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Wu, Xianliang, Housden, James, Varma, Niharika, Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, Rueckert, Daniel and Rhode, Kawal
(2013)
Catheter tracking in 3D echocardiographic sequences based on tracking in 2D X-ray sequences for cardiac catheterization interventions.
In:
Proceedings - International Symposium on Biomedical Imaging.
The Institute of Electrical and Electronics Engineers (IEEE), pp. 25-28.
ISBN 978-1-4673-6456-0
Abstract
Although X-ray imaging has played a dominant role in cardiac catheter-based interventions, sometimes 3D soft tissue information, which X-ray images cannot provide, may be required. In contrast, 3D echocardiographic imaging is able to visualise soft tissue. In this paper, we propose a real-time catheter tracking strategy in echocardiographic sequences based on catheter tracking in 2D X-ray images and registration between these two modalities. The catheter tracking in X-ray images can be divided into catheter initialization and tracking. For initialization, an extraction algorithm based on SURF features, patch analysis and Kalman filtering is used to locate the catheter in the first X-ray image. Following this, a tracking algorithm based on patch analysis and Fast-PD optimization is used to track the catheter in the following images. The tracking result of each X-ray frame, as well as the transformation between X-ray and ultrasound images obtained from registration, is used to reduce the search space in the echo volume to only a curved surface. Using a graphical model and shortest path optimization, the position of the catheters in each echo frame can be estimated. Based on 10 pairs of X-ray and US sequences, comprising more than 800 frames, our experimental results show that the tracking system can track catheters with an average error of less than 1mm in X-ray images and less than 2mm in US. Also, our strategy for tracking in X-ray outperforms previous methods using only Fast-PD. The speed in total can reach 1.5 fps.
| 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 |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 05 Jan 2023 12:30 |
| Last Modified: | 10 Dec 2024 01:12 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90422 |
| DOI: | 10.1109/ISBI.2013.6556403 |
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