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  4. Collaborative Robotic Biopsy with Trajectory Guidance and Needle Tip Force Feedback
 
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Collaborative Robotic Biopsy with Trajectory Guidance and Needle Tip Force Feedback

Citation Link: https://doi.org/10.15480/882.13948
Publikationstyp
Conference Paper
Date Issued
2023-05
Sprache
English
Author(s)
Mieling, Till Robin  
Medizintechnische und Intelligente Systeme E-1  
Neidhardt, Maximilian  
Medizintechnische und Intelligente Systeme E-1  
Latus, Sarah  orcid-logo
Medizintechnische und Intelligente Systeme E-1  
Stapper, Carolin  
Medizintechnische und Intelligente Systeme E-1  
Gerlach, Stefan  orcid-logo
Medizintechnische und Intelligente Systeme E-1  
Kniep, Inga  
Heinemann, Axel  
Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Hamburg
Ondruschka, Benjamin  
Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Hamburg
Schlaefer, Alexander  
Medizintechnische und Intelligente Systeme E-1  
TORE-DOI
10.15480/882.13948
TORE-URI
https://hdl.handle.net/11420/43248
Volume
2023
Start Page
6893
End Page
6900
Citation
IEEE International Conference on Robotics and Automation (ICRA 2023) 2023: 6893-6900
Contribution to Conference
IEEE International Conference on Robotics and Automation, ICRA 2023  
Publisher DOI
10.1109/ICRA48891.2023.10161377
Scopus ID
2-s2.0-85168713122
ArXiv ID
2306.07129
Publisher
IEEE
ISBN
979-8-3503-2365-8
979-8-3503-2366-5
The diagnostic value of biopsies is highly dependent on the placement of needles. Robotic trajectory guidance has been shown to improve needle positioning, but feedback for real-time navigation is limited. Haptic display of needle tip forces can provide rich feedback for needle navigation by enabling localization of tissue structures along the insertion path. We present a collaborative robotic biopsy system that combines trajectory guidance with kinesthetic feedback to assist the physician in needle placement. The robot aligns the needle while the insertion is performed in collaboration with a medical expert who controls the needle position on site. We present a needle design that senses forces at the needle tip based on optical coherence tomography and machine learning for real-time data processing. Our robotic setup allows operators to sense deep tissue interfaces independent of frictional forces to improve needle placement relative to a desired target structure. We first evaluate needle tip force sensing in ex-vivo tissue in a phantom study. We characterize the tip forces during insertions with constant velocity and demonstrate the ability to detect tissue interfaces in a collaborative user study. Participants are able to detect 91 percent of ex-vivo tissue interfaces based on needle tip force feedback alone. Finally, we demonstrate that even smaller, deep target structures can be accurately sampled by performing post-mortem in situ biopsies of the pancreas.
Subjects
MLE@TUHH
DDC Class
600: Technology
Lizenz
https://creativecommons.org/licenses/by/4.0/
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2306.07129v2.pdf

Type

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