# Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning

arXiv: https://arxiv.org/abs/2202.08262 · HTML: https://mlsanity.com/paper/2202.08262/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound](https://mlsanity.com/paper/1907.10257/) (2019)
- [Deep Learning-based Universal Beamformer for Ultrasound Imaging](https://mlsanity.com/paper/1904.02843/) (2019)
- [Ultrasound Aberration Correction based on Local Speed-of-Sound Map Estimation](https://mlsanity.com/paper/1909.10254/) (2019)
- [The SVD Beamformer: Physical Principles and Application to Ultrafast Adaptive Ultrasound](https://mlsanity.com/paper/1910.05795/) (2019)

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

- [Phase Aberration Correction without Reference Data: An Adaptive Mixed Loss Deep Learning Approach](https://mlsanity.com/paper/2303.05747/) (2023)

## Abstract (arXiv)

Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature. However, its lesion detectability is often limited in many applications due to the phase aberration artefact caused by variations in the speed of sound (SoS) within body parts. To address this, here we propose a novel self-supervised 3D CNN that enables phase aberration robust plane-wave imaging. Instead of aiming at estimating the SoS distribution as in conventional methods, our approach is unique in that the network is trained in a self-supervised manner to robustly generate a high-quality image from various phase aberrated images by modeling the variation in the speed of sound as stochastic. Experimental results using real measurements from tissue-mimicking phantom and textitin vivo scans confirmed that the proposed method can significantly reduce the phase aberration artifacts and improve the visual quality of deep scans.