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Ma, Ying Liang 
ORCID: https://orcid.org/0000-0001-5770-5843, Shetty, Anoop K., Duckett, Simon, Etyngier, Patrick, Gijsbers, Geert, Bullens, Roland, Schaeffter, Tobias, Razavi, Reza, Rinaldi, Christopher A. and Rhode, Kawal S.
(2012)
An integrated platform for image-guided cardiac resynchronization therapy.
Physics in Medicine and Biology, 57 (10).
ISSN 0031-9155
Abstract
Cardiac resynchronization therapy (CRT) is an effective procedure for patients with heart failure but 30% of patients do not respond. This may be due to sub-optimal placement of the left ventricular (LV) lead. It is hypothesized that the use of cardiac anatomy, myocardial scar distribution and dyssynchrony information, derived from cardiac magnetic resonance imaging (MRI), may improve outcome by guiding the physician for optimal LV lead positioning. Whole heart MR data can be processed to yield detailed anatomical models including the coronary veins. Cine MR data can be used to measure the motion of the LV to determine which regions are late-activating. Finally, delayed Gadolinium enhancement imaging can be used to detect regions of scarring. This paper presents a complete platform for the guidance of CRT using pre-procedural MR data combined with live x-ray fluoroscopy. The platform was used for 21 patients undergoing CRT in a standard catheterization laboratory. The patients underwent cardiac MRI prior to their procedure. For each patient, a MRI-derived cardiac model, showing the LV lead targets, was registered to x-ray fluoroscopy using multiple views of a catheter looped in the right atrium. Registration was maintained throughout the procedure by a combination of C-arm/x-ray table tracking and respiratory motion compensation. Validation of the registration between the three-dimensional (3D) roadmap and the 2D x-ray images was performed using balloon occlusion coronary venograms. A 2D registration error of 1.2 ± 0.7 mm was achieved. In addition, a novel navigation technique was developed, called Cardiac Unfold, where an entire cardiac chamber is unfolded from 3D to 2D along with all relevant anatomical and functional information and coupled to real-time device detection. This allowed more intuitive navigation as the entire 3D scene was displayed simultaneously on a 2D plot. The accuracy of the unfold navigation was assessed off-line using 13 patient data sets by computing the registration error of the LV pacing lead electrodes which was found to be 2.2 ± 0.9 mm. Furthermore, the use of Unfold Navigation was demonstrated in real-time for four clinical cases.
| 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 11:30 |
| Last Modified: | 10 Dec 2024 01:40 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90409 |
| DOI: | 10.1088/0031-9155/57/10/2953 |
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