A Neural Network Approach for Ultrasound Attenuation Coefficient Estimation

Quantitative ultrasound (QUS) imaging complements the standard B-mode images with a quantitative represen-tation of the target's acoustic properties. Attenuation coefficient is an important parameter characterizing these properties, with applications in medical diagnosis and tissue characterization. Traditional QUS methods use analytical models to estimate this coefficient from the acquired signal. Propagation effects, such as diffraction, which are difficult to model analytically are usually ignored, affecting their estimation accuracy. To tackle this issue, reference phantom measurements are commonly used. These are, however, time-consuming and may not always be feasible, limiting the existing approaches' practical applicability. To overcome these challenges, we leverage recent advances in the deep learning field and propose a neural network approach which takes the magnitude spectra of the backscattered ultrasound signal at different axial depths as the input and provides the target's attenuation coefficient as the output. For the presented proof-of-concept study, the network was trained on a simulated dataset, and learnt a proper model from the training data, thereby avoiding the need for an analytical model. The trained network was tested on both simulated and tissue-mimicking phantom datasets, demonstrating the capability of neural networks to provide accurate attenuation estimates from diffraction affected recordings without a reference phantom measurement.