Image-based view-angle independent cardiorespiratory motion gating for x-ray-guided interventional electrophysiology procedures

Tools

Panayiotou, Maria, King, Andrew P., Housden, R. James, Ma, YingLiang ORCID: https://orcid.org/0000-0001-5770-5843, Truong, Michael, Cooklin, Michael, O'Neill, Mark, Gill, Jaswinder, Rinaldi, C. Aldo and Rhode, Kawal S. (2015) Image-based view-angle independent cardiorespiratory motion gating for x-ray-guided interventional electrophysiology procedures. In: Statistical Atlases and Computational Models of the Heart - Imaging and Modelling Challenges. Lecture Notes in Computer Science . Springer, 158–167. ISBN 978-3-319-14677-5

Full text not available from this repository. (Request a copy)

Abstract

Cardiorespiratory phase determination has numerous applications during cardiac imaging. We propose a novel view-angle independent prospective cardiorespiratory motion gating technique for X-ray fluoroscopy images that are used to guide cardiac electrophysiology procedures. The method is based on learning coronary sinus catheter motion using principal component analysis and then applying the derived motion model to unseen images taken at arbitrary projections. We validated our technique on 7 sequential biplane sequences in normal and very low dose scenarios and on 5 rotational sequences in normal dose. For the normal dose images we established average systole, end-inspiration and end-expiration gating success rates of 100 %, 97.4 % and 95.2 %, respectively. For very low dose applications, the method was tested on images with added noise. Average gating success rates were 93.4 %, 90 % and 93.4 % even at the low SNR value of 5–√, representing a dose reduction of more than 10 times. This technique can extract clinically useful motion information whilst minimising exposure to ionising radiation.

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: 04 Jan 2023 17:30
Last Modified: 10 Dec 2024 01:13
URI: https://ueaeprints.uea.ac.uk/id/eprint/90384
DOI: 10.1007/978-3-319-14678-2_16

Actions (login required)

View Item View Item