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Item type:Publication, Dispersion model for the optical absorption of two-dimensional materials(American Physical Society, 2026-01-07)The optical response of two-dimensional systems is strongly influenced by tightly bound excitons. Despite its relevance in helping predict device performance, the current derivation of the two-dimensional Elliott equation is rarely used to estimate exciton binding energy and band gap in these systems, primarily due to its lack of an analytical form and the complexity introduced by substrate interactions. In this work, we present a new approach based on optical absorption measurements via an extended Elliott band fluctuations model, which notably provides analytical expressions for isotropic systems. Our method accurately captures the optical absorption near the band edge, fully incorporating spin–orbit band splitting and substrate effects via the Keldysh effective potential. It also includes the influence of surface and interface contributions to the dielectric environment, which give rise to localized defect-related absorption features. We apply this approach to key transition metal dichalcogenides (MoS2, MoSe2, WS2, andWSe2) exhibiting small and large spin-orbit band splitting, on various substrates and over a broad temperature range. The results show good agreement with magnetoabsorption and photoluminescence measurements, allowing for an accurate description of excitonic properties using only optical measurements and is readily extendable to other 2D isotropic materials.2 - 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, 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, 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, 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