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Computational models of the heart at various scales and levels of complexity have been independently developed, parameterised and validated using a wide range of experimental data for over four decades. However, despite remarkable progress, the lack of coordinated efforts to compare and combine these computational models has limited their impact on the numerous open questions in cardiac physiology...
To recover physiologically meaningful cardiac deformation from medical images, realistic physiological models are essential to constrain the recovery process, and a statistical filtering framework is required to couple the models and images according to their respective uncertainties. As realistic cardiac models are usually nonlinear, existing cardiac deformation recovery frameworks either ignore...
Volumetric details of cardiac electrophysiology, such as transmembrane potential dynamics and tissue excitability of the myocardium, are of fundamental importance for understanding normal and pathological cardiac mechanisms, and for aiding the diagnosis and treatment of cardiac arrhythmia. Noninvasive observations, however, are made on body surface as an integration-projection of the volumetric phenomena...
To noninvasively reconstruct transmembrane potential (TMP) dynamics throughout the 3D myocardium using body surface potential recordings, it is necessary to combine prior physiological models and patient's data with regard to their respective uncertainties. To fulfill model-data melding for this large-scale and high-dimensional system, data assimilation with proper computational reduction is needed...
Body surface potential (BSP) has been used as the single data source in inverse electrocardiography (IECG). The lack of volumetric spatial resolutions in BSP, however, hinders the noninvasive mapping of volumetric cardiac transmembrane potentials (TMPs). Tomographic image sequence, which contains temporally sparse but spatially dense cardiac kinematic measures, becomes ideal dynamic complements to...
In patient-specific cardiac information recovery, meaningful a priori models for constraining the sparse and noise-corrupted measurements, and also the optimal criteria for coupling them together, are essential for obtaining more reliable estimates. Although the extensively used biomechanical models give promising results, it cannot account for the active components of the myocytes. In view of this,...
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