Imperial College London
Alternate

Mr Colin D Bicknell BM MD FRCS

Faculty of Medicine, Department of Surgery & Cancer

Clinical Reader in Vascular Surgery
 
 
 
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Contact

 

+44 (0)20 3312 6428colin.bicknell

 
 
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Location

 

1020Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Chi:2018:10.1007/s11548-018-1743-5,
author = {Chi, W and Liu, J and Rafii-Tari, H and Riga, C and Bicknell, C and Yang, G-Z},
doi = {10.1007/s11548-018-1743-5},
journal = {INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY},
pages = {855--864},
title = {Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization},
url = {http://dx.doi.org/10.1007/s11548-018-1743-5},
volume = {13},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - PurposeEndovascular intervention is limited by two-dimensional intraoperative imaging and prolonged procedure times in the presence of complex anatomies. Robotic catheter technology could offer benefits such as reduced radiation exposure to the clinician and improved intravascular navigation. Incorporating three-dimensional preoperative imaging into a semiautonomous robotic catheterization platform has the potential for safer and more precise navigation. This paper discusses a semiautonomous robotic catheter platform based on previous work (Rafii-Tari et al., in: MICCAI2013, pp 369–377. https://doi.org/10.1007/978-3-642-40763-5_46, 2013) by proposing a method to address anatomical variability among aortic arches. It incorporates anatomical information in the process of catheter trajectories optimization, hence can adapt to the scale and orientation differences among patient-specific anatomies.MethodsStatistical modeling is implemented to encode the catheter motions of both proximal and distal sites based on cannulation data obtained from a single phantom by an expert operator. Non-rigid registration is applied to obtain a warping function to map catheter tip trajectories into other anatomically similar but shape/scale/orientation different models. The remapped trajectories were used to generate robot trajectories to conduct a collaborative cannulation task under flow simulations. Cross-validations were performed to test the performance of the non-rigid registration. Success rates of the cannulation task executed by the robotic platform were measured. The quality of the catheterization was also assessed using performance metrics for manual and robotic approaches. Furthermore, the contact forces between the instruments and the phantoms were measured and compared for both approaches.ResultsThe success rate for semiautomatic cannulation is 98.1% under dry simulation and 94.4% under continuous flow simulation. The proposed robotic approach achieved smoother cathete
AU  - Chi,W
AU  - Liu,J
AU  - Rafii-Tari,H
AU  - Riga,C
AU  - Bicknell,C
AU  - Yang,G-Z
DO  - 10.1007/s11548-018-1743-5
EP  - 864
PY  - 2018///
SN  - 1861-6410
SP  - 855
TI  - Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization
T2  - INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
UR  - http://dx.doi.org/10.1007/s11548-018-1743-5
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000433496100012&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/60741
VL  - 13
ER  -
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