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Tuyisenge V.,University of Auvergne | Sarry L.,University of Auvergne | Corpetti T.,University of Rennes 2 - Upper Brittany | Innorta-Coupez E.,Pole de Radiologie et d Imagerie Medicale | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

We present a cardiac motion estimation method with variational data assimilation that combines image observations and a dynamic evolution model. The novelty of the model is that it embeds new parameters modeling heart contraction and relaxation. It was applied to a synthetic dataset with known ground truth motion and to 10 cine-MRI sequences of patients with normal or dyskinetic myocardial zones. It was compared to the inTag tagging tracking software for computing the radial motion component, and to the diagnosis for dyskinesia. We found that the new dynamic model performed better than the standard transport model, and the contraction parameters are promising features for diagnosing dyskinesia. © Springer International Publishing Switzerland 2015. Source

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