Plane-Wave Ultrasound Beamforming Through Independent Component Analysis

TL;DR

This paper introduces a novel ultrasound beamforming method using independent component analysis (ICA), improving image resolution and contrast in plane-wave imaging by treating beamforming as a blind source separation problem.

Contribution

It formulates plane-wave beamforming as a blind source separation problem and adapts ICA to enhance ultrasound image quality, demonstrating superior performance over classical methods.

Findings

Improves lateral resolution by up to 36.5%

Enhances contrast by 10% compared to classical delay and sum

Demonstrates robustness to noise

Abstract

Beamforming in plane-wave imaging (PWI) is an essential step in creating images with optimal quality. Adaptive methods estimate the apodization weights from echo traces acquired by several transducer elements. Herein, we formulate plane-wave beamforming as a blind source separation problem. The output of each transducer element is considered as a non-independent observation of the field. As such, beamforming can be formulated as the estimation of an independent component out of the observations. We then adapt the independent component analysis (ICA) algorithm to solve this problem and reconstruct the final image. The proposed method is evaluated on a set of simulation, real phantom, and in vivo data available from the PWI challenge in medical ultrasound. The performance of the proposed beamforming approach is also evaluated in different imaging settings. The proposed algorithm improves lateral resolution by as much as $36.5\%$ and contrast by $10\%$ as compared to the classical delay and sum. Moreover, results are compared with other well-known adaptive methods. Finally, the robustness of the proposed method to noise is investigated.