Photoacoustic imaging of porous heterogeneous media is fundamentally challenged by spatially distributed optical absorbers that simultaneously serve as acoustic scatterers. This dual role causes multiple scattering, wavefront distortion, and frequency-dependent attenuation, which cannot be decoupled by conventional homogeneous-medium reconstruction algorithms. As a result, deep-tissue images suffer from severe image blurring and irreversible loss of high-spatial-frequency microstructural details. To address this, we propose a physics-constrained deep learning framework termed the Layer-Stripping Multi-Scattering Network (LSMS-Net), which recursively solves the inverse multiple scattering problem. LSMS-Net implements a top-down, curriculum-driven strategy to progressively estimate propagation-induced effects and correct raw measurements layer by layer. The framework integrates a Ghost Network for non-local reverberation, an Adaptive Scattering Point Spread Function Layer for wavefront distortion, and a Spectral Consistency Constraint for frequency-dependent attenuation. Quantitative evaluation via numerical simulations and phantom experiments confirms accurate, high-fidelity reconstruction of deep porous structures under strongly scattering, showing potential for clinical application in bone-related diseases.

Artificial intelligence (AI) empowers innovative diagnostic tools for common diseases, yet its clinical application in skeletal health evaluation is constrained by unsatisfactory accuracy, owing to the inherent porous and poroelastic biophysical features of bone. To address such bottlenecks amid global population aging, this study targets skeletal health and develops a reliable AI framework for precise bone microstructural characterization. We proposed Biot-PINN, a physics-informed neural network embedded with Biot's poroelasticity theory to characterize mechanical responses and wave propagation in poroelastic bone tissues. By decoding photoacoustic signals encoding bone mineral and microstructural features, the framework enables automatic bone microstructural grading. Experimental results reveal that Biot-PINN reaches an accuracy of 97

Wave propagation through complex poroelastic multilayered media is difficult to model and invert because pronounced heterogeneity, scattering, mode conversion and fluid-solid coupling jointly distort acoustic signals during propagation. Here we present Physics-Constrained Learning for Complex Multilayered Media (PCL-CMM), a general framework that integrates Biot's poroelastic theory with the elastic wave equation to bridge the gap between physically rigorous wave modelling and data-driven learning. PCL-CMM constructs a high-fidelity digital twin that dynamically computes an effective acoustic stiffness tensor for forward wave modelling and incorporates the resulting physical constraint as a loss term to regularize the training of deep neural networks. We demonstrate PCL-CMM on transcranial photoacoustic imaging, where skull-induced acoustic distortions severely degrade image formation. Across simulations and ex vivo experiments, PCL-CMM effectively compensates for these distortions and improves SSIM by more than 0.06 compared with purely data-driven neural networks. This work establishes a physics-constrained learning framework for acoustic wave modelling in complex poroelastic multilayered media.

Underwater imaging technology has been confronted with the challenge in computation, storage and transmission. Compressive sensing with advantage in reducing data redundancy is widely used in underwater imaging. However, compressive sensing with fixed sampling rate restricts reconstruction quality of the primary object. To address this issue, this paper innovatively proposes a compound depth-based adaptive block compressed sensing method (CD-ABCS). The compound depth matrix that is correlated with underwater depth information, image saliency and image variance is used to set sampling rate of the image block. According to the compound depth matrix, the original image is divided into multi-level image blocks to conduct sparse sampling. Reconstructed image blocks are stitched into a complete image. To verify propose method, experiments including method comparison, ablation study and parameter optimization are executed. Experimental results show that, the proposed method is certified to have a significant improvement in image quality by comparing with other adaptive block compressive sensing methods. Specifically, when the global sampling rate is 0.5, the peak signal-to-noise ratio (PSNR) is increased by 1dB, and the structural similarity (SSIM) improves by at least 0.015. Proposed method is capable of enhancing image quality at various global sampling rates.

Objective: Noninvasive photoacoustic (PA) imaging techniques afford abundant microstructure information for the diagnosis and therapeutic monitoring of diseases. However, their use for bone tissue imaging is challenging owing to the high scattering and attenuation properties of bone tissue. The PA signal waveforms inherently contain optical and ultrasonic properties related to bone health. This study entailed the development of a robust, compensation-free PA eigen waveform analysis (PEWA) method for characterizing high-scattering cancellous bone in transmission mode. Methods: Numerical simulations and experimental studies were conducted on cancellous bone models with various bone mineral densities (BMDs), optical, and ultrasonic properties. The resulting PA signals were analyzed using PEWA method, facilitating the quantification of parameters related to bone conditions, such as the exponential growth coefficient. Results: The simulation results indicate that bone specimens with lower BMDs have lower exponential growth coefficients. Furthermore, we found that the exponential growth coefficient has better robustness and stability than conventional amplitude-based parameter. The experimental findings from animal cancellous bone tissues ex vivo with different BMDs were in close agreement with the simulation results, thus demonstrating that the PEWA method can perform BMD assessments for cancellous bone. Significance: Considering that PA measurements are nonionizing and noninvasive and have sufficient penetration in both nonorganic (bone matrix) and organic tissues (bone marrow), the proposed compensation-free, PEWA bone evaluation method has the potential to facilitate early and rapid clinical assessment of osteoporosis. The proposed approach has considerable applicability in the domains of miniaturization equipment intelligent evaluation of bone health.

