast scanning. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189294
Volume :
69
Issue :
1
Database :
Academic Search Index
Journal :
IEEE Transactions on Biomedical Engineering
Publication Type :
Academic Journal
Accession number :
154310648
Full Text :
https://doi.org/10.1109/TBME.2021.3091326