# Single Plane-Wave Imaging using Physics-Based Deep Learning

arXiv: https://arxiv.org/abs/2109.03661 · HTML: https://mlsanity.com/paper/2109.03661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Micro-Batch Training with Batch-Channel Normalization and Weight Standardization](https://mlsanity.com/paper/1903.10520/) (2019)
- [Deep learning in ultrasound imaging](https://mlsanity.com/paper/1907.02994/) (2019)
- [Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound](https://mlsanity.com/paper/1907.10257/) (2019)
- [Fast ultrasonic imaging using end-to-end deep learning](https://mlsanity.com/paper/2009.02194/) (2020)
- [Deep data compression for approximate ultrasonic image formation](https://mlsanity.com/paper/2009.02293/) (2020)

## Built on by (most cited first, 1 of 1)

- [Experimental Validation of Ultrasound Beamforming with End-to-End Deep Learning for Single Plane Wave Imaging](https://mlsanity.com/paper/2404.14188/) (2024)

## Abstract (arXiv)

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 ±16^circ. This illustrates the great potential of combining deep neural networks with physics-based image formation algorithms for SPW imaging.