Tools
Tools
Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, Duckett, Simon, Chinchapatnam, Phani, Shetty, Anoop, Rinaldi, C. Aldo, Schaeffter, Tobias and Rhode, Kawal S.
(2010)
Image and physiological data fusion for guidance and modelling of cardiac resynchronization therapy procedures.
In:
Statistical Atlases and Computational Models of the Heart.
Lecture Notes in Computer Science
.
Springer, 105–113.
ISBN 978-3-642-15834-6
Abstract
Cardiac resynchronization therapy (CRT) can be an effective procedure for patients with heart failure but 30% of patients do not respond. This may be partially caused by the sub-optimal placement of the left ventricular (LV) lead. Detailed cardiac anatomy and dyssynchrony information could improve optimal LV lead placement. As a pre-interventional imaging modality, cardiac magnetic resonance (MR) imaging has the potential to provide all the relevant information. Whole heart MR image 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, late Gadolinium enhancement imaging can be used to detect regions of scarring. This paper presents a complete software solution for the guidance of CRT using pre-procedural MR data combined with live X-ray fluoroscopy. The platform was evaluated using 7 live CRT cases. For each patient, a detailed cardiac model was generated and registered to the X-ray fluoroscopy using multiple views of a catheter looped in the right atrium. There was complete freedom of movement of the X-ray system and respiratory motion compensation was achieved by tracking the diaphragm. The registration was validated using balloon occlusion coronary venograms. The mean 2D target registration error for 7 patients was 1.3 ± 0.68 mm. All patients had a successful left lead implant.
| 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 10:30 |
| Last Modified: | 10 Dec 2024 01:13 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/90402 |
| DOI: | 10.1007/978-3-642-15835-3_11 |
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