Binary photoacoustic holography, which combines the photoacoustic effect with binary optical modulation, offers a low-cost approach to generating acoustic holograms. In this work, the ability of binary light fields to form acoustic holograms was investigated through numerical simulations. Binary amplitude patterns were optimized using the Direct Binary Search (DBS) algorithm to generate specific acoustic pressure distributions, such as the letters "T" and "J", at the imaging plane. Photoacoustic wave propagation was simulated using MATLAB k-Wave toolbox. The results showed that binary light fields can effectively generate acoustic holograms at the predefined plane with a high image reconstruction quality. Furthermore, distinct acoustic fields could be formed at multiple distances when different regions of the binary light field were optimized for different imaging planes. These acoustic fields exhibited low inter-plane crosstalk, indicating its potential for three-dimensional acoustic field control. These findings suggest that binary photoacoustic holography enables spatial control of acoustic fields without the need for complex multi-level light modulation, highlighting its potential for biomedical imaging and ultrasonic manipulation.

Transcranial focused ultrasound (tFUS) is an emerging noninvasive neuromodulation technique, but efficient acoustic energy delivery through the skull remains a major challenge due to skull-induced attenuation and distortion. In this study, we systematically characterized the frequency-dependent transmission efficiency of human skulls using both experimental measurements and acoustic simulations. By conducting frequency sweeps across different skull thicknesses and locations, we identified significant spectral variability and site-specific optimal transmission frequencies. Spatial field mapping further confirmed that transmission dips not only reduce energy delivery but also lead to severe focal distortion. To address this, we proposed a frequency-optimized ultrasound array design by selecting the optimal frequency for each element based on local transmission properties. Results demonstrated that the optimized-frequency array significantly improved the focal pressure amplitude and spatial confinement compared to conventional uniform-frequency arrays.

Ultrasound neuromodulation shows promise for treating neurological disorders, but the underlying mechanisms remain unclear. Here, we developed an integrated surface acoustic wave (SAW) ultrasound chip enabling simultaneous electrophysiological recording and Ca2+ imaging of cultured hippocampal neurons to investigate neuronal excitability and synaptic transmission during ultrasound stimulation. This study revealed, for the first time, three distinct neuronal response patterns induced by SAW ultrasound: an immediate response showing rapid activation, a delayed response exhibiting facilitation after several minutes, and a non-response maintaining baseline activity. Ultrasound stimulation increased action potential firing, enhanced excitatory postsynaptic currents, and elevated intracellular Ca2+ levels. These effects were dependent on extracellular Ca2+ influx and primarily dominated by L-type Ca2+ channels. Our findings suggest that individual neurons exhibit heterogeneous responses to SAW ultrasound stimulation based on their intracellular Ca2+ levels and L-type Ca2+ channel activity. This integrated approach provides new insights into the cellular mechanisms of ultrasound neuromodulation while highlighting the potential of SAW technology for precise, cell-type-specific neural control.

Confocal Raman spectroscopy has emerged as a powerful tool for noninvasive chemical analysis of human skin, offering unparalleled molecular specificity. Despite its promise, the pervasive issue of etalon artifacts─caused by multiple reflections and interference in back-illuminated charge-coupled devices─significantly hinders its application, particularly in pigmented skin, which is prevalent in Asia. Addressing this critical challenge, we present a toward-automated digital processing workflow that combines independent component analysis and continuous wavelet transform to effectively identify and eliminate etalon artifacts. By leveraging the consistent fringe patterns generated in a dual-wavelength excitation Raman system, our method isolates the specific frequency and wavelength ranges impacted by etalon artifacts without requiring additional calibration or manual parameter tuning. Validation on in vivo Raman spectra from diverse skin samples demonstrates that this approach not only removes etalon artifacts with high precision but also preserves critical spectral features, establishing a robust foundation for accurate and detailed skin analysis.

