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I need help in summarizing the paragraph below into 3-4 sentences.

{The framework includes an unconventional beamforming technique and a dedicated motion estimation algorithm. The beamformer aims at introducing phase information in the lateral direction by producing transverse oscillations. The estimator directly exploits the phase information in the two directions by decomposing the image into two 2-D single-orthant analytic signals. An in silico evaluation of the proposed framework is presented on five ultra-realistic simulated echocardiographic sequences, where the proposed motion estimator is contrasted against other two phase-based solutions exploiting the presence of transverse oscillations and against block-matching on standard images.

The beamformer was designed so to add oscillations in the lateral direction. As already known from blood flow imaging and elastography such an acquisition scheme leads to an image model intrinsically better suited for the estimation of lateral displacements. We then presented an algorithm specifically designed to exploit the availability of a phase information in the two directions. This was done by decomposing the ultrasound image into two 2-D single-orthant analytic signals and assuming time conservation of the two associated image phases.

A quantitative evaluation of the proposed setup was performed in silico on five synthetic cardiac ultrasound sequences. The comparison included block-matching on standard images without transverse oscillations and other two phase-based solutions exploiting the presence of oscillations in the lateral direction. The obtained results revealed an higher accuracy in the estimation of the cardiac motion when TO were employed. In particular the proposed estimator were the most accurate among the three phase-based algorithms. This better accuracy reflected into a more robust estimation of the cardiac strain. More specifically the proposed setup was the one allowing in average for the highest correlation with the reference strain values, the smallest bias and the smallest limits of agreement as computed with the Bland Altman analysis.}

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