Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization

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Title: Learning-based endovascular navigation through the use of non-rigid registration for collaborative robotic catheterization
Authors: Chi, W
Liu, J
Rafii-Tari, H
Riga, C
Bicknell, C
Yang, G-Z
Item Type: Journal Article
Abstract: Purpose Endovascular 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. Methods Statistical 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. Results The success rate for semiautomatic cannulation is 98.1% under dry simulation and 94.4% under continuous flow simulation. The proposed robotic approach achieved smoother catheter paths than manual approach. The mean contact forces have been reduced by 33.3% with the robotic approach, and 70.6% less STDEV forces were observed with the robot. Conclusions This work provides insights into catheter task planning and an improved design of hands-on ergonomic catheter navigation robots.
Issue Date: 1-Jun-2018
Date of Acceptance: 19-Mar-2018
URI: http://hdl.handle.net/10044/1/60741
DOI: https://dx.doi.org/10.1007/s11548-018-1743-5
ISSN: 1861-6410
Publisher: SPRINGER HEIDELBERG
Start Page: 855
End Page: 864
Journal / Book Title: INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
Volume: 13
Issue: 6
Copyright Statement: © 2018 The Author(s). Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/N024877/1
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Radiology, Nuclear Medicine & Medical Imaging
Surgery
Engineering
Robotic catheterization
Robotic surgery
Human-robot collaboration
Imitation learning
Human–robot collaboration
1103 Clinical Sciences
Nuclear Medicine & Medical Imaging
Publication Status: Published
Conference Place: Berlin, GERMANY
Open Access location: https://link.springer.com/article/10.1007/s11548-018-1743-5
Online Publication Date: 2018-04-12
Appears in Collections:Faculty of Engineering
Division of Surgery
Computing
Faculty of Medicine



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