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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, Nonlinearity parameter estimation method from fundamental band signal depletion in pulse-echo using a dual-energy model(Acoustical Society of America, 2025)The estimation of the nonlinearity parameter (B/A) has the potential to be used in the clinical diagnosis of conditions such as liver steatosis. Recently, a pulse-echo method to estimate B/A based on the theory of the fundamental band amplitude depletion of weak waves, namely, the depletion method, was proposed. In the present work, the depletion method is presented with more technical detail. Then, the robustness of the depletion method is assessed by using simulations that diverge from the model requirements: (1) monochromatic plane wave propagation and (2) quadratic power-law frequency dependence attenuation. Regarding requirement (1), the results led to a critical finding that when using wideband pulses (37%–113% bandwidth), the bias of the B/A estimates is larger than the bias obtained using narrowband pulses (11%–28% bandwidth), even if requirement (2) holds. Regarding requirement (2), power-law frequency dependence closer to those of soft tissues, i.e., 1.1 or 1.2, using narrowband pulses presented bias of less than 10%. The use of narrowband pulses also was shown to be robust when the reference phantom and sample had attenuation mismatches of around 60%. Finally, the experimental feasibility of the depletion method was evaluated, showing results with good accuracy (bias <17%), which are consistent with the observations in the simulations.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Spatially Weighted Fidelity and Regularization Terms for Attenuation Imaging(Institute of Electrical and Electronics Engineers Inc., 2025)Quantitative ultrasound (QUS) holds promise in enhancing diagnostic accuracy. For attenuation imaging, the regularized spectral log difference (RSLD) can generate accurate local attenuation maps. However, the performance of the method degrades when significant changes in backscatter amplitude occur. Variations in the technique were introduced involving a weighted approach to backscatter regularization, which, however, is not effective when changes in both attenuation and backscatter are present. This study introduces a novel approach that incorporates an L1-norm for backscatter regularization and spatially varying weights for both fidelity and regularization terms. The weights are calculated from an initial estimation of backscatter changes. Comparative analyses with simulated, phantom, and clinical data were performed. When changes in backscatter and attenuation occur, the proposed approach reduced the lowest root mean square error by up to 73%. It also improved the contrast-to-noise ratio (CNR) by a factor of 4.4 on average compared with previously available methods, considering the simulated and phantom data. In vivo results from healthy livers, thyroid nodules, and a breast tumor further confirm its effectiveness. In the liver, it is shown to be effective at reducing artifacts of attenuation images. In thyroid and breast tumors, the method demonstrated an enhanced CNR and better consistency of the attenuation measurements with the posterior acoustic enhancement. Overall, this approach offers promise for enhancing ultrasound attenuation imaging by helping differentiate tissue characteristics that may indicate pathology.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 - 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, Multi-Frequency Regularized Approach for Simultaneous Estimation of the Acoustic Nonlinearity Parameter and Attenuation Coefficient(IEEE Computer Society, 2025)The acoustic nonlinearity parameter (B/A) could enhance conventional ultrasound diagnostics in diseases associated with changes in fat tissue content. Recently, a simultaneous estimator of the B/A and the attenuation coefficient (AC) in pulse-echo was introduced, which was based on fitting measurements derived from backscattered data from a dual-energy model to a nonlinear model using the Gauss-Newton Levenberg-Marquardt algorithm (GNLM). However, the GNLM algorithm presented high sensitivity to the initial guess values. This paper improves the Gauss-Newton method by using data from several tone-burst transmissions at different center frequencies rather than only one narrowband tone-burst. In addition, it is combined with a total variation regularization approach (GNTV). The results in simulated and experimental phantoms suggest that incorporating regularization and increasing the number of transmission frequencies improves robustness compared to the GNLM method by accurately estimating B/A values (mean relative error less than 13% in the experiments).1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Integrating Deep Learning into PnP-ADMM for Ultrasound Attenuation Coefficient Estimation(IEEE Computer Society, 2025)Estimating the attenuation coefficient slope (ACS) is essential for tissue characterization in quantitative ultrasound (QUS). Traditional model-based methods such as the regularized spectral log difference (RSLD) rely on manually tuned priors, while recent end-to-end deep learning approaches struggle to generalize to in vivo data. This work proposes a hybrid method that integrates pre-trained Attention U-Nets within a Plug-and-Play ADMM framework. The fidelity term is replaced by a network conditioned on spectral inputs and the iteration index, while a second network acts as a learned regularizer. The method was evaluated on physical phantoms and in vivo breast and thyroid acquisitions, after being trained entirely on simulations. Results show improved accuracy and generalization over RSLD and end-to-end baselines, suggesting that embedding a pre-trained deep learning model within a physics-based framework enhances robustness and enables more reliable ACS estimates in real-world scenarios.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of the Effect of Clutter Reduction in Attenuation Coefficient Estimation(IEEE Computer Society, 2025)Quantitative ultrasound (QUS) aims to provide objective measurements of tissue properties, thereby overcoming the limitations of conventional subjective assessments. A relevant clinical application of QUS is the assessment of metabolic dysfunction-associated steatotic liver disease (MASLD). However, in abdominal imaging, tissue heterogeneity increases the likelihood of acoustic interference, known as clutter, a type of image degradation caused by multiple scattering, reverberation, or off-axis reflections that introduce unwanted signals into the data received by the transducer. Clutter has a negative effect on the estimation of the attenuation coefficient (AC). In this study, the ADMIRE algorithm was evaluated to reduce clutter and improve the robustness of AC estimates.Results in simulations with known values (0.4-0.6 dB/cm-MHz) show that ADMIRE reduced the standard deviation by up to 56.7% (from ±0.3 to ±0.13) and the overestimated maximum AC value decreased by approximately 83.3% compared to the ground truth value (from around 1.2 to 0.7 dB/cm-MHz), which reduced the mean error from 35% to 13.3%. In clinical data from healthy livers, the algorithm consistently improved accuracy, reducing the standard deviation by up to 27.8% and producing values within the range expected according to the literature (0.56-0.63 dB/cm-MHz).2