Quantitative ultrasound (QUS) allows estimating the intrinsic tissue properties. Speckle statistics are the QUS parameters that describe the first order statistics of ultrasound (US) envelope data. The parameters of Homodyned K-distribution (HK-distribution) are the speckle statistics that can model the envelope data in diverse scattering conditions. However, they require a large amount of data to be estimated reliably. Consequently, finding out the intrinsic uncertainty of the estimated parameters can help us to have a better understanding of the estimated parameters. In this paper, we propose a Bayesian Neural Network (BNN) to estimate the parameters of HK-distribution and quantify the uncertainty of the estimator.
Cite
@article{arxiv.2211.00175,
title = {Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks},
author = {Ali K. Z. Tehrani and Ivan M. Rosado-Mendez and Hassan Rivaz},
journal= {arXiv preprint arXiv:2211.00175},
year = {2022}
}
Comments
Submitted to IEEE International Symposium on Biomedical Imaging (ISBI) 2023