  Quantitative ultrasound (QUS) analyzes the ultrasound backscattered data to
find the properties of scatterers that correlate with the tissue
microstructure. Statistics of the envelope of the backscattered radiofrequency
(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 ultrasound 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 ultrasound. 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 signalto-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 paper, 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. A demo code are available online at
http://code.sonography.ai
