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Proceedings Paper

Differentiation of pre-ablation and post-ablation late gadolinium-enhanced cardiac MRI scans of longstanding persistent atrial fibrillation patients
Author(s): Guang Yang; Xiahai Zhuang; Habib Khan; Shouvik Haldar; Eva Nyktari; Lei Li; Xujiong Ye; Greg Slabaugh; Tom Wong; Raad Mohiaddin; Jennifer Keegan; David Firmin
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Paper Abstract

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is an emerging non-invasive technique to image and quantify preablation native and post-ablation atrial scarring. Previous studies have reported that enhanced image intensities of the atrial scarring in the LGE CMRI inversely correlate with the left atrial endocardial voltage invasively obtained by electro-anatomical mapping. However, the reported reproducibility of using LGE CMRI to identify and quantify atrial scarring is variable. This may be due to two reasons: first, delineation of the left atrium (LA) and pulmonary veins (PVs) anatomy generally relies on manual operation that is highly subjective, and this could substantially affect the subsequent atrial scarring segmentation; second, simple intensity based image features may not be good enough to detect subtle changes in atrial scarring. In this study, we hypothesized that texture analysis can provide reliable image features for the LGE CMRI images subject to accurate and objective delineation of the heart anatomy based on a fully-automated whole heart segmentation (WHS) method. We tested the extracted texture features to differentiate between pre-ablation and post-ablation LGE CMRI studies in longstanding persistent atrial fibrillation patients. These patients often have extensive native scarring and differentiation from post-ablation scarring can be difficult. Quantification results showed that our method is capable of solving this classification task, and we can envisage further deployment of this texture analysis based method for other clinical problems using LGE CMRI.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340O (3 March 2017); doi: 10.1117/12.2250910
Show Author Affiliations
Guang Yang, Royal Brompton Hospital (United Kingdom)
National Heart and Lung Institute, Imperial College London (United Kingdom)
Xiahai Zhuang, Shanghai Jiao Tong Univ. (China)
Habib Khan, Royal Brompton Hospital (United Kingdom)
National Heart and Lung Institute, Imperial College London (United Kingdom)
Shouvik Haldar, Royal Brompton Hospital (United Kingdom)
Eva Nyktari, Royal Brompton Hospital (United Kingdom)
Lei Li, Shanghai Jiao Tong Univ. (China)
Xujiong Ye, Univ. of Lincoln (United Kingdom)
Greg Slabaugh, City Univ. London (United Kingdom)
Tom Wong, Royal Brompton Hospital (United Kingdom)
Raad Mohiaddin, Royal Brompton Hospital (United Kingdom)
National Heart and Lung Institute, Imperial College London (United Kingdom)
Jennifer Keegan, Royal Brompton Hospital (United Kingdom)
National Heart and Lung Institute, Imperial College London (United Kingdom)
David Firmin, Royal Brompton Hospital (United Kingdom)
National Heart and Lung Institute, Imperial College London (United Kingdom)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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