Plane-Wave Ultrasound Beamforming: A Deep Learning Approach

TL;DR

This paper introduces a deep learning-based beamforming method for plane-wave ultrasound imaging that enhances image resolution and contrast from single transmission data, achieving high-quality results in simulations and in vivo tests.

Contribution

It presents a novel deep learning framework with wavelet-based multi-resolution architecture for improved TRF reconstruction in plane-wave ultrasound imaging.

Findings

High-resolution, high-contrast images achieved in simulations.

Method generalizes well to in vivo data without fine-tuning.

Preserves resolution, contrast, and framerate simultaneously.

Abstract

Medical ultrasound provides images which are the spatial map of the tissue echogenicity. Unfortunately, an ultrasound image is a low-quality version of the expected Tissue Reflectivity Function (TRF) mainly due to the non-ideal Point Spread Function (PSF) of the imaging system. This paper presents a novel beamforming approach based on deep learning to get closer to the ideal PSF in Plane-Wave Imaging (PWI). The proposed approach is designed to reconstruct the desired TRF from echo traces acquired by transducer elements using only a single plane-wave transmission. In this approach, first, an ideal model for the TRF is introduced by setting the imaging PSF as a sharp Gaussian function. Then, a mapping function between the pre-beamformed Radio-Frequency (RF) channel data and the proposed TRF is constructed using deep learning. Network architecture contains multi-resolution decomposition and reconstruction using wavelet transform for effective recovery of high-frequency content of the desired TRF. Inspired by curriculum learning, we exploit step by step training from coarse (mean square error) to fine ($\ell_{0.2}$) loss functions. The proposed method is trained on a large number of simulation ultrasound data with the ground-truth echogenicity map extracted from real photographic images. The performance of the trained network is evaluated on the publicly available simulation and \textit{in vivo} test data without any further fine-tuning. Simulation test results confirm that the proposed method reconstructs images with a high quality in terms of resolution and contrast, which are also visually similar to the proposed ground-truth image. Furthermore, \textit{in vivo} results show that the trained mapping function preserves its performance in the new domain. Therefore, the proposed approach maintains high resolution, contrast, and framerate simultaneously.