Search Results for author:
Found 8 papers, 0 papers with code
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches
Statistics of the envelope of the backscattered radiofrequency (RF) data can be utilized to estimate several QUS parameters.
A deep learning approach for patchless estimation of ultrasound quantitative parametric image with uncertainty measurement
Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure.
Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks
Speckle statistics are the QUS parameters that describe the first order statistics of ultrasound (US) envelope data.
Physics-Inspired Regularized Pulse-Echo Quantitative Ultrasound: Efficient Optimization with ADMM
Pulse-echo Quantitative ultrasound (PEQUS), which estimates the quantitative properties of tissue microstructure, entails estimating the average attenuation and the backscatter coefficient (BSC).
Deep Estimation of Speckle Statistics Parametric Images
Quantitative Ultrasound (QUS) provides important information about the tissue properties.
Robust Scatterer Number Density Segmentation of Ultrasound Images
In conventional methods, the envelope data is divided into small overlapping windows (a strategy here we refer to as patching), and statistical parameters such as SNR and skewness are employed to classify each patch of envelope data.
Estimation of the Scatterer Size Distributions in Quantitative Ultrasound Using Constrained Optimization
In the first technique, we cast scatterer size distribution as an optimization problem, and efficiently solve it using a linear system of equations.
Ultrasound Scatterer Density Classification Using Convolutional Neural Networks by Exploiting Patch Statistics
The results also show that the proposed network is able to work with different imaging parameters with no need for a reference phantom.
