Volumetric Attenuation Estimation Using a Matrix Array with Spatially Weighted Fidelity and Regularization

Abstract

Quantitative ultrasound (QUS) enables system-independent tissue characterization by deriving acoustic biomarkers. Among these, attenuation imaging has emerged as a promising tool for clinical applications. While regularization techniques have been proposed for parameter estimation, balancing spatial resolution and accuracy remains a critical challenge. Volumetric QUS imaging offers enhanced resolution by leveraging 3D spatial information, yet simultaneous variations in backscatter and attenuation properties often degrade accuracy. Recently, a spatially adaptive approach that weighted the regularization and fidelity terms (SWIFT) has demonstrated success in mitigating this effect. This study investigates the benefits of combining SWIFT and volumetric QUS imaging using a matrix array transducer. The method was compared to the 2D versions and a non-weighted approach using phantom data. The root mean square error is reduced from 40% to 10% compared to the 2D algorithms, and the contrast-to-noise ratio increases by at least 30%. The method with spatially weighted regularization and fidelity improves the precision-resolution trade-off in QUS reconstruction despite variations in backscatter and attenuation.