Ultrasound localization microscopy (ULM) enables super-resolution visualization of cerebral microvasculature; however, its clinical translation is hindered by the substantial computational burden of singular value decomposition (SVD)-based clutter filtering. To address this issue, we propose Compressed-Matrix Randomized QR Decomposition (CM-rQRD) as an accelerated and memory-efficient filtering framework. By integrating a Compressed-Matrix strategy into rQRD-based methods, the computational complexity of the decomposition step is substantially reduced. Specifically, the proposed methods achieve the lowest theoretical complexity, providing a 7.7-fold acceleration over conventional SVD while reducing memory usage to 50-65% of baseline levels. Although Power Doppler (PD) imaging exhibits a marginal 1-2 dB reduction in SNR and CNR, ULM reconstructions preserved spatial resolution and flow-related information comparable to SVDcov, with supplementary structural similarity values above 0.95. Furthermore, on an 800-frame in vivo dataset, CM-rQRDs complete clutter filtering in 72 ms on a CPU and in 7.7 ms on a GPU, underscoring their potential as online-compatible clutter-filtering modules for scalable ULM processing.
Delay-and-sum (DAS) beamforming is widely used in ultrasound flow imaging due to its computational simplicity; however, its high sidelobe levels significantly degrade image contrast. Coherence factor (CF) beamforming partially alleviates this limitation by emphasizing spatial coherence, yet residual incoherent energy remains and continues to impair image quality. In this study, we propose a dual-stage beamforming approach that integrates CF weighting with a frequency-domain filtering stage designed to discriminate and suppress incoherent energy while preserving coherent signal components. The proposed method, termed SCORE (Spectral COherence REfinement), introduces an additional coherence-based discrimination mechanism beyond conventional CF beamforming. Simulation experiments involving single-target, multi-target, and vascular scenarios demonstrate that SCORE achieves substantial sidelobe suppression while maintaining signal integrity. In vivo validation using an open-source contrast-enhanced rat brain and kidney dataset, as well as contrast-free human spleen further confirms improved flow detectability, reduced background noise, and enhanced power Doppler imaging performance, as evidenced by increases in SNR and CNR approaching 10 dB compared to CF and GCF beamforming. Overall, the SCORE beamformer provides a robust framework for high-contrast vascular ultrasound imaging, offering superior sidelobe and noise suppression relative to DAS, CF and GCF. To enhance computational efficiency, the beamforming pipeline was implemented on a graphics processing unit (GPU) using an optimized CUDA framework, achieving real-time beamforming performance with processing times below 30 ms per frame.
Focused ultrasound transducer concentrates acoustic energy to enable inspection and treatment of localized target regions. To achieve precise control of focal zone, piezoelectric element is needed to be shaped as acoustically designed curvature. In this study, ultra-precision laser-assisted diamond turning approach was demonstrated as an effective manufacturing approach to fabricate a highly controlled curved structure. Four types of geometry were designed based on vibrational characteristics and were precisely fabricated using developed laser-assisted diamond turning approach. The machining quality and piezoelectric properties of the machined single-crystal PMN-PT were characterized, with a P-V form error below 2 μm and surface roughness Ra below 5 nm. Single-element piston-type ultrasound transducers were assembled and bandwidth characterization was conducted through pulse-echo evaluation. Especially, acoustic beam fields of focused ultrasound transducers were tested in both numerical and experimental methods. The machined elements exhibited the intended focusing effect, with the concave-convex model showing around 60% increase in intensity compare with flat shaped model, and a full width at half maximum(FWHM) of 2.98 mm at a focusing distance of 16.5 mm.
This short communication proposes a joint wavenumber- and spatial-domain compressed sensing (JWSCS) framework for guided wavefield reconstruction. Existing sparse reconstruction methods typically operate in either the wavenumber domain or the spatial domain, each exploiting only one type of sparsity prior and exhibiting a lack of robustness against different damage scales. The proposed JWSCS framework simultaneously leverages wavenumber-domain modal sparsity and spatial-domain damage-induced sparsity. A joint sparse representation model is first established to integrate the spatial and wavenumber characteristics of the wavefield signals. A unified compressed sensing (CS) problem is then formulated, where a weighting coefficient is introduced to balance the sparsity constraints between the two domains. The non-convex problem is relaxed into a weighted ℓ1-minimization problem and efficiently solved via convex optimization. Simulation and experimental results validate that the proposed JWSCS framework enables more accurate wavefield reconstruction with significantly fewer measurements than conventional single-domain CS methods. On real measured data at 70% sample compressive ratio, JWSCS achieves an average Pearson correlation coefficient of 0.88 ± 0.02, improving by approximately 7% and 17% compared with wavenumber-domain and spatial-domain CS, respectively.
