Single Plane-Wave Imaging using Physics-Based Deep Learning

TL;DR

This paper introduces a physics-based deep learning approach for single-plane wave ultrasound imaging, combining wave physics with neural networks to improve image quality from minimal data.

Contribution

It proposes a novel data-to-image neural network architecture that integrates Fourier migration layers, enhancing image quality in single-plane wave ultrasound imaging.

Findings

High-quality images comparable to using 75 plane waves achieved with fewer waves.

Physics-based layers improve image reconstruction over purely data-driven models.

End-to-end training of the combined model is feasible and effective.

Abstract

In plane-wave imaging, multiple unfocused ultrasound waves are transmitted into a medium of interest from different angles and an image is formed from the recorded reflections. The number of plane waves used leads to a trade-off between frame-rate and image quality, with single-plane-wave (SPW) imaging being the fastest possible modality with the worst image quality. Recently, deep learning methods have been proposed to improve ultrasound imaging. One approach is to use image-to-image networks that work on the formed image and another is to directly learn a mapping from data to an image. Both approaches utilize purely data-driven models and require deep, expressive network architectures, combined with large numbers of training samples to obtain good results. Here, we propose a data-to-image architecture that incorporates a wave-physics-based image formation algorithm in-between deep convolutional neural networks. To achieve this, we implement the Fourier (FK) migration method as network layers and train the whole network end-to-end. We compare our proposed data-to-image network with an image-to-image network in simulated data experiments, mimicking a medical ultrasound application. Experiments show that it is possible to obtain high-quality SPW images, almost similar to an image formed using 75 plane waves over an angular range of $\pm$16$^\circ$. This illustrates the great potential of combining deep neural networks with physics-based image formation algorithms for SPW imaging.