BackgroundPhotoacoustic spectral analysis has been demonstrated to be efficacious in the diagnosis of prostate cancer (PCa). With the incorporation of deep learning, its discrimination accuracy is progressively enhancing. Nevertheless, individual heterogeneity persists as a significant factor that impacts discrimination performance.ObjectiveExtracting more reliable features from intricate biological tissue and augmenting discrimination accuracy of the prostate cancer.MethodsSupervised contrastive learning is introduced to explore its performance in photoacoustic spectral feature extraction. Three distinct models, namely the CNN-based model, the supervised contrastive (SC) model, and the supervised contrastive loss adjust (SCL-adjust) model, have been compared, along with traditional feature extraction and machine learning-based methods.ResultsThe outcomes have indicated that the SCL-adjust model exhibits the optimal performance, its accuracy rate has increased by more than 10% compared with the traditional method. Besides, the features extracted from this model are more resilient, regardless of the presence of uniform or Gaussian noise and model transfer. Compared with CNN model, the transfer performance of the proposed model has improved by approximately 5%.ConclusionsSupervised contrast learning is integrated into photoacoustic spectrum analysis and its effectiveness is verified. A comprehensive analysis is conducted on the performance improvement of the proposed SCL-adjust model in photoacoustic prostate cancer diagnosis, its resistance to noise, and its adaptability to the data heterogeneity of different systems.

The inadequate generation of reactive oxygen species (ROS) and metastasis of malignant tumors are critical factors that limit the efficacy of conventional sonodynamic therapy in cancer treatment. Herein, an engineered piezocatalyst: cholesterol oxidase (CHO)-loaded Pt-ZnO nanoparticles (Pt-ZnO/CHO) that can explosively generate large amounts of ROS and block the metastasis of tumor, is developed for improving piezocatalytic tumor therapy. In this process, Pt-ZnO can substantially generate ROS via initiating ultrasound (US)-triggered piezocatalytic reactions. In situ-grown Pt nanoparticles not only optimize piezocatalytic activities but also facilitate oxygen (O2) production, thereby synergistically boosting ROS generation. Moreover, O2 produced by Pt-ZnO can accelerate the depletion of excess cholesterol in tumor cells under CHO catalysis to disrupt the integrity of lipid rafts and inhibit the formation of lamellipodia, significantly suppressing the proliferation and metastasis of tumor cells. This strategy by promoting ROS generation and blocking the metastatic pathway of cancer cells offers a new idea for enhanced efficacy-oriented cancer therapeutic strategies.

The manipulation of the various forms of behavior of acoustic propagation holds significant importance in the realm of underwater acoustic communication and detection. Recent advancements have highlighted the efficacy of metamaterials and metasurfaces in precisely shaping acoustic wave fronts. However, unlike their counterparts in airborne environments, where air-solid boundaries are conventionally treated as rigid, underwater metastructures face challenges due to fluid-structure coupling, inevitably leading to near-field distortions. These distortions considerably impede the efficacy of manipulating acoustic wave fronts, especially at large angles. Here, we introduce an efficient underwater metasurface employing a grating structure that utilizes an overarching optimization strategy. Distinct from previous studies, this strategy comprehensively addresses both nonlocal interaction among all subunits and fluid-structure interaction. The employed methodology convincingly demonstrates the achievement of highly efficient abnormal reflections at various angles. The simulation results showcase an exceptional modulation efficiency exceeding 99% for the designed metagratings, encompassing a wide reflection-angle range of 45-85. Additionally, experimental validation corroborates the effectiveness of these underwater metagratings. This study might present an effective technique for advancing underwater acoustic devices, offering a valuable contribution to the field.

Abstract The Angular Spectrum (AS) method is commonly used to calculate the propagation of scalar wave fields. This study extends the application of AS to vector wave propagation, specifically addressing acoustic waves in anisotropic media. The approach involves using plane wave spectra to express all possible mode waves, namely one q-P wave and two q-S waves, that propagate in various directions in the medium. These AS are determined by applying boundary conditions at the source surface where the vector waves are generated and the modes waves are coupled. AS decomposition of waves requires the determination of complex wavenumber vectors (or slownesses) through the Christoffel equation, including both propagating and evanescent waves. The determination of the acoustic beam profile in TeO2 crystals is presented as an application example of AS.

AbstractBiomedical photoacoustics has shown great potential for precise medical diagnosis because it can provide structural, physiological/pathological characteristics, and metabolic information of biological tissues noninvasively in vivo. Photoacoustic imaging has made great breakthroughs in many preclinical studies, including microvascular imaging, blood oxygen detection, and tumor detection. However, compared with photoacoustic imaging, the photoacoustic spectrum can provide more information, such as the rich molecular information in the optical spectrum and the rich microstructural information in the ultrasonic spectrum, which is closely related to the disease evolution process. Recently, photoacoustic spectrum analysis (PASA) has demonstrated the ability to quantitatively extract physicochemical information from biological tissues to distinguish between normal and diseased tissues, especially for classifying, grading, and staging cancer tissue, making it one of the most promising methods for noninvasive, accurate diagnosis of clinical diseases. In this chapter, we introduce the methods of PASA for the diagnosis of soft tissue diseases.