Accurate evaluation of corrosion in metal plates is essential across many industries, particularly in shipbuilding. Among available techniques, non-destructive testing (NDT) is especially valuable, as it enables the assessment of material degradation without compromising structural integrity. Within this domain, guided ultrasonic waves have attracted increasing attention in recent years due to their potential for estimating statistical corrosion parameters, such as mean thickness loss and its standard deviation. However, practical application of these methods remains challenging because of the complex interaction between wave propagation and spatially varying thickness profiles. This study presents an advanced methodology for precise estimation of corrosion parameters using deep learning-based inverse models. The corrosion morphology is modeled as a stochastic field, while guided wave responses, obtained through numerical simulations and recorded by a sensor network, are postprocessed to extract statistical descriptors. In particular, the mean vector and covariance matrix of sensor signals, including wave packet location, width, and amplitude, are computed. To ensure robustness, feature scaling and elliptic envelope-based covariance estimation are employed, effectively mitigating the influence of outliers, which are especially pronounced at higher levels of thickness loss and surface roughness. The inverse model establishes a mapping between extracted feature vectors and the corresponding corrosion parameters, enabling their direct prediction. This framework addresses the intrinsic non-uniqueness of the problem, where identical statistical descriptors may arise from infinitely many realizations of the stochastic corrosion field. Extensive numerical investigations demonstrate that reliable feature extraction, particularly robust handling of outliers, combined with the ability of deep neural networks to capture nonlinear relationships, is critical for accurate parameter estimation. Prediction performance is further improved by aggregating responses from multiple independent realizations of the stochastic corrosion field. Overall, the results demonstrate that the proposed framework, combining robust statistical feature extraction, covariance-based feature representation and nonlinear inverse modeling, provides improved prediction accuracy compared with the previously developed feature-based linear regression model and the conventional time-of-flight approach under the considered numerical simulation conditions.
Efficient and non-destructive separation of cells and pathogenic microorganisms is a core technical bottleneck in clinical point-of-care diagnostics. Traditional separation methods mostly rely on microchannel structures, external fluid actuation, or biochemical labeling, making it difficult to synergistically optimize separation efficiency, purity, cell viability, and system portability. To address this challenge, this study proposes a label-free separation platform based on traveling surface acoustic wave (TSAW) in an open sessile droplet. The platform innovatively places a droplet of only 2 μL asymmetrically at the edge of the acoustic aperture of an interdigital transducer (IDT), with approximately one-third of the droplet area lying within the acoustic propagation path. By exploiting the asymmetric leakage of TSAW at the solid-liquid interface, a gradient-distributed acoustic streaming vortex field is induced inside the droplet, enabling rapid separation of cell-bacteria systems without channels, pumps, or labels. To elucidate the separation mechanism, a multiphysics coupling model was established and a size-dependent force-balance criterion was established to analyze the motion behaviors of particles of different sizes. Experimental results showed that under 17.5 MHz TSAW excitation, human breast cancer cells (MCF-7, ∼20 μm in diameter) and Escherichia coli (E. coli, 2-3 μm) were successfully separated. The smaller bacteria were driven by the streaming-induced drag force and thus enriched at the droplet periphery, whereas the larger cells were dominated by the acoustic radiation force (ARF) and concentrated at the droplet center. A separation efficiency of 91.2 ± 1.2 % was achieved, and the cells retained high viability after separation. The platform requires only 2 μL of sample, combines simple operation, rapid separation, non-invasiveness, and portability, and can provide a lightweight, label-free paradigm for biological sample pretreatment in application scenarios, such as point-of-care screening of bloodstream infections, circulating tumor cell enrichment, and environmental microbial detection.
Upstream flow disturbances degrade ultrasonic transit-time flow measurement by introducing transit-time jitter along the acoustic path. This paper characterizes how butterfly valve closure angle affects the uncertainty of a dual-path transit-time sensor (DN250, water, 7.5D downstream) when a variable-speed pump maintains a constant flow rate. Eleven valve angles (0 to 81 degrees), a baseline, and an Asymmetric Swirl Generator were tested. Formal hypothesis testing showed that the linear blockage coefficient is not statistically significant at the 95% confidence level, yielding a reduced two-parameter model dominated by vortex shedding with sine-squared scaling of the valve angle. The saturation flow rate of (95 ± 9) m3/h remained constant across configurations, confirming bandwidth-limited uncertainty behavior governed by the sensor Nyquist-frequency limit. A transition near 63 degrees defines a practical operating limit of 60 degrees. The Asymmetric Swirl Generator produced approximately 15% higher turbulence than a fully open valve. The single experimental configuration and the two-point exponential region are discussed as constraints on generalizability.
