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Item type:Publication, Regularized Joint Estimator of the Nonlinearity Parameter and Attenuation Coefficient Using a Nonlinear Least-Squares Algorithm(SAGE Publications, 2025)The acoustic nonlinearity parameter (B/A) could enhance the diagnostic capabilities of conventional ultrasonography and quantitative ultrasound in tissues and diseases. Nonlinear acoustic propagation theory of plane waves has been used to develop a dual-energy model of the depletion of the fundamental related to the Gol’dberg number and subsequently to the B/A of media (a reference phantom is used as a baseline). The depletion method, however, needs a priori information of the attenuation coefficient (AC) of the assessed media. For this reason, recently, a work introduced a simultaneous estimator of the B/A and AC based on fitting depletion method measurements to a nonlinear model using the iterative algorithm Gauss-Newton Levenberg-Marquardt (GNLM). However, the GNLM method presented high sensitivity to the initial guess values of the algorithm which limits the robustness of the approach. In the present work, the Gauss-Newton method is combined with a total variation regularization approach (GNTV), which is achievable by expanding the nonlinear model of the GNLM method for joint estimation of the B/A and AC of all pixels of the parametric images instead of a block-wise approach. In addition, the GNTV used compounding data from several tone-burst transmissions at different center frequencies rather than only one narrowband tone-burst. The results suggest that incorporating regularization and increasing the number of frequencies improves the robustness of the GNTV compared to the GNLM method by accurately estimating B/A values in uniform and nonuniform experimental phantoms (mean relative error less than 18%). The best performance of B/A reconstruction was observed when the sample medium exhibited a constant Gol’dberg number.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improved reference frequency method for attenuation imaging using multi-frequency coupling(IEEE Computer Society, 2025)A well-known acoustical parameter used for tissue characterization is the attenuation coefficient slope (ACS), which has shown potential in clinical applications, such as quantization of liver fat content. Conventional methods estimate ACS from backscattered echo data in the spectral domain. However, they are affected by system dependencies and require a calibrated reference phantom to compensate for diffraction effects. To overcome these limitations, the Reference Frequency Method (RFM) was introduced, enabling ACS estimation without a reference phantom. Building on this framework, a method named TNV-RFM that leverages the multi-frequency coupling using the Total Nuclear Variation is proposed. Data from simulated and tissue-mimicking phantoms, and in vivo liver acquisitions from healthy and metabolic dysfunction–associated steatotic liver disease (MASLD) volunteers—diagnosed through clinical evaluation, cardiometabolic profile, and histopathological analysis of laparoscopic biopsies—were used to compare both methods. Results demonstrated a consistently lower coefficient of variation with TNV-RFM (12.2%, 23.9%, and 30.7% for simulations, phantoms, and liver samples, respectively) vs RFM (20.7%, 38.9%, and 54.1%). These findings suggest that TNV-RFM provides more stable and reliable ACS estimates, further improving the conventional RFM framework in attenuation imaging.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ACS-Net: A Deep Unfolded ADMM Framework for Ultrasound Attenuation Imaging(IEEE Computer Society, 2025)Ultrasound attenuation imaging is gaining traction for its promising clinical diagnostic applications. Estimation methods such as Spatially Weighted Fidelity and Regularization Terms (SWIFT) and its deep learning-aided variant (DL-SWIFT) have demonstrated improved contrast-to-noise ratio (CNR) and more consistent attenuation coefficient slope (ACS) estimates through the use of spatially weighted formulations. However, both methods may still produce artifacts in heterogeneous regions with abrupt backscatter coefficient changes. To address these limitations, we propose ACS-Net, a deep unfolded Alternating Direction Method of Multipliers framework that integrates learned denoising operations within the classical iterative optimization process to reduce ACS estimation bias while preserving inclusion delineation. Phantom experiments confirmed a bias reduction of more than 40% on inclusions, while in vivo thyroid nodule results showed that ACS-Net decreases background coefficient of variation by nearly 50% and improves CNR by more than 30% compared to SWIFT and DL-SWIFT. These findings highlight the clinical promise of deep unfolded optimization methods for reliable and accurate ultrasound attenuation imaging.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Volumetric Attenuation Estimation Using a Matrix Array with Spatially Weighted Fidelity and Regularization(IEEE Computer Society, 2025)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.2