1 citations · 1 across the 13 of their papers we have counts for
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Lateral Strain Imaging using Self-supervised and Physically Inspired Constraints in Unsupervised Regularized Elastography
Ali K. Z. Tehrani, Md Ashikuzzaman, Hassan Rivaz
Convolutional Neural Networks (CNN) have shown promising results for displacement estimation in UltraSound Elastography (USE). Many modifications have been proposed to improve the…
Infusing known operators in convolutional neural networks for lateral strain imaging in ultrasound elastography
Ali K. Z. Tehrani, Hassan Rivaz
Convolutional Neural Networks (CNN) have been employed for displacement estimation in ultrasound elastography (USE). High-quality axial strains (derivative of the axial displacemen…
Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography
Ali K. Z. Tehrani, Hassan Rivaz
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 p…
Deep Estimation of Speckle Statistics Parametric Images
Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz
Quantitative Ultrasound (QUS) provides important information about the tissue properties. QUS parametric image can be formed by dividing the envelope data into small overlapping pa…
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation
Ali K. Z. Tehrani, Mostafa Sharifzadeh, Emad Boctor +1
The performance of ultrasound elastography (USE) heavily depends on the accuracy of displacement estimation. Recently, Convolutional Neural Networks (CNN) have shown promising perf…
Robust Scatterer Number Density Segmentation of Ultrasound Images
Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz
Quantitative UltraSound (QUS) aims to reveal information about the tissue microstructure using backscattered echo signals from clinical scanners. Among different QUS parameters, sc…