Abstract
Abstract
Purpose
Bioelectric navigation is a navigation modality for minimally invasive endovascular procedures promising non-fluoroscopic navigation. However, the method offers only limited navigation accuracy between anatomical features and expects the tracked catheter to move only in one direction at all times. We propose to extend bioelectric navigation with additional sensing capabilities, allowing for the estimation of the distance traveled by the catheter, thereby improving accuracy between feature locations and allowing to track also under alternating forward- and backward motion.
Methods
We perform experiments in finite element method (FEM) simulations and in a 3D printed phantom. A solution for estimating the traveled distance using a stationary electrode is proposed, together with an approach on how to evaluate the signals obtained with this additional electrode. We investigate the effects of surrounding tissue conductance on this approach. Finally, the approach is refined in order to mitigate the effects of parallel conductance on the navigation accuracy.
Results
The approach allows to estimate the catheter movement direction and the distance traveled. Simulations show absolute errors below 0.89 mm for non-conducting surrounding tissue, but errors up to 60.27 mm when the tissue is electrically conductive. This effect can be mitigated by a more sophisticated modeling (errors up to 33.96 mm). In experiments in a 3D printed phantom, the mean absolute error over 6 catheter paths is 6.3 mm, with standard deviations smaller than or equal to 1.1 mm.
Conclusions
Extending the setup of bioelectric navigation with an additional stationary electrode allows to estimate the distance traveled by the catheter, as well as the movement direction. The effects of parallel conductive tissue could be partially mitigated in simulations, but further research is needed to investigate these effects in real biological tissue, and to bring the introduced errors down to a clinically acceptable level.
Funder
TUM International Graduate School of Science and Engineering (IGSSE) within the ICL-TUM Joint Academy of Doctoral Studies (JADS) program
Bayerisches Staatsministerium für Wissenschaft und Kunst
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Radiology, Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering