Search

Ring-array photoacoustic tomography (PAT) system has been widely used in noninvasive biomedical imaging. However, the reconstructed image usually suffers from spatially rotational blur and streak artifacts due to the non-ideal imaging conditions. To improve the reconstructed image towards higher quality, we propose a concept of spatially rotational convolution to formulate the image blur process, then we build a regularized restoration problem model accordingly and design an alternating minimization algorithm which is called blind spatially rotational deconvolution to achieve the restored image. Besides, we also present an image preprocessing method based on the proposed algorithm to remove the streak artifacts. We take experiments on phantoms and in vivo biological tissues for evaluation, the results show that our approach can significantly enhance the resolution of the image obtained from ring-array PAT system and remove the streak artifacts effectively.

Expansion microscopy (ExM) is a promising technology that enables nanoscale imaging on conventional optical microscopes by physically magnifying the specimens. Here, we report the development of a strategy that enables i) on-demand labeling of subcellular organelles in live cells for ExM through transfection of fluorescent proteins that are well-retained during the expansion procedure; and ii) non-fluorescent chromogenic color-development towards efficient bright-field and photoacoustic imaging in both planar and volumetric formats, which is applicable to both cultured cells and biological tissues. Compared to the conventional ExM methods, our strategy provides an expanded toolkit, which we term as expansion fluorescence and photoacoustic microscopy (ExFLPAM), by allowing on-demand fluorescent protein labeling of cultured cells, as well as non-fluorescent absorption contrast-imaging of biological samples.

Background: The differentiation between benign and malignant breast tumors extends beyond morphological structures to encompass functional alterations within the nodules. The combination of photoacoustic (PA) imaging and radiomics unveils functional insights and intricate details that are imperceptible to the naked eye. Purpose: This study aims to assess the efficacy of PA imaging in breast cancer radiomics, focusing on the impact of peritumoral region size on radiomic model accuracy. Materials and methods: From January 2022 to November 2023, data were collected from 358 patients with breast nodules, diagnosed via PA/US examination and classified as BI-RADS 3–5. The study used the largest lesion dimension in PA images to define the region of interest, expanded by 2 mm, 5 mm, and 8 mm, for extracting radiomic features. Techniques from statistics and machine learning were applied for feature selection, and logistic regression classifiers were used to build radiomic models. These models integrated both intratumoral and peritumoral data, with logistic regressions identifying key predictive features. Results: The developed nomogram, combining 5 mm peritumoral data with intratumoral and clinical features, showed superior diagnostic performance, achieving an AUC of 0.950 in the training cohort and 0.899 in validation. This model outperformed those based solely on clinical features or other radiomic methods, with the 5 mm peritumoral region proving most effective in identifying malignant nodules. Conclusion: This research demonstrates the significant potential of PA imaging in breast cancer radiomics, especially the advantage of integrating 5 mm peritumoral with intratumoral features. This approach not only surpasses models based on clinical data but also underscores the importance of comprehensive radiomic analysis in accurately characterizing breast nodules.

We present a sensitive and compact quantum cascade laser-based photoacoustic greenhouse gas sensor for the detection of CO2, CH4 and CO and discuss its applicability toward on-line real-time trace greenhouse gas analysis. Differential photoacoustic resonators with different dimensions were used and optimized to balance sensitivity with signal saturation. The effects of ambient parameters, gas flow rate, pressure and humidity on the photoacoustic signal and the spectral cross-interference were investigated. Thanks to the combined operation of in-house designed laser control and lock-in amplifier, the gas detection sensitivities achieved were 5.6 ppb for CH4, 0.8 ppb for CO and 17.2 ppb for CO2, signal averaging time 1 s and an excellent dynamic range beyond 6 orders of magnitude. A continuous outdoor five-day test was performed in an observation station in China’s Qinling National Botanical Garden (E longitude 108°29’, N latitude 33°43’) which demonstrated the stability and reliability of the greenhouse gas sensor.