Thin layers of porous materials are shown to produce an unexpected order-of-magnitude amplification of bulk-driven acoustic streaming generated by a high-powered airborne transducer, increasing the streaming velocity from 0.12 m/s to 2.5 m/s. Particle image velocimetry (PIV) measurements were used to characterise the streaming field produced by a Langevin horn operating at 27 kHz with a range of thin porous layers (various filter papers and a woven Kevlar fabric) placed in front of the source. We explain this effect as enhanced acoustic attenuation within the porous layer, which increases the acoustic body force on the fluid. The flow resistance of the porous layer counters this effect and reduces the streaming flow rate. Hence the streaming enhancement is thought to be governed by a subtle balance between the attenuation within the layer and the flow resistance of the layer. These experiments demonstrate that porous layers provide a passive mechanism for significantly enhancing acoustic streaming, enabling advances in haptics and levitation and broadening its applicability to a wide range of fluid systems.
Magnetic nanoparticles (MNPs) are attractive for biomedical applications due to their biocompatibility, controllable magnetic response, and multifunctionality in both diagnostic and therapeutic contexts. Magnetomotive ultrasound (MMUS) exploits these properties to detect MNPs-induced displacements, enabling nanoparticle mapping and quantitative assessment of tissue mechanics. However, conventional MMUS methods are limited by single-axis or quasi-static measurements, which fail to fully capture the multidirectional and dynamic behavior of soft tissues. In this study, we introduce a pixel-wise vectorial approach for the dynamic assessment of soft media, validated through experiments on tissue-mimicking phantoms and finite element simulations. By integrating lateral displacement data with axial measurements, the proposed method expanded viscoelastic map coverage from roughly 40% to nearly the full width of the lateral dimension within the transducer’s field of view, enabling fully spatially resolved viscoelastic imaging. The proposed technique also quantifies viscoelastic variations, capturing differences of up to 50% in both stiffness and viscosity between the tested samples, thus providing an enhanced MMUS framework capable of assessing treatment efficacy and distinguishing the mechanical properties of regions containing MNPs from surrounding tissue.
This work presents a reduced-parameter ultrasound electrical impedance spectroscopy (RP-UEIS) framework for estimating the complex electromechanical parameters of piezoelectric transducers - specifically lead zirconate titanate (PZT) ceramics - and the viscoelastic properties of polymer materials. The method combines a low-cost electrical impedance spectroscopy setup with an inverse finite-element fitting procedure to identify material parameters from measured impedance spectra. A sensitivity-based parameter analysis of the transducer reduces the dimensionality of the inverse problem from sixteen to ten parameters, significantly improving computational efficiency. Once the transducer parameters are calibrated, the viscoelastic properties of polymer samples can be identified in approximately one hour, compared with the ∼10 hours typically required by the original UEIS method. Using a PZT-4 reference transducer, complex elastic moduli and acoustic attenuation parameters are estimated at 1MHz for several polymeric materials commonly used in additive and subtractive manufacturing. The RP-UEIS framework is further demonstrated through the design of an acoustofluidic chip, where the identified polylactic acid (PLA) parameters are incorporated into a finite-element model of a circular half-wavelength resonator. RP-UEIS provides a practical and efficient tool for the characterization of polymer materials and for physically consistent parameter estimation in the modeling of polymer-based acoustofluidic devices.
Nonlinear ultrasonic techniques have attracted extensive attention for their superior capability in characterizing micro-defects which are hardly detectable by conventional linear ultrasonic approaches. However, the challenge in achieving effective imaging of local nonlinear responses has severely restricted the further engineering application. This paper theoretically establishes the inherent relationship between the deflection angle of difference-frequency reflected harmonics generated by in-plane nonlinear mixing of longitudinal and transverse waves and the mixing depth. It is demonstrated that, under specific theoretical constraints, the surface receiving position of the mixing wave remains constant regardless of the internal defect depth. This mechanism is verified by numerical simulations, based on which a novel multi-mode integrated ultrasonic transducer is developed. To verify the characterization performance of the proposed nonlinear mixing imaging method for porosity defects in additively manufactured components, four groups of additively manufactured specimens with different pore damage are prepared by precisely regulating the process parameters of laser additive manufacturing. On this basis, nonlinear parameter scanning and imaging of internal pore defects in the as-fabricated specimens are successfully realized using only a single integrated transducer. Theoretical analysis shows that, for fixed transducer parameters, the optimal receiving position only depends on the product of the frequency ratio and acoustic velocity ratio. This feature enables the proposed method to be extended to the inspection of arbitrary isotropic materials, providing a valuable reference for engineering applications of nonlinear ultrasonic techniques.
