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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/26316

Title: 3D Magnetic Resonance Image-based Cardiac Computer Models of Cardic Electrophysiology
Authors: Pop, Mihaela Paula
Advisor: Wright, Graham
Department: Medical Biophysics
Keywords: cardiac modelling MRI
Issue Date: 22-Feb-2011
Abstract: There is a clear need for improved methods (e.g. computer modelling, imaging) to characterize the substrate of abnormal rhythms like ventricular tachycardia (VT) developed by patients who have suffered a heart attack. Progress leading to improved disease management and treatment planning (based on predictive models) as well as outcomes assessment will have immediate impact on the quality of life in this large patient population. Prior to integration into clinical applications, the predictive models have to be properly validated using experimental techniques selected to reflect the electrophysiological phenomena at spatio-temporal scales similar to those considered in simulations. This thesis advanced us toward this goal by addressing the challenge of building more accurate models of electrophysiology for individual hearts. A novel construction of a realistic 3D cardiac model from Magnetic Resonance Images (MRI), with a long-term aim to predict propagation of the electrical impulse in normal and pathologic large hearts (translatable to human hearts), and associated inducibility of VT is described. To parameterize the model, an original evaluation method of electrophysiological (EP) characteristics of the heart tissue was used. The method combined state-of-the-art experimental physiology tools like optical fluorescence imaging using voltage-sensitive dyes and a CARTO electro-anatomical system, with a cardiac computer model generated from high resolution MR scans of explanted normal and pathologic porcine hearts. Several input model parameters (e.g., conductivity, anisotropy, restitution) were successfully adjusted using the ex-vivo measurements of action potential to yield close correspondence between model output and experiments. Moreover, a simple, fast, and macroscopic mathematical model was used with computation times less than 1h, attractive for clinical EP applications.
URI: http://hdl.handle.net/1807/26316
Appears in Collections:Doctoral

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