Ultrasound Phase Aberrated Point Spread Function Estimation with Convolutional Neural Network: Simulation Study

TL;DR

This paper introduces a deep learning approach using U-Net architectures to accurately estimate phase-aberrated point spread functions in ultrasound imaging, addressing challenges caused by tissue inhomogeneity.

Contribution

It presents a novel deep learning framework trained on synthetic data to improve PSF estimation under phase aberration in ultrasound imaging.

Findings

Complex U-Net outperforms standard U-Net.

Log-compressed B-mode perceptual loss yields best results.

Method effectively estimates PSFs with phase aberration in simulations.

Abstract

Ultrasound imaging systems rely on accurate point spread function (PSF) estimation to support advanced image quality enhancement techniques such as deconvolution and speckle reduction. Phase aberration, caused by sound speed inhomogeneity within biological tissue, is inevitable in ultrasound imaging. It distorts the PSF by increasing sidelobe level and introducing asymmetric amplitude, making PSF estimation under phase aberration highly challenging. In this work, we propose a deep learning framework for estimating phase-aberrated PSFs using U-Net and complex U-Net architectures, operating on RF and complex k-space data, respectively, with the latter demonstrating superior performance. Synthetic phase aberration data, generated using the near-field phase screen model, is employed to train the networks. We evaluate various loss functions and find that log-compressed B-mode perceptual loss achieves the best performance, accurately predicting both the mainlobe and near sidelobe regions of the PSF. Simulation results validate the effectiveness of our approach in estimating PSFs under varying levels of phase aberration.