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Ultrasound imaging (UI) is characterized by the presence of multiplicative speckle noise and various acquisition artefacts. Designing ultrasound (US) similarity measures thus requires a particular attention. In the specific context of motion estimation, incorporating US characteristics does not only benefit traditional methods but also learning-based approaches, which are highly sensitive to the quality of training data. Deriving similarity measures from a maximum likelihood (ML) perspective allows us to take these specificities into account. As opposed to the classical Rayleigh modelling, the proposed similarity measures incorporate more realistic scattering conditions, such as, varying speckle densities and shadowing. Specifically, the deviations from the Rayleigh statistics are modelled using the $t$ -distribution for the complex radio-frequency (RF) signals and the Nakagami-Gamma (NG) compound model for the echo amplitudes. Furthermore, the model parameters are learnt patch-wise, which leads to data-adaptive similarity measures. The proposed criteria are investigated in the context of motion estimation using synthetic, phantom, as well as 2D and 3D in vivo images. The experimental results show an improvement in performance and robustness in comparison to the classical Rayleigh-based approach and state-of-the-art similarity measures. [ABSTRACT FROM AUTHOR]

This paper introduces a robust 2-D cardiac motion estimation method. The problem is formulated as an energy minimization with an optical flow-based data fidelity term and two regularization terms imposing spatial smoothness and the sparsity of the motion field in an appropriate cardiac motion dictionary. Robustness to outliers, such as imaging artefacts and anatomical motion boundaries, is introduced using robust weighting functions for the data fidelity term as well as for the spatial and sparse regularizations. The motion fields and the weights are computed jointly using an iteratively re-weighted minimization strategy. The proposed robust approach is evaluated on synthetic data and realistic simulation sequences with available ground-truth by comparing the performance with state-of-the-art algorithms. Finally, the proposed method is validated using two sequences of in vivo images. The obtained results show the interest of the proposed approach for 2-D cardiac ultrasound imaging. [ABSTRACT FROM AUTHOR]

This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one data set with available ground-truth, including four sequences of highly realistic simulations. The approach is also validated on both healthy and pathological sequences of in vivo data. We evaluate the method in terms of motion estimation accuracy and strain errors and compare the performance with state-of-the-art algorithms. The results show that the proposed method gives competitive results for the considered data. Furthermore, the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors. [ABSTRACT FROM PUBLISHER]