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Multimodal Registration and Data Fusion for Cardiac Resynchronization Therapy Optimization

Abstract : Cardiac Resynchronization Therapy (CRT) has been shown to improve cardiovascular function in specific patients suffering from heart failure. This procedure still needs to be optimized to overcome the high rate of implanted patients that do not respond to this therapy. We propose in this work a better characterization of the electro-mechanical (EM) coupling of each region of the left ventricle (LV) that could be useful to precise the best implantation site. A new descriptor is proposed with the extraction of local electro-mechanical delays. Their measurement is based on the fusion of anatomical, functional and electrical data acquired using computed tomography (CT), speckle tracking echocardiography (STE) and electro-anatomical mappings (EAM). We propose a workflow to place multimodal data in the same geometrical referential system and to extract local electro-mechanical descriptors. It implies the fusion of electrical and mechanical data on a 3D+t anatomical model of the left ventricle (LV). It mainly consists in four steps: (1) the modeling of the endocardium using a dynamic surface estimated from CT images; (2) the semi-interactive registration of EAM data and CT images; (3) the automatic registration of STE data on the dynamic model, using a metric based on Fourier descriptors and Dynamic Time Warping (DTW); (4) the temporal alignment between EAM and STE and the estimation of local electro-mechanical delays. The proposed process has been applied to real data corresponding to five patients undergoing CRT. Results show that local electro-mechanical delays provide meaningful information on the local characterization of the LV and may be useful for the optimal pacing site selection in CRT.
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Submitted on : Wednesday, March 19, 2014 - 8:46:14 AM
Last modification on : Tuesday, November 23, 2021 - 2:34:10 PM
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François Tavard, Antoine Simon, Christophe Leclercq, Erwan Donal, Alfredo Hernández, et al.. Multimodal Registration and Data Fusion for Cardiac Resynchronization Therapy Optimization. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2014, 33 (6), pp.1363-1372. ⟨10.1109/TMI.2014.2311694⟩. ⟨hal-00960857⟩



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