This study presents a strategy of non‑invasive transcranial ultrasound stimulation (TUS) to mitigate sleep insufficiency. Mice were subjected to 20 h of sleep deprivation, while receiving 10 min of TUS of the basal forebrain for 7 days. Sleep architecture was evaluated by electroencephalographic analysis, while anxiety-like behavior and cognitive performance were assessed using open field testing and Y-maze. Hippocampal neuronal integrity was examined by Nissl staining, and serum cortisol levels was assessed by ELISA, whereas basal forebrain cholinergic activation was analyzed using c-Fos immunofluorescence. TUS significantly increased rapid eye movement (REM) sleep compared with the sleep-deprived group (14.97 ± 1.05% vs. 10.86 ± 1.11%, P < 0.05), while non-rapid eye movement-related delta power rose to 130.77% of normal control levels (P < 0.0001) and REM-related theta power was enhanced (P < 0.05). TUS improved cognitive performance, increasing Y-maze alternation rate to 61.42 ± 1.31% versus 50.05 ± 1.59% in sleep-deprived mice (P < 0.001). Histological analysis demonstrated marked neuroprotection, with TUS significantly restoring hippocampal neuronal counts in the cornu ammonis 1 and dentate gyrus. In addition, serum cortisol levels were elevated toward normal values (P < 0.05), accompanied by increased c-Fos expression, suggesting enhanced basal forebrain neuronal activity and potential cholinergic involvement. These findings suggest that TUS provides a novel non-pharmacological treatment strategy targeting the basal forebrain in the sleep deprivation mouse model.
Brain tissue mechanics play a critical role in neurological disorders. However, noninvasive characterization is impeded by the tissue's biphasic composition and small shear modulus. We developed a transcranial ultrasound viscoelasticity and fluidity imaging method using multiscale spatiotemporal deep learning. This method integrates multiscale pyramidal convolution and hybrid losses to overcome the limitations of low-SNR signals and small shear displacements through dual-branch processing, capturing high-frequency details (300 Hz) and low-frequency patterns (100 Hz) simultaneously. The proposed method achieves transcranial ultrasound viscoelasticity and fluidity imaging in brain tissue, outperforming the pyramid, warping, and cost volume network (PWC-Net) with a 4% reduction in low-frequency reconstruction errors and enhanced high-frequency signal fidelity. Validation of the method was conducted on simulation data, phantom data, and ex vivo animal data, demonstrating dual-tumor imaging at a 5.5 mm radius with quantitatively superior metrics (SNR=17.43, CNR=5.64) compared to existing methods.
This study investigates the microstructure and mechanical properties of 14Ni3Cr3Mo2Mn ultra-high-strength steel components fabricated by ultrasonic impact-assisted wire arc additive manufacturing (UI-WAAM) at four interlayer temperatures: 80 °C, 150 °C, 200 °C, and 300 °C. Results reveal that interlayer temperature non-linearly regulates microstructural evolution through thermal cycling. The average grain size in the top region increased from 32.9 μm at 80 °C to 49.0 μm at 200 °C and then decreased to 40.2 μm at 300 °C owing to the expanded effective action range of UI. The middle region experienced significant grain coarsening, reaching 84.6 μm at 300 °C due to heat accumulation. The phase composition varied with interlayer temperature, while the middle region generally retained a higher fraction of austenite due to repeated thermal cycling and elemental redistribution. Mechanical properties deteriorated as temperature increased. Optimal performance was achieved at 80 °C, with ultimate tensile strengths of approximately 1230 MPa, elongations between 18.0 % and 20.0 %, and a microhardness of 315.3 HV0.5. Simulations showed that elevated interlayer temperatures widened and prolonged the effective UI action zone between 800 °C and 1450 °C. While UI dominates strengthening at low temperatures, thermal accumulation diminishes its benefits at high temperatures. This work clarifies the temperature-UI coupling mechanism, providing critical insights for optimizing the manufacturing process.
