# Learning beamforming in ultrasound imaging

arXiv: https://arxiv.org/abs/1812.08043 · HTML: https://mlsanity.com/paper/1812.08043/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [Joint optimization of system design and reconstruction in MIMO radar imaging](https://mlsanity.com/paper/2110.03218/) (2021)
- [Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation](https://mlsanity.com/paper/2202.05204/) (2022)

## Abstract (arXiv)

Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation. In this paper, we demonstrate that replacing the traditional ultrasound processing pipeline with a data-driven, learnable counterpart leads to significant improvement in image quality. Moreover, we demonstrate that greater improvement can be achieved through a learning-based design of the transmitted beam patterns simultaneously with learning an image reconstruction pipeline. We evaluate our method on an in-vivo first-harmonic cardiac ultrasound dataset acquired from volunteers and demonstrate the significance of the learned pipeline and transmit beam patterns on the image quality when compared to standard transmit and receive beamformers used in high frame-rate US imaging. We believe that the presented methodology provides a fundamentally different perspective on the classical problem of ultrasound beam pattern design.