The achievable sensitivity level of photo-acoustic trace-gas sensors essentially depends on the performances of the acoustic transducer. In this work, the mechanical response of different silicon-based micro-electro-mechanical systems (MEMS) is characterized, aiming at investigating both their mechanical properties, namely the resonance frequency and the quality factor, and the minimum detection limit (MDL) achievable when they are exploited as an acoustic-to-voltage transducer in a trace-gas photoacoustic setup. For this purpose, a 4.56µm Continuous-Wave (CW) quantum cascade laser (QCL) is used to excite a strong N2O roto-vibrational transition with a line strength of 2.14 × 10−19 cm/molecule, and the detection of MEMS oscillations is performed via an interferometric readout. As a general trend, the minimum detection limit decreases when the resonance frequency investigated increases, achieving a value of 15 parts per billion with a 3 dB cut-off lock-in bandwidth equal to 100 mHz, around 10 kHz.

Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) is typically assumed in PA image reconstruction, similar to that of ultrasound (US) imaging. Failure to compensate for the SoS variations leads to aberration artefacts, deteriorating the image quality. Various methods have been proposed to address this issue, but they usually involve complex hardware and/or time-consuming algorithms, hindering clinical translation. In this work, we introduce a deep learning framework for SoS estimation and subsequent aberration correction in a dual-modal PA/US imaging system exploiting a clinical US probe. As the acquired PA and US images were inherently co-registered, the estimated SoS distribution from US channel data using a deep neural network was incorporated for accurate PA image reconstruction. The framework comprised an initial pre-training stage based on digital phantoms, which was further enhanced through transfer learning using physical phantom data and associated SoS maps obtained from measurements. This framework achieved a root mean square error of 10.2 m/s and 15.2 m/s for SoS estimation on digital and physical phantoms, respectively and structural similarity index measures of up to 0.88 for PA reconstructions compared to the conventional approach of 0.69. A maximum of 1.2 times improvement in the signal-to-noise ratio of PA images was further demonstrated with a human volunteer study. Our results show that the proposed framework could be valuable in various clinical and preclinical applications to enhance PA image reconstruction.

Quantitative photoacoustic tomography (qPAT) holds great potential in estimating chromophore concentrations, whereas the involved optical inverse problem, aiming to recover absorption coefficient distributions from photoacoustic images, remains challenging. To address this problem, we propose an extractor-attention-predictor network architecture (EAPNet), which employs a contracting–expanding structure to capture contextual information alongside a multilayer perceptron to enhance nonlinear modeling capability. A spatial attention module is introduced to facilitate the utilization of important information. We also use a balanced loss function to prevent network parameter updates from being biased towards specific regions. Our method obtains satisfactory quantitative metrics in simulated and real-world validations. Moreover, it demonstrates superior robustness to target properties and yields reliable results for targets with small size, deep location, or relatively low absorption intensity, indicating its broader applicability. The EAPNet, compared to the conventional UNet, exhibits improved efficiency, which significantly enhances performance while maintaining similar network size and computational complexity.

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.

Spectral photoacoustic imaging in combination with unmixing techniques may be applied to retrieve information about high-risk features present in atherosclerotic plaques, possibly providing prognostic insights into future stroke events. We present the photoacoustic spectral contrast found in 12 systematically scanned advanced atherosclerotic plaques in the near-infrared wavelength range (850–1250 nm). The main absorbers are lipid, water, and hemoglobin, with the highest photoacoustic intensities at the lipid’s second overtone at 1190 and 1210 nm. Linear unmixing resulted in visualizing regions with high lipid and hemoglobin absorption, corresponding to the histological presence of lipid and intraplaque hemorrhage. A non-negative matrix factorization approach reveals differences in lipid spectral contrast, providing potential insights into the vulnerability of atherosclerotic plaque. These results provide a reference for future, more complex, in vivo photoacoustic imaging of carotid artery atherosclerosis, potentially contributing to assessing the risk of future events and treatment decision.

