Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning

TL;DR

This paper introduces a deep learning method for estimating breast tissue sound speed from ultrasound signals, aiming to improve image quality and disease detection by accounting for tissue variability.

Contribution

It presents a novel deep neural network trained on a large simulated dataset for accurate, generalizable sound speed estimation in breast ultrasound imaging.

Findings

Accurately estimates sound speed in simulated, phantom, and in-vivo data.

Enhances ultrasound image reconstruction by accounting for tissue-specific sound speeds.

Model generalizes well due to thermal noise augmentation during training.

Abstract

Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians with diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form B-mode images for diagnosis. However, the various types of breast tissue, such as glandular, fat, and lesions, differ in sound speed. These differences can degrade the image reconstruction process. Alternatively, sound speed can be a powerful tool for identifying disease. To this end, we propose a deep-learning approach for sound speed estimation from in-phase and quadrature ultrasound signals. First, we develop a large-scale simulated ultrasound dataset that generates quasi-realistic breast tissue by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We developed a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map from inputting three complex-value in-phase and quadrature ultrasound images formed from plane-wave transmissions at separate angles. Furthermore, thermal noise augmentation is used during model optimization to enhance generalizability to real ultrasound data. We evaluate the model on simulated, phantom, and in-vivo breast ultrasound data, demonstrating its ability to accurately estimate sound speeds consistent with previously reported values in the literature. Our simulated dataset and model will be publicly available to provide a step towards accurate and generalizable sound speed estimation for pulse-echo ultrasound imaging.