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Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, Mistry, Umesh, Thorpe, Ashley, Housden, R. James, Chen, Zhong, Schulze, Walther H. W., Rinaldi, C. Aldo, Razavi, Reza and Rhode, Kawal S.
(2013)
Automatic electrode and CT/MR image co-localisation for electrocardiographic imaging.
In:
International Conference on Functional Imaging and Modeling of the Heart.
Lecture Notes in Computer Science
.
Springer, pp. 268-275.
Abstract
Body surface potential mapping (BSPM) can be used to non-invasively measure the electrical activity of the heart using a dense set of thorax electrodes and a CT/MR scan of the thorax to solve the inverse problem of electrophysiology (ECGi). This technique now shows potential clinical value for the assessment and treatment of patients with arrhythmias. Co-localisation of the electrode positions and the CT/MR thorax scan is essential. This manuscript describes a method to perform the co-localisation using multiple biplane X-ray images. The electrodes are automatically detected and paired in the X-ray images. Then the 3D positions of the electrodes are computed and mapped onto the thorax surface derived from CT/MR. The proposed method is based on a multi-scale blob detection algorithm and the generalized Hough transform, which can automatically discriminate the leads used for BSPM from other ECG leads. The pairing method is based on epi-polar constraint matching and line pattern detection which assumes that BSPM electrodes are arranged in strips. The proposed methods are tested on a thorax phantom and two clinical cases. Results show an accuracy of 0.33 ± 0.20mm for detecting electrodes in the X-ray images and a success rate of 95.4%. The automatic pairing method achieves a 91.2% success rate.
| 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: | 03 Jan 2023 15:30 |
| Last Modified: | 10 Dec 2024 01:12 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90375 |
| DOI: | 10.1007/978-3-642-38899-6_32 |
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