Quartz tuning fork (QTF)-based techniques of photoacoustic spectroscopy and thermoelastic spectroscopy play a significant role in trace gas sensing due to unique high sensitivity and compactness. However, the stability of both techniques remains plagued by the inevitable and unpredictable laser power variation and demodulation phase variation. Herein, we investigate the phase change of a QTF when integrating both techniques for enhanced gas sensing. By demonstrating harmonic phase-sensitive methane detection as an example, we achieve stable gas measurement at varying laser power (2.4–9.4 mW) and varying demodulation phase (−90–90°). Besides, this method shows more tolerance to resonant frequency drift, contributing to a small signal fluctuation of ≤ 6.4 % over a wide modulation range (>10 times of the QTF bandwidth). The realization of harmonic-phase detection allows strengthening the stability of QTF-based sensors in a simple manner, especially when stable parameters, such as laser power, demodulation phase, even resonant frequency, cannot always be maintained.

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.

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 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 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 6.0nsto11.5ns, 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/Hz1/2@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.

Microwave ablation (MWA) is a potent cancer treatment tool, but its effectiveness can be hindered by the lack of visual feedback. This paper validates the feasibility of using microwave-induced thermoacoustic imaging (TAI) technique to monitor the MWA process. A feasibility analysis was conducted at the principle level and a high-performance real-time TAI system was introduced. To address the interference caused by MWA, a robust principal component analysis (RPCA)-based method for TAI was proposed. This method leverages the correlation between multiple signal frames to eliminate interference. RPCA’s effectiveness in TAI was demonstrated through three sets of different experiments. Experiments demonstrated that TAI can effectively monitors the MWA process. This work represents the first application of RPCA-related matrix decomposition methods in TAI, paving the way for the application of TAI in more complex clinical scenarios. By providing rapid and accurate visual feedback, this research advances MWA technology.

Differential photoacoustic spectroscopy (PAS) for flow gas detection based on single microphone is innovatively proposed and experimentally demonstrated. Unlike the traditional systems, only one microphone is used to suppress flowing gas noise. Wavelength modulation spectroscopy and second harmonic detection technique are applied in this PAS system with Q-point demodulation for acetylene (C2H2) gas detection. The experiment is conducted at 1 atm and 300 K. Different concentrations and flow rates of C2H2 from 0 sccm to 225 sccm are detected by using nitrogen (N2) as the carrier gas, which indicates that the system can respond well to flowing gases while maintaining the noise at the same level. The system response time decreases to 3.58 s while the gas velocity is 225 sccm. The detection limit of 43.97 ppb with 1 s integration time and normalized noise equivalent absorption (NNEA) coefficient of 4.0 × 10-9 cm-1 W Hz-1/2 is achieved at the flow rate of 225 sccm. The firstly proposed differential PAS based on single microphone greatly simplifies the system structure for flow gas detection, which provides a novel route for development of PAS with significant practical implementation prospects.

Photoacoustic tomography (PAT) regularly operates in limited-view cases owing to data acquisition limitations. The results using traditional methods in limited-view PAT exhibit distortions and numerous artifacts. Here, a novel limited-view PAT reconstruction strategy that combines model-based iteration with score-based generative model was proposed. By incrementally adding noise to the training samples, prior knowledge can be learned from the complex probability distribution. The acquired prior is then utilized as constraint in model-based iteration. The information of missing views can be gradually compensated by cyclic iteration to achieve high-quality reconstruction. The performance of the proposed method was evaluated with the circular phantom and in vivo experimental data. Experimental results demonstrate the outstanding effectiveness of the proposed method in limited-view cases. Notably, the proposed method exhibits excellent performance in limited-view case of 70° compared with traditional method. It achieves a remarkable improvement of 203% in PSNR and 48% in SSIM for the circular phantom experimental data, and an enhancement of 81% in PSNR and 65% in SSIM for in vivo experimental data, respectively. The proposed method has capability of reconstructing PAT images in extremely limited-view cases, which will further expand the application in clinical scenarios.

