Photoacoustics最新文献

A high-sensitivity photoacoustic spectroscopy (PAS) sensor based on differential Helmholtz photoacoustic cell (DHPAC) with dense spot pattern is reported in this paper for the first time. A multi-pass cell based on two concave mirrors was designed to achieve a dense spot pattern, which realized 212 times excitation of incident laser. A finite element analysis was utilized to simulate the sound field distribution and frequency response of the designed DHPAC. An erbium-doped fiber amplifier (EDFA) was employed to amplify the output optical power of the laser to achieve strong excitation. In order to assess the designed sensor's performance, an acetylene (C₂H₂) detection system was established using a near infrared diode laser with a central wavelength 1530.3 nm. According to experimental results, the differential characteristics of DHPAC was verified. Compared to the sensor without dense spot pattern, the photoacoustic signal with dense spot pattern had a 44.73 times improvement. The minimum detection limit (MDL) of the designed C₂H₂-PAS sensor can be improved to 5 ppb when the average time of the sensor system is 200 s.

A fast scanner of optical-resolution photoacoustic microscopy is inherently vulnerable to perturbation, leading to severe image distortion and significant misalignment among multiple 2D or 3D images. Restoration and registration of these images is critical for accurately quantifying dynamic information in long-term imaging. However, traditional registration algorithms face a great challenge in computational throughput. Here, we develop an unsupervised deep learning based registration network to achieve real-time image restoration and registration. This method can correct artifacts from B-scan distortion and remove misalignment among adjacent and repetitive images in real time. Compared with conventional intensity based registration algorithms, the throughput of the developed algorithm is improved by 50 times. After training, the new deep learning method performs better than conventional feature based image registration algorithms. The results show that the proposed method can accurately restore and register the images of fast-scanning photoacoustic microscopy in real time, offering a powerful tool to extract dynamic vascular structural and functional information.

We proposed a non-contact photoacoustic (PA) detection method using spectral domain optical coherence tomography (SDOCT). Two interference spectrums (A-lines) were acquired before and after the PA excitation with SDOCT. PA signal propagated within the sample causing the vibration. The vibration inner the sample introduced phase change between the acquired two A-lines. Thus, the PA signal can be detected by evaluating the difference in phase between the two A-lines. Based on the method, an OCT-PAM dual-mode imaging system was constructed. In the system, SDOCT served as the detection unit for PAM. Thus, the combination of the two imaging modalities was simplified. Another advantage of the system is that it realizes non-contact all-optic detection, which is attractive for biomedical imaging. Using the system, we imaged phantoms of carbon fibers, asparagus leaves and human hairs. Furthermore, the cortical vasculature of rat was imaged in vivo and the flow status was evaluated quantitatively.

Microcirculatory dysfunction has been observed in the dermal white adipose tissue (dWAT) and subcutaneous white adipose tissue (scWAT) of obese humans and has been proposed as an early prediction marker for cardio-metabolic disease progression. In-vivo visualization and longitudinal monitoring of microvascular remodeling in these tissues remains challenging. We compare the performance of two optoacoustic imaging methods, i.e. multi-spectral optoacoustic tomography (MSOT) and raster-scanning optoacoustic mesoscopy (RSOM) in visualizing lipid and hemoglobin contrast in scWAT and dWAT in a mouse model of diet-induced obesity (DIO) undergoing voluntary wheel running intervention for 32 weeks. MSOT visualized lipid and hemoglobin contrast in murine fat depots in a quantitative manner even at early stages of DIO. We show for the first time to our knowledge that RSOM allows precise visualization of the dWAT microvasculature and provides quantitative readouts of skin layer thickness and vascular density in dWAT and dermis. Combination of MSOT and RSOM resolved exercise-induced morphological changes in microvasculature density, tissue oxygen saturation, lipid and blood volume content in dWAT and scWAT. The combination of MSOT and RSOM may allow precise monitoring of microcirculatory dysfunction and intervention response in dWAT and scWAT in a mouse model for DIO. Our findings have laid out the foundation for future clinical studies using optoacoustic-derived vascular readouts from adipose tissues as a biomarker for monitoring microcirculatory function in metabolic disease.

Hydrogen cyanide (HCN) is a toxic industrial chemical, necessitating low-level detection capabilities for safety and environmental monitoring. This study introduces a novel approach for detecting hydrogen cyanide (HCN) using a clamp-type custom quartz tuning fork (QTF) integrated with a dual-tube acoustic micro-resonator (AmR) for enhanced photoacoustic gas sensing. The design and optimization of the AmR geometry were guided by theoretical simulation and experimental validation, resulting in a robust on-beam QEPAS (Quartz-Enhanced Photoacoustic Spectroscopy) configuration. To boost the QEPAS sensitivity, an Erbium-Doped Fiber Amplifier (EDFA) was incorporated, amplifying the laser power by approximately 286 times. Additionally, a transformer-based U-shaped neural network, a machine learning filter, was employed to refine the photoacoustic signal and reduce background noise effectively. This combination yielded a significantly low detection limit for HCN at 0.89 parts per billion (ppb) with a rapid response time of 1 second, marking a substantial advancement in optical gas sensing technologies. Key modifications to the QTF and innovative use of AmR lengths were validated under various experimental conditions, affirming the system's capabilities for real-time, high-sensitivity environmental monitoring and industrial safety applications. This work not only demonstrates significant enhancements in QEPAS but also highlights the potential for further technological advancements in portable gas detection systems.

