Nonlinear Waveform Inversion for Quantitative Ultrasound

TL;DR

This paper introduces a nonlinear waveform inversion method for quantitative ultrasound that models nonlinear acoustics to improve reconstruction of tissue properties, outperforming linear models and enabling clinical application.

Contribution

It develops a nonlinear acoustic model using neural networks for ultrasound inversion, reconstructing multiple tissue parameters simultaneously, advancing beyond linear models like FWI.

Findings

Nonlinear modeling improves reconstruction accuracy.

Neglecting nonlinear effects degrades image quality.

The method outperforms traditional linear approaches.

Abstract

Due to its non-invasive and non-radiating nature, along with its low cost, ultrasound (US) imaging is widely used in medical applications. Typical B-mode US images have limited resolution and contrast and weak physical interpretation. Inverse US methods were developed to reconstruct the media's speed-of-sound (SoS) based on a linear acoustic model. However, the wave propagation in medical US is governed by nonlinear acoustics, which introduces more complex behaviors neglected in the linear model. In this work we propose a nonlinear waveform inversion (NWI) approach for quantitative US, that considers a nonlinear acoustics model to simultaneously reconstruct multiple material properties, including the medium's SoS, density, attenuation, and nonlinearity parameter. We thus broaden current inverse US approaches, such as the full waveform inversion (FWI) algorithm, by considering nonlinear media, and additional physical parameters. We represent the nonlinear acoustic model by means of a recurrent neural network, which enables us to apply advanced optimization algorithms borrowed from the deep learning toolbox and achieve more efficient reconstructions compared to the FWI method. We evaluate the performance of our approach on in-silico data and show that neglecting nonlinear effects may result in substantial degradation in the reconstruction, paving the way of NWI into clinical applications.