Off-Grid Ultrasound Imaging by Stochastic Optimization

TL;DR

This paper introduces INFER, an off-grid stochastic optimization method for ultrasound imaging that improves image quality, reduces the number of required transmissions, and enhances robustness compared to traditional inverse and beamforming techniques.

Contribution

INFER jointly optimizes grid locations, reflectivities, and measurement parameters, enabling high-quality imaging with fewer gridpoints and greater robustness to model variations.

Findings

Outperforms traditional beamforming in contrast and resolution.

Requires fewer transmissions for high-quality images.

Robust across different targets and transmit schemes.

Abstract

Ultrasound images formed by delay-and-sum beamforming are plagued by artifacts that only clear up after compounding many transmissions. Some prior works pose imaging as an inverse problem. This approach can yield high image quality with few transmits, but requires a very fine image grid and is not robust to changes in measurement model parameters. We present INverse grid-Free Estimation of Reflectivities (INFER), an off-grid and stochastic algorithm that solves the inverse scattering problem in ultrasound imaging. Our method jointly optimizes for the locations of the gridpoints, their reflectivities, and the measurement model parameters such as the speed of sound. This approach allows us to use significantly fewer gridpoints, while obtaining better contrast and resolution and being more robust to changes in the imaging target and the hardware. The use of stochastic optimization enables solving for multiple transmissions simultaneously without increasing the required memory or computational load per iteration. We show that our method works across different imaging targets and across different transmit schemes and compares favorably against other beamforming and inverse solvers. The source code and the dataset to reproduce the results in this paper are available at www.github.com/vincentvdschaft/off-grid-ultrasound.