Background: Accurate assessment of Rheumatoid Arthritis (RA) activity remains a challenge. Multimodal photoacoustic/ultrasound (PA/US) joint imaging emerges as a novel imaging modality capable of depicting microvascularization and oxygenation levels in inflamed joints associated with RA. However, the scarcity of large-scale studies limits the exploration of correlating joint oxygenation status with disease activity. Objective: This study aimed to explore the correlation between multimodal PA/US imaging scores and RA disease activity, assessing its clinical applicability in managing RA. Methods: In this study, we recruited 111 patients diagnosed with RA and conducted examinations of seven small joints on their clinically dominant side using a PA/US imaging system. The PA and power Doppler ultrasound (PDUS) signals were semi-quantitatively assessed using a 0–3 grading system. The cumulative scores for PA and PDUS across these seven joints (PA-sum and PDUS-sum) were calculated. Relative oxygen saturation (So2) values of inflamed joints on the clinically dominant side were measured, and categorized into four distinct PA+So2 patterns. The correlation between PA/US imaging scores and disease activity indices was systematically evaluated. Results: Analysis of 777 small joints in 111 patients revealed that the PA-sum scores exhibited a strong positive correlation with standard clinical scores for RA, including DAS28 [ESR] (ρ = 0.682), DAS28 [CRP] (ρ = 0.683), CDAI (ρ = 0.738), and SDAI (ρ = 0.739), all with p < 0.001. These correlations were superior to those of the PDUS-sum scores (DAS28 [ESR] ρ = 0.559, DAS28 [CRP] ρ = 0.555, CDAI ρ = 0.575, SDAI ρ = 0.581, p < 0.001). Significantly, in patients with higher PA-sum scores, notable differences were observed in the erythrocyte sedimentation rate (ESR) (p < 0.01) and swollen joint count 28 (SJC28) (p < 0.01) between hypoxia and intermediate groups. Notably, RA patients in the hypoxia group exhibited higher clinical scores in certain clinical indices. Conclusion: Multi-modal PA/US imaging introduces potential advancements in RA assessment, especially regarding So2 evaluations in synovial tissues and associated PA scores. However, further studies are warranted, particularly with more substantial sample sizes and in multi-center settings. Summary: This study utilized multi-modal PA/US imaging to analyze Rheumatoid Arthritis (RA) patients' synovial tissues and affected joints. When juxtaposed with traditional PDUS imaging, the PA approach demonstrated enhanced sensitivity, especially concerning detecting small vessels in thickened synovium and inflamed tendon sheaths. Furthermore, correlations between the derived PA scores, PA+So2 patterns, and standard clinical RA scores were observed. These findings suggest that multi-modal PA/US imaging could be a valuable tool in the comprehensive assessment of RA, offering insights not only into disease activity but also into the oxygenation status of synovial tissues. However, as promising as these results are, further investigations, especially in larger and diverse patient populations, are imperative. Key points: ⸸ Multi-modal PA/US Imaging in RA: This novel technique was used to assess the So2 values in synovial tissues and determine PA scores of affected RA joints.⸸ Correlation significantly with Clinical RA Scores: Correlations significantly were noted between PA scores, PA+So2 patterns, and standard clinical RA metrics, hinting at the potential clinical applicability of the technique.

Strain-induced variation of the refractive index is the main mechanism of strain detection in photoacoustic experiments. However, weak strain-optic coupling in many materials limits the application of photoacoustics as an imaging tool. A straightforward deposition of a transparent thin film as a top layer has previously been shown to provide signal enhancement due to elastic boundary effects. In this paper, we study photoacoustic signal formation in metal covered by thin transparent films of different thicknesses and demonstrate that in addition to boundary effects, the photoacoustic response is affected by optical effects caused by the presence of the top layer. The interplay of optical effects leads to a complex temporal signal shape that strongly depends on the thickness of the thin film.

This study highlights the potential of scanning optoacoustic angiography (OA) in identifying alterations of superficial vasculature in patients with post-thrombotic syndrome (PTS) of the foot, a venous stress disorder associated with significant morbidity developing from long-term effects of deep venous thrombosis. The traditional angiography methods available in the clinics are not capable of reliably assessing the state of peripheral veins that provide blood outflow from the skin, a key hallmark of personalized risks of PTS formation after venous thrombosis. Our findings indicate that OA can detect an increase in blood volume, diameter, and tortuosity of superficial blood vessels. The inability to spatially separate vascular plexuses of the dermis and subcutaneous adipose tissue serves as a crucial criterion for distinguishing PTS from normal vasculature. Furthermore, our study demonstrates the ability of scanning optoacoustic angiography to detect blood filling decrease in an elevated limb position versus increase in a lowered position.

