2002
DOI: 10.1109/tmi.2002.801163
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Estimation of 3-D left ventricular deformation from medical images using biomechanical models

Abstract: The quantitative estimation of regional cardiac deformation from three-dimensional (3-D) image sequences has important clinical implications for the assessment of viability in the heart wall. We present here a generic methodology for estimating soft tissue deformation which integrates image-derived information with biomechanical models, and apply it to the problem of cardiac deformation estimation. The method is image modality independent. The images are segmented interactively and then initial correspondence … Show more

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Cited by 187 publications
(118 citation statements)
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“…[8]. We emphasize that this approach is notably different from the objective of cardiac motion tracking per se [37,31,9,44], sometimes complemented by extracting some valuable indices from available data by using some constraining physical model equations [21,30]. The discriminating criterion in data assimilation is that once the estimation has been performed the biophysical model must be able to run independently of any data, while providing accurate and predictive solutions.…”
Section: Introductionmentioning
confidence: 99%
“…[8]. We emphasize that this approach is notably different from the objective of cardiac motion tracking per se [37,31,9,44], sometimes complemented by extracting some valuable indices from available data by using some constraining physical model equations [21,30]. The discriminating criterion in data assimilation is that once the estimation has been performed the biophysical model must be able to run independently of any data, while providing accurate and predictive solutions.…”
Section: Introductionmentioning
confidence: 99%
“…From the solution of the PDEs on the source surface, a series of spatially distributed isocontours representing distinct potentials are determined. For each level set, an isocontour of equivalent potential energy is found on the target surface and the two curves matched according to the symmetric closest point method described by Papademetris et al (2002). (3) Generate boundary conditions.…”
Section: Feasibility Of Automated Boundary Condition Generating Methodsmentioning
confidence: 99%
“…In order to find the sequence of corresponding points over time, the symmetric nearest neighbor correspondences [15] is applied by sampling a set of equallyspaced points along the LV boundary. The construction of a sequence of points is essential to analyze wall motion regionally.…”
Section: The Recursive Bayesian Filteringmentioning
confidence: 99%