3D path planning from a single 2D fluoroscopic image for robot assisted
fenestrated endovascular aortic repair
fenestrated endovascular aortic repair
File(s)
Working paper
Author(s)
Zheng, Jian-Qing
Zhou, Xiao-Yun
Riga, Celia
Yang, Guang-Zhong
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.
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.
Date Issued
2018-09-16
Citation
2018
Publisher
arXiv
Copyright Statement
© 2018 The Authors.
Sponsor
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
Identifier
http://arxiv.org/abs/1809.05955v1
Grant Number
RDB04 79560
RD207
EP/N024877/1
RDB02
Subjects
cs.CV
cs.CV
Publication Status
Published