A Two-Parameter Model for Ultrasonic Tissue Characterization with Harmonic Imaging

TL;DR

This paper introduces a new two-parameter reference phantom model for ultrasonic harmonic imaging that accurately estimates tissue attenuation and backscatter coefficients, aiding in disease detection and monitoring, especially for liver conditions.

Contribution

The paper presents a novel RPM model that simultaneously estimates AC and BSC using spectral differences, with practical implementation and initial clinical feasibility for liver disease assessment.

Findings

Accurately estimates AC and BSC within 5% in simulations.

Successfully differentiates between normal and diseased liver tissue in a clinical study.

Provides a stable and practical method for tissue characterization in harmonic imaging.

Abstract

Over the past few decades, researchers have developed several approaches such as the Reference Phantom Method (RPM) to estimate ultrasound attenuation coefficient (AC) and backscatter coefficient (BSC). AC and BSC can help to discriminate pathology from normal tissue during in-vivo imaging. In this paper, we propose a new RPM model to simultaneously compute AC and BSC for harmonic imaging and a normalized score that combines the two parameters as a measure of disease progression. The model utilizes the spectral difference between two regions of interest, the first, a proximal, close to the probe and second, a distal, away from the probe. We have implemented an algorithm based on the model and shown that it provides accurate and stable estimates to within 5% of AC and BSC for simulated received echo from post-focal depths of a homogeneous liver-like medium. For practical applications with time gain and time frequency compensated in-phase and quadrature (IQ) data from ultrasound scanner, the method has been approximated and generalized to estimate AC and BSC for tissue layer underlying a more attenuative subcutaneous layer. The angular spectrum approach for ultrasound propagation in biological tissue is employed as a virtual Reference Phantom (VRP). The VRP is calibrated with a fixed probe and scanning protocol for application to liver tissue. In a feasibility study with 16 subjects, the method is able to separate 9/11 cases of progressive non-alcoholic fatty liver disease from 5 normal. In particular, it is able to separate 4/5 cases of non-alcoholic steato-hepatitis and early fibrosis (F<=2) from normal tissue. More extensive clinical studies are needed to assess the full capability of this model for screening and monitoring disease progression in liver and other tissues.