We propose a methodology for performing the estimation of a key constitutive parameter in a biomechanical heart model - namely, the tissue contractility - using tagged-MRI data. We adopt a sequential data assimilation strategy, and the image data is assumed to be processed in the form of deforming tag planes, which we employ to obtain a discrepancy between the model and the data by computing distances to these surfaces. We assess our procedure using synthetic measurements produced with a model representing an infarcted heart as observed in an animal experiment, and the estimation results are found to be of superior accuracy compared to assimilation based on segmented endo- and epicardium surfaces. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Imperiale, A., Chabiniok, R., Moireau, P., & Chapelle, D. (2011). Constitutive parameter estimation methodology using tagged-MRI data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6666 LNCS, pp. 409–417). https://doi.org/10.1007/978-3-642-21028-0_52
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