A comprehensive understanding of a tumor is required for accurate diagnosis and effective treatment. However, currently, there is no single imaging modality that can provide sufficient information. Photoacoustic (PA) imaging is a hybrid imaging technique with high spatial resolution and detection sensitivity, which can be combined with ultrasound (US) imaging to provide both optical and acoustic contrast. Elastography can noninvasively map the elasticity distribution of biological tissue, which reflects pathological conditions. In this study, we incorporated PA elastography into a commercial US/PA imaging system to develop a tri-modality imaging system, which has been tested for tumor detection using four mice with different physiological conditions. The results show that this tri-modality imaging system can provide complementary information on acoustic, optical, and mechanical properties. The enabled visualization and dimension estimation of tumors can lead to a more comprehensive tissue characterization for diagnosis and treatment.

This study reports an imaging method for gigahertz surface acoustic waves in transparent layers using infrared subpicosecond laser pulses in the ablation regime and an optical pump–probe technique. The reflectivity modulations due to the photoelastic effect of generated multimodal surface acoustic waves were imaged by an sCMOS camera illuminated by the time-delayed, frequency-doubled probe pulses. Moving the delay time between $6.0ns\text{to}11.5ns$, image stacks of wave field propagation were created.

Two representative samples were investigated: wafers of isotropic fused silica and anisotropic x-cut quartz. Rayleigh (SAW) and longitudinal dominant high-velocity pseudo-surface acoustic wave (HVPSAW) modes could be observed and tracked along a circular grid around the excitation center, allowing the extraction of angular profiles of the propagation velocity. In quartz, the folding of a PSAW was observed. A finite element simulation was developed to predict the measurement results. The simulation and measurement were in good agreement with a relative error of 2 % to 5 %.

These results show the potential for fast and full-field imaging of laser-generated ultrasonic surface wave modes, which can be utilized for the characterization of thin transparent samples such as semiconductor wafers or optical crystals in the gigahertz frequency range.

Here we present a computational and experimental fluid dynamics study for the characterization of the flow field within the gas chamber of a Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) sensor, at different flow rates at the inlet of the chamber. The transition from laminar to turbulent regime is ruled both by the inlet flow conditions and dimension of the gas chamber. The study shows how the distribution of the flow field in the chamber can influence the QEPAS sensor sensitivity, at different operating pressures. When turbulences and eddies are generated within the gas chamber, the efficiency of photoacoustic generation is significantly altered.

Photoacoustic spectroscopy (PAS) has been rapidly developed and applied to different detection scenarios. The acoustic pressure detection is an important part in the PAS system. In this paper, an ultrahigh sensitivity Fabry-Perot acoustic sensor with a T-shaped cantilever was proposed. To achieve the best acoustic pressure effect, the dimension of the cantilever structure was designed and optimized by finite element analysis using COMSOL Multiphysics. Simulation results showed that the sensitivity of such T-shaped cantilever was 1.5 times higher than that based on a rectangular cantilever, and the resonance frequency of T-shaped cantilever were able to modulate from 800 Hz to 1500 Hz by adjusting the multi-parameter characteristics. Experimental sensing results showed that the resonance frequency of T-shaped Fabry-Perot acoustic sensor was 1080 Hz, yielding a high sensitivity of 1.428 μm/Pa, with a signal-to-noise ratio (SNR) of 84.8 dB and a detectable pressure limit of 1.9 μPa/Hz^1/2@1 kHz. We successfully used such acoustic sensor to measure acetylene (C₂H₂) concentration in the PAS. The sensitivity of PAS for C₂H₂ gas was 3.22 pm/ppm with a concentration range of 50 ppm ∼100 ppm, and the minimum detection limit was 24.91ppb.

Photoacoustic tomography (PAT), as a novel medical imaging technology, provides structural, functional, and metabolism information of biological tissue in vivo. Sparse Sampling PAT, or SS-PAT, generates images with a smaller number of detectors, yet its image reconstruction is inherently ill-posed. Model-based methods are the state-of-the-art method for SS-PAT image reconstruction, but they require design of complex handcrafted prior. Owing to their ability to derive robust prior from labeled datasets, deep-learning-based methods have achieved great success in solving inverse problems, yet their interpretability is poor. Herein, we propose a novel SS-PAT image reconstruction method based on deep algorithm unrolling (DAU), which integrates the advantages of model-based and deep-learning-based methods. We firstly provide a thorough analysis of DAU for PAT reconstruction. Then, in order to incorporate the structural prior constraint, we propose a nested DAU framework based on plug-and-play Alternating Direction Method of Multipliers (PnP-ADMM) to deal with the sparse sampling problem. Experimental results on numerical simulation, in vivo animal imaging, and multispectral un-mixing demonstrate that the proposed DAU image reconstruction framework outperforms state-of-the-art model-based and deep-learning-based methods.