Convolutional Neural Networks (CNN) have shown promising results for displacement
estimation in UltraSound Elastography (USE). Many modifications have been proposed to …

The recovery of morphologically accurate anatomical images from deformed ones is
challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent …

Tracking the displacement between the pre-and post-deformed radio-frequency (RF) frames
is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to …

Accurate and computationally efficient motion estimation is a critical component of real-time
ultrasound strain elastography (USE). With the advent of deep-learning neural network …

Ultrasound elastography is a non-invasive medical imaging technique that maps
viscoelastic properties to characterize tissues and diseases. Elastography can be divided …

One of the primary sources of suboptimal image quality in ultrasound imaging is phase
aberration. It is caused by spatial changes in sound speed over a heterogeneous medium …

Background Ultrasound strain imaging, which delineates mechanical properties to detect
tissue abnormalities, involves estimating the time delay between two radio‐frequency (RF) …

Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field
where researchers adapt the image analysis capabilities of DL algorithms to biomedical …

Displacement estimation is a critical step of virtually all Ultrasound Elastography (USE)
techniques. Two main features make this task unique compared to the general optical flow …

Objective: Displacement estimation (also known as time-delay estimation (TDE)) is a
challenging step in ultrasound elastography. The main issue of existing TDE techniques is …