Single Plane-Wave Imaging using Physics-Based Deep Learning

• URL: http://arxiv.org/abs/2109.03661v1
• Date: Wed, 8 Sep 2021 14:06:29 GMT
• Title: Single Plane-Wave Imaging using Physics-Based Deep Learning
• Authors: Georgios Pilikos, Chris L. de Korte, Tristan van Leeuwen, Felix Lucka
• Abstract summary: In plane-wave imaging, multiple unfocused ultrasound waves are transmitted into a medium of interest from different angles. Deep learning methods have been proposed to improve ultrasound imaging. We propose a data-to-image architecture that incorporates a wave-physics-based image formation algorithm in-between deep convolutional neural networks.
• Score: 2.1410799064827226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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