Joint Myocardial Motion and Contraction Phase Estimation from Cine MRI Using Variational Data Assimilation

Abstract : 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.
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Communication dans un congrès
Statistical Atlases and Computational Models of the Heart, Sep 2014, Boston, United States. Springer, 8896, pp.187-195, 2015, Lecture Notes in Computer Science. 〈10.1007/978-3-319-14678-2_19〉
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https://hal.archives-ouvertes.fr/hal-01887664
Contributeur : Laurent Sarry <>
Soumis le : jeudi 4 octobre 2018 - 12:22:05
Dernière modification le : samedi 6 octobre 2018 - 01:16:58

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Laurent Sarry, Viateur Tuyisenge, Thomas Corpetti, Innorta-Coupez Elisabeth, Lemlih Ouchchane, et al.. Joint Myocardial Motion and Contraction Phase Estimation from Cine MRI Using Variational Data Assimilation. Statistical Atlases and Computational Models of the Heart, Sep 2014, Boston, United States. Springer, 8896, pp.187-195, 2015, Lecture Notes in Computer Science. 〈10.1007/978-3-319-14678-2_19〉. 〈hal-01887664〉

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