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Robot-assisted ultrasound reconstruction for spine surgery: from bench-top to pre-clinical study.

Authors :
Li, Ruixuan
Davoodi, Ayoob
Cai, Yuyu
Niu, Kenan
Borghesan, Gianni
Cavalcanti, Nicola
Massalimova, Aidana
Carrillo, Fabio
Laux, Christoph J.
Farshad, Mazda
Fürnstahl, Philipp
Poorten, Emmanuel Vander
Source :
International Journal of Computer Assisted Radiology & Surgery; Sep2023, Vol. 18 Issue 9, p1613-1623, 11p
Publication Year :
2023

Abstract

Purpose: Robot-assisted ultrasound (rUS) systems have already been used to provide non-radiative three-dimensional (3D) reconstructions that form the basis for guiding spine surgical procedures. Despite promising studies on this technology, there are few studies that offer insight into the robustness and generality of the approach by verifying performance in various testing scenarios. Therefore, this study aims at providing an assessment of a rUS system, with technical details from experiments starting at the bench-top to the pre-clinical study. Methods: A semi-automatic control strategy was proposed to ensure continuous and smooth robotic scanning. Next, a U-Net-based segmentation approach was developed to automatically process the anatomic features and derive a high-quality 3D US reconstruction. Experiments were conducted on synthetic phantoms and human cadavers to validate the proposed approach. Results: Average deviations of scanning force were found to be 2.84±0.45 N on synthetic phantoms and to be 5.64±1.10 N on human cadavers. The anatomic features could be reliably reconstructed at mean accuracy of 1.28±0.87 mm for the synthetic phantoms and of 1.74±0.89 mm for the human cadavers. Conclusion: The results and experiments demonstrate the feasibility of the proposed system in a pre-clinical setting. This work is complementary to previous work, encouraging further exploration of the potential of this technology in in vivo studies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18616410
Volume :
18
Issue :
9
Database :
Complementary Index
Journal :
International Journal of Computer Assisted Radiology & Surgery
Publication Type :
Academic Journal
Accession number :
171844903
Full Text :
https://doi.org/10.1007/s11548-023-02932-z