Microscopic defects in flip chips, originating from manufacturing, significantly affect performance and longevity. Post-fabrication sampling methods ensure product functionality but lack in-line defect monitoring to enhance chip yield and lifespan in real-time. This study introduces a photoacoustic remote sensing (PARS) system for in-line imaging and defect recognition during flip-chip fabrication. We first propose a real-time PARS imaging method based on continuous acquisition combined with parallel processing image reconstruction to achieve real-time imaging during the scanning of flip-chip samples, reducing reconstruction time from an average of approximately 1134 ms to 38 ms. Subsequently, we propose improved YOLOv7 with space-to-depth block (IYOLOv7-SPD), an enhanced deep learning defect recognition method, for accurate in-line recognition and localization of microscopic defects during the PARS real-time imaging process. The experimental results validate the viability of the proposed system for enhancing the lifespan and yield of flip-chip products in chip manufacturing facilities.

Psoriasis is a chronic inflammatory skin disease, characterized by thick scaly plaques. It imposes a notable disease burden with varying levels of severity affecting the quality of life significantly. Current disease severity assessment relies on semi-objective visual inspection based on the Psoriasis Area and Severity index (PASI) score that might not be sensitive to sub-clinical changes. Histology of psoriasis skin lesions necessitate invasive skin biopsies. This indicates an unmet need for a non-invasive, objective and quantitative approach to assess disease severity serially. Herein, we employ multispectral Raster-Scanning Optoacoustic Mesoscopy (ms-RSOM) derived structural and microvascular functional imaging metrics to examine the lesional and non-lesional skin in psoriasis subjects across different severities and also evaluate the treatment outcome in a subject with topical steroids and biologics, such as adalimumab. ms-RSOM derived structural metrics like epidermal thickness and total blood volume (TBV) and microvascular functional information such as oxygen saturation (sO2) are evaluated by spectrally resolving the endogenous chromophores like melanin, oxy-, and deoxy-hemoglobin. Initial findings reveal an elevated sO2 and TBV with severity in lesional and non-lesional psoriasis skin, thus representing increasing inflammation. An increase in epidermal thickness is also noted with the degree of severity, corresponding to the inflammation and increased abnormal cell growth. As a marker to evaluate the treatment response, we observed a decrease in epidermal thickness, sO2, and TBV in a psoriasis patient post-treatment, which is consistent with the decrease in the PASI score from 4.1 to 1.9. We envision that ms-RSOM has a huge potential to be translated into routine clinical setting for the diagnosis of severity and assessment of treatment monitoring in psoriasis subjects.

Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN), named PAT-ADN, was proposed to address the issue. This network is equipped with specialized encoders and decoders that are responsible for encoding and decoding the artifacts and content components of unpaired images, respectively. The performance of the proposed PAT-ADN was evaluated using circular phantom data and the animal in vivo experimental data. The results demonstrate that PAT-ADN exhibits excellent performance in effectively removing artifacts. In particular, under extremely sparse view (e.g., 16 projections), structural similarity index and peak signal-to-noise ratio are improved by ∼188 % and ∼85 % in in vivo experimental data using the proposed method compared to traditional reconstruction methods. PAT-ADN improves the imaging performance of PAT, opening up possibilities for its application in multiple domains.

A miniaturized photoacoustic spectroscopy-based gas sensor is proposed for the purpose of detecting sub-ppm-level carbonyl sulfide (OCS) using a tunable mid-infrared interband cascade laser (ICL) and a Helmholtz photoacoustic cell. The tuning characteristics of the tunable ICL with a center wavelength of 4823.3 nm were investigated to achieve the optimal driving parameters. A Helmholtz photoacoustic cell with a volume of ∼2.45 mL was designed and optimized to miniaturize the measurement system. By optimizing the modulation parameters and signal processing, the system was verified to have a good linear response to OCS concentration. With a lock-in amplifier integration time of 10 s, the 1σ noise standard deviation in differential mode was 0.84 mV and a minimum detection limit (MDL) of 409.2 ppbV was achieved at atmospheric pressure and room temperature.

