3D path planning from a single 2D fluoroscopic image for robot assisted fenestrated endovascular aortic repair

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Title: 3D path planning from a single 2D fluoroscopic image for robot assisted fenestrated endovascular aortic repair
Authors: Zheng, J-Q
Zhou, X-Y
Riga, C
Yang, G-Z
Item Type: Working Paper
Abstract: The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for need of 3D alignments between inserted devices and aortic branches. The navigation commonly via 2D fluoroscopic images, lacks anatomical information, resulting in longer operation hours and radiation exposure. In this paper, a framework for real-time 3D robotic path planning from a single 2D fluoroscopic image of Abdominal Aortic Aneurysm (AAA) is introduced. A graph matching method is proposed to establish the correspondence between the 3D preoperative and 2D intra-operative AAA skeletons, and then the two skeletons are registered by skeleton deformation and regularization in respect to skeleton length and smoothness. Furthermore, deep learning was used to segment 3D pre-operative AAA from Computed Tomography (CT) scans to facilitate the framework automation. Simulation, phantom and patient AAA data sets have been used to validate the proposed framework. 3D distance error of 2mm was achieved in the phantom setup. Performance advantages were also achieved in terms of accuracy, robustness and time-efficiency. All the code will be open source.
Issue Date: 16-Sep-2018
URI: http://hdl.handle.net/10044/1/72666
Publisher: arXiv
Copyright Statement: © 2018 The Authors.
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Engineering & Physical Science Research Council (EPSRC)
NIHR Imperial BRC Cardiovascular Theme
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: RDB04 79560
RD207
EP/N024877/1
RDB02
Keywords: cs.CV
cs.CV
cs.CV
cs.CV
Publication Status: Published
Appears in Collections:Division of Surgery



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