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Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, Zhou, DiWei, Ye, Lei, Housden, R. James, Fazili, Ansab and Rhode, Kawal S.
(2022)
A tensor-based catheter and wire detection and tracking framework and its clinical applications.
IEEE Transactions on Biomedical Engineering, 69 (2).
pp. 635-644.
ISSN 0018-9294
Abstract
Objective: Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task. Method: In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed. Results: The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 0.28 mm and 0.68 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm 0.9 mm was achieved. For co-registration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 0.9 mm was achieved. Conclusion: A novel and fully automatic detection framework and its clinical applications are developed. Significance: The proposed framework can be applied to improve the accuracy of image-guidance systems for cardiac catheterization procedures.
| Item Type: | Article |
|---|---|
| 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 01:40 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90429 |
| DOI: | 10.1109/TBME.2021.3102670 |
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