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Collaborative robot assisted smart needle placement

Citation Link: https://doi.org/10.15480/882.4079
Publikationstyp
Journal Article
Publikationsdatum
2021-10-09
Sprache
English
Author
Neidhardt, Maximilian 
Gerlach, Stefan orcid-logo
Laves, Max-Heinrich 
Latus, Sarah orcid-logo
Stapper, Carolin 
Gromniak, Martin 
Schlaefer, Alexander 
Institut
Medizintechnische und Intelligente Systeme E-1 
DOI
10.15480/882.4079
TORE-URI
http://hdl.handle.net/11420/11400
Lizenz
https://creativecommons.org/licenses/by-nc-nd/4.0/
Enthalten in
Current directions in biomedical engineering 
Volume
7
Issue
2
Start Page
472
End Page
475
Citation
Current Directions in Biomedical Engineering 7 (2): 472-475 (2021-10-09)
Publisher DOI
10.1515/cdbme-2021-2120
Scopus ID
2-s2.0-85121781092
Publisher
de Gruyter
Needles are key tools to realize minimally invasive interventions. Physicians commonly rely on subjectively perceived insertion forces at the distal end of the needle when advancing the needle tip to the desired target. However, detecting tissue transitions at the distal end of the needle is difficult since the sensed forces are dominated by shaft forces. Disentangling insertion forces has the potential to substantially improve needle placement accuracy.We propose a collaborative system for robotic needle insertion, relaying haptic information sensed directly at the needle tip to the physician by haptic feedback through a light weight robot. We integrate optical fibers into medical needles and use optical coherence tomography to image a moving surface at the tip of the needle. Using a convolutional neural network, we estimate forces acting on the needle tip from the optical coherence tomography data. We feed back forces estimated at the needle tip for real time haptic feedback and robot control. When inserting the needle at constant velocity, the force change estimated at the tip when penetrating tissue layers is up to 94% between deep tissue layers compared to the force change at the needle handle of 2.36 %. Collaborative needle insertion results in more sensible force change at tissue transitions with haptic feedback from the tip (49.79 ± 25.51)% compared to the conventional shaft feedback (15.17 ± 15.92) %. Tissue transitions are more prominent when utilizing forces estimated at the needle tip compared to the forces at the needle shaft, indicating that a more informed advancement of the needle is possible with our system.
Schlagworte
biopsy
haptic feedback
machine learning
robotics
DDC Class
004: Informatik
570: Biowissenschaften, Biologie
600: Technik
610: Medizin
620: Ingenieurwissenschaften
Projekt(e)
Mechatronisch geführte Mikronavigation von Nadeln in Weichgewebe 
More Funding Information
The robot used in this study was provided by KUKA as part of the KUKA Innovation Award 2020.
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