Osteoporosis is a systemic disease with a high incidence in the elderly and seriously affects the quality of life of patients. Photoacoustic (PA) technology, which combines the advantages of light and ultrasound, can provide information about the physiological structure and chemical information of biological tissues in a non-invasive and non-radiative way. Due to the complex structural characteristics of bone tissue, PA signals generated by bone tissue are non-stationary and nonlinear. However, conventional PA signal processing methods are not effective for non-stationary signal processing. In this study, an empirical mode decomposition (EMD)-based Hilbert-Huang transform (HHT) PA signal analysis method, called HHT PA signal analysis (HPSA), was developed to assess the microstructure information of bone tissue, which is closely related to bone health. The feasibility of the HPSA method in bone health assessment was proven by numerical simulation and experimental studies on animal samples with different bone volume/total volume (BV/TV) and bone mineral densities. First, based on adaptive EMD, the different modes correlated with multi-scale information were mined from the PA signal, the correlations between different intrinsic mode function (IMF) modes and BV/TVs were analyzed, and the optimal mode for more efficient PA time-frequency analysis was selected. Second, multi-wavelength HPSA was used to assess the changes in the chemical components of the bone tissue. The results demonstrate that the HPSA method can distinguish bones with different BV/TVs and microstructure conditions adaptively with high efficiency. They further emphasize the potential of PA techniques in characterizing biological tissues in bones for early and rapid detection of bone diseases.

e20008 Background: Radiation pneumonitis (RP) is the most common adverse response in patients with lung cancer receiving thoracic radiotherapy. Higher grade RP is more likely to lead to mortality and poor quality of life, which could be abated by rigorous treatment standards formulated with individualized clinical characteristics. In this study, we aimed to identify the best clinical prognostic model for evaluating patients with lung cancer after radical radiotherapy. Methods: We collected information of RP patients in recent years and established the new consensus on the diagnosis and treatment of radiation-related pneumonitis. In a nutshell, clinical data of patients treated with radical radiotherapy were collected from August 2020 to August 2022, and part of patients from August 2021 to August 2022 was been intervented by treatment methods. The interventions included limited irradiation of the planning target volume (PTV), individualized lung dose limitation, and standardized steroid therapy. Clinical characteristics including baseline and treatment data were obtained from 693 patients, including 623 patients in the training cohort and 70 patients in the test cohort. Three models were built using different screening methods, including multivariate logistics regression (MLR), backward stepwise regression (BSR), and random forest regression (RFR), to evaluate their prognostic power. Overoptimism in the training cohorts was evaluated by four validation methods including hold-out, 10-fold, leave-one-out, and bootstrap methods, and extra data were used to evaluate the predictive performance of the model. Model calibration, Decision curve analysis (DCA), and evaluation of the nomograms for the three models were completed. Results: The incidence of RP was significantly decreased after the interventions compared to before (68.80% vs. 59.56%, P < 0.05), and the probability of grade 3 or higher RP also decreased from 11.97% to 6.67% (P < 0.05). A model of intervention, interstitial lung disease (ILD), concurrent chemoradiotherapy, standardized steroids, carbon monoxide diffusing capacity (DLCO) > 86.9%, and total lung volume exceeding 5 Gy (V5) > 35.2% for the radiological parameter had the best discriminative ability with an area under the curve of 0.963 (95% CI: 0.938–0.989) in the training chort . The calibration curve showed good agreement between the predicted and actual values, and the DCA showed a positive net benefit for the final model based on the nomogram. Conclusions: The implementation of standardized interventions for the prevention and treatment of RP accompanied with standardized use of steroids resulted in a significant decrease in the incidence of grade 3 or higher RP. Early intervention and methods for reducing the complications of high-dose steroids will be explored in future work.

Constrained by the causality nature, it is a challenge to achieve low-frequency broadband efficient absorption via passive materials. By coupling local resonances as many as possible, broadband metamaterial absorbers can be realized. In this scenario, the coupling effect is crucial for improving the absorption efficiency. Here, we demonstrate a kind of acoustic metamaterial absorber capable of high-efficiency broadband absorption by optimizing the non-local coupling effect. Cascade neck-embedded Helmholtz resonator is designed as the subunit of the metamaterial absorber. By coupling 36 subunits, a metamaterial absorber is constructed with the aid of optimization algorithm. The simulation results suggest that strong non-locality is induced in the near field, which effectively suppresses the impedance oscillations and absorption dips at the antiresonance frequencies. Eventually, the presented metamaterial absorber realizes impedance matching to the air from 320 Hz to 6400 Hz, leading to efficient broadband absorption performance. The results and methodologies in this work reveal the significance of non-local coupling effect in coupled-resonant metamaterials.