Deep Estimation of Speckle Statistics Parametric Images

TL;DR

This paper introduces a CNN-based method for estimating quantitative ultrasound parametric images directly from envelope data, avoiding patch-based errors and assumptions of tissue homogeneity.

Contribution

It presents a novel deep learning approach that improves accuracy and border delineation in QUS parametric imaging by bypassing traditional patch-based estimation methods.

Findings

Reduces estimation errors in QUS parametric images

Enhances border definition in the images

Outperforms traditional patch-based methods

Abstract

Quantitative Ultrasound (QUS) provides important information about the tissue properties. QUS parametric image can be formed by dividing the envelope data into small overlapping patches and computing different speckle statistics such as parameters of the Nakagami and Homodyned K-distributions (HK-distribution). The calculated QUS parametric images can be erroneous since only a few independent samples are available inside the patches. Another challenge is that the envelope samples inside the patch are assumed to come from the same distribution, an assumption that is often violated given that the tissue is usually not homogenous. In this paper, we propose a method based on Convolutional Neural Networks (CNN) to estimate QUS parametric images without patching. We construct a large dataset sampled from the HK-distribution, having regions with random shapes and QUS parameter values. We then use a well-known network to estimate QUS parameters in a multi-task learning fashion. Our results confirm that the proposed method is able to reduce errors and improve border definition in QUS parametric images.