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 tradeoff 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 ±16°. This illustrates the great potential of combining deep neural networks with physics-based image formation algorithms for SPW imaging.
Mathematics and Algorithms for 3D Imaging of Dynamic Processes
2021 IEEE International Ultrasonics Symposium (IUS)
Computational Imaging

Pilikos, G, Korte, C.L, van Leeuwen, T, & Lucka, F. (2021). Single plane-wave imaging using physics-based deep learning. In IEEE International Ultrasonics Symposium. doi:10.1109/IUS52206.2021.9593589