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Communication Dans Un Congrès Année : 2019

Robust cardiac motion estimation with dictionary learning and temporal regularization for ultrasound imaging

Résumé

Estimating the cardiac motion from ultrasound (US) images is an ill-posed problem that requires regularization. In a recent study, it was shown that constraining the cardiac motion fields to be patch-wise sparse in a learnt overcomplete motion dictionary is more accurate than local parametric models (affine) or global functions (B-splines, total variation). In this work, we extend this method by incorporating temporal smoothness in a multi-frame optical-flow (OF) strategy. An efficient optimization strategy using the constrained split augmented Lagrangian shrinkage algorithm (C-SALSA) is proposed. The performance is evaluated on a realistic simulated cardiac dataset with available ground-truth. A comparison with the pairwise approach shows the interest of the proposed temporal regularization and multi-frame strategy in terms of accuracy and computational time.
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Dates et versions

hal-02901597 , version 1 (17-07-2020)

Identifiants

Citer

Nora Leïla Ouzir, José Bioucas-Dias, Adrian Basarab, Jean-Yves Tourneret. Robust cardiac motion estimation with dictionary learning and temporal regularization for ultrasound imaging. IEEE International Ultrasonics Symposium (IUS 2019), Oct 2019, Glasgow, United Kingdom. pp.2326-2329, ⟨10.1109/ULTSYM.2019.8925936⟩. ⟨hal-02901597⟩
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