Real-time catheter extraction from 2D X-ray fluoroscopic and 3D echocardiographic images for cardiac interventions

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Wu, Xianliang, Housden, James, Ma, Yingliang ORCID: https://orcid.org/0000-0001-5770-5843, Rueckert, Daniel and Rhode, Kawal S. (2013) Real-time catheter extraction from 2D X-ray fluoroscopic and 3D echocardiographic images for cardiac interventions. In: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Lecture Notes in Computer Science . Springer, 198–206. ISBN 978-3-642-36960-5

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Abstract

X-ray fluoroscopic images are widely used for image guidance in cardiac electrophysiology (EP) procedures to diagnose or treat cardiac arrhythmias based on catheter ablation. However, the main disadvantage of fluoroscopic imaging is the lack of soft tissue information and harmful radiation. In contrast, ultrasound (US) has the advantages of low-cost, non-radiation, and high contrast in soft tissue. In this paper we propose a framework to extract the catheter from both X-ray and US images in real time for cardiac interventions. The catheter extraction from X-ray images is based on SURF features, local patch analysis and Kalman filtering to acquire a set of sorted key points representing the catheter. At the same time, the transformation between the X-ray and US images can be obtained via 2D/3D rigid registration between a 3D model of the US probe and its projection on X-ray images. By backprojecting the information about the catheter location in the X-ray images to the US images the search space can be drastically reduced. The extraction of the catheter from US is based on 3D SURF feature clusters, graph model building, A* algorithm and B-spline smoothing. Experiments show the overall process can be achieved in 2.72 seconds for one frame and the reprojected error is 1.99 mm on average.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Faculty of Science > Research Groups > Data Science and AI
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Depositing User: LivePure Connector
Date Deposited: 05 Jan 2023 11:30
Last Modified: 10 Dec 2024 01:13
URI: https://ueaeprints.uea.ac.uk/id/eprint/90408
DOI: 10.1007/978-3-642-36961-2_23

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