Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches

• Published in: IEEE transactions on ultrasonics, ferroelectrics, and frequency control ferroelectrics, and frequency control, 2024-03, Vol.71 (3), p.354-365
• Main Authors: Tehrani, Ali K. Z., Cloutier, Guy, Tang, An, Rosado-Mendez, Ivan M., Rivaz, Hassan
• Format: Article
• Language: English
• Subjects: Algorithms; Autoencoder; Backscattering; Clustering; Coherent scattering; deep learning (DL); homodyned K-distribution; Hyperplanes; In vivo methods and tests; K-distribution; Kurtosis; Mathematical models; Neural networks; Noise reduction; Optimization; Optimization methods; Parameter estimation; quantitative ultrasound (QUS); Radio frequency; Scattering; Signal to noise ratio; Ultrasonic imaging
• ISSN: 0885-3010; 1525-8955
• DOI: 10.1109/TUFFC.2024.3357438

Quantitative ultrasound (QUS) analyzes the ultrasound (US) backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radio frequency (RF) data can be utilized to estimate several QUS parameters. Different distributions have been proposed to model envelope data. The homodyned K-distribution (HK-distribution) is one of the most comprehensive distributions that can model US backscattered envelope data under diverse scattering conditions (varying scatterer number density and coherent scattering). The scatterer clustering parameter ( \alpha ) and the ratio of the coherent to diffuse scattering power ( {k} ) are the parameters of this distribution that have been used extensively for tissue characterization in diagnostic US. The estimation of these two parameters (which we refer to as HK parameters) is done using optimization algorithms in which statistical features such as the envelope point-wise signal-to-noise ratio (SNR), skewness, kurtosis, and the log-based moments have been utilized as input to such algorithms. The optimization methods minimize the difference between features and their theoretical value from the HK model. We propose that the true value of these statistical features is a hyperplane that covers a small portion of the feature space. In this article, we follow two approaches to reduce the effect of sample features' error. We propose a model projection neural network based on denoising autoencoders to project the noisy features into this space based on this assumption. We also investigate if the noise distribution can be learned by the deep estimators. We compare the proposed methods with conventional methods using simulations, an experimental phantom, and data from an in vivo animal model of hepatic steatosis. The network weight and a demo code are available online at ht.tp://code.sonography.ai.