1 citations · 1 across the 14 of their papers we have counts for
15 papers
Constrained and Regularized Quantitative Ultrasound Parameter Estimation using ADMM
Ali K. Z. Tehrani, Hassan Rivaz, Ivan M. Rosado-Mendez
Regularized estimation of quantitative ultrasound (QUS) parameters, such as attenuation and backscatter coefficients, has gained research interest. Recently, the alternating direct…
Uncertainty Decomposition and Error Margin Detection of Homodyned-K Distribution in Quantitative Ultrasound
Dorsa Ameri, Ali K. Z. Tehrani, Ivan M. Rosado-Mendez +1
Homodyned K-distribution (HK-distribution) parameter estimation in quantitative ultrasound (QUS) has been recently addressed using Bayesian Neural Networks (BNNs). BNNs have been s…
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches
Ali K. Z. Tehrani, Guy Cloutier, An Tang +2
Quantitative ultrasound (QUS) analyzes the ultrasound backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envel…
Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography
Md Ashikuzzaman, Ali K. Z. Tehrani, Hassan Rivaz
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…
A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement
Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hayley Whitson +1
Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure. Among different QUS parameters, scatterer number density has bee…
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…
Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks
Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz
Quantitative ultrasound (QUS) allows estimating the intrinsic tissue properties. Speckle statistics are the QUS parameters that describe the first order statistics of ultrasound (U…
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…
Ultrasound Domain Adaptation Using Frequency Domain Analysis
Mostafa Sharifzadeh, Ali K. Z. Tehrani, Habib Benali +1
A common issue in exploiting simulated ultrasound data for training neural networks is the domain shift problem, where the trained models on synthetic data are not generalizable to…
Ultrasound Scatterer Density Classification Using Convolutional Neural Networks by Exploiting Patch Statistics
Ali K. Z. Tehrani, Mina Amiri, Ivan M. Rosado-Mendez +2
Quantitative ultrasound (QUS) can reveal crucial information on tissue properties such as scatterer density. If the scatterer density per resolution cell is above or below 10, the…
Semi-Supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography
Ali K. Z. Tehrani, Morteza Mirzaei, Hassan Rivaz
Convolutional Neural Networks (CNN) have been found to have great potential in optical flow problems thanks to an abundance of data available for training a deep network. The displ…