Photoacoustic microscopy (PAM) has gained increasing popularity in biomedical imaging, providing new opportunities for tissue monitoring and characterization. With the development of deep learning techniques, convolutional neural networks have been used for PAM image resolution enhancement and denoising. However, there exist several inherent challenges for this approach. This work presents a Unified PhotoAcoustic Microscopy image reconstruction Network (UPAMNet) for both PAM image super-resolution and denoising. The proposed method takes advantage of deep image priors by incorporating three effective attention-based modules and a mixed training constraint at both pixel and perception levels. The generalization ability of the model is evaluated in details and experimental results on different PAM datasets demonstrate the superior performance of the method. Experimental results show improvements of 0.59 dB and 1.37 dB, respectively, for 1/4 and 1/16 sparse image reconstruction, and 3.9 dB for image denoising in peak signal-to-noise ratio.

We present a technique called photoacoustic vector-flow (PAVF) to quantify the speed and direction of flowing optical absorbers at each pixel from acoustic-resolution PA images. By varying the receiving angle at each pixel in post-processing, we obtain multiple estimates of the phase difference between consecutive frames. These are used to solve the overdetermined photoacoustic Doppler equation with a least-squares approach to estimate a velocity vector at each pixel. This technique is tested in bench-top experiments and compared to simultaneous pulse-echo ultrasound vector-flow (USVF) on whole rat blood at speeds on the order of 1 mm/s. Unlike USVF, PAVF can detect flow without stationary clutter filtering in this experiment, although the velocity estimates are highly underestimated. When applying spatio-temporal singular value decomposition clutter filtering, the flow speed can be accurately estimated with an error of 16.8% for USVF and −8.9% for PAVF for an average flow speed of 2.5 mm/s.

In this research we present a low-cost system for breath acetone analysis based on UV-LED photoacoustic spectroscopy. We considered the end-tidal phase of exhalation, which represents the systemic concentrations of volatile organic compounds (VOCs) – providing clinically relevant information about the human health. This is achieved via the development of a CO2-triggered breath sampling system, which collected alveolar breath over several minutes in sterile and inert containers. A real-time mass spectrometer is coupled to serve as a reference device for calibration measurements and subsequent breath analysis. The new sensor system provided a 3σ detection limit of 8.3 ppbV and an NNEA of 1.4E-9 Wcm−1Hz−0.5. In terms of the performed breath analysis measurements, 12 out of 13 fell within the error margin of the photoacoustic measurement system, demonstrating the reliability of the measurements in the field.

Dual-comb photoacoustic spectroscopy (DC-PAS) advances spectral measurements by offering high-sensitivity and compact size in a wavelength-independent manner. Here, we present a novel cantilever-enhanced DC-PAS scheme, employing a high-sensitivity fiber-optic acoustic sensor based on an optical cantilever and a non-resonant photoacoustic cell (PAC) featuring a flat-response characteristic. The dual comb is down-converted to the audio frequency range, and the resulting multiheterodyne sound waves from the photoacoustic effect, are mapped into the response frequency region of the optical cantilever microphone. This cantilever-enhanced DC-PAS method provides advantages such as high sensitivity, compact design, and immunity to electromagnetic interference. Through 10 seconds averaging time, the proposed approach experimentally achieved a minimum detection limit of 860 ppb for acetylene. This technology presents outstanding opportunities for highly sensitive detection of trace gases in a wavelength-independent manner, all within a compact volume.

Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level.

Nowadays, the scientific community and industry are increasingly pressed to provide solutions for developing compact and highly-performing trace-gas sensors for several applications of crucial importance, such as environmental monitoring or medical diagnostics. In this context, this work describes a novel configuration, making use of a mid-IR spectrophone combining the compactness of a photo-acoustic setup, a non-conventional micro-electro-mechanical (MEMS) acousto-to-voltage transducer, and the sensitivity enhancement given by a cost-effective and easy-to-build dual-tube resonator configuration. In the optimal condition of sample pressure, the system developed in this work can achieve a minimum detection limit (MDL) equal to 0.34 ppb when averaging up to 10 s. Compared with previous literature of single-pass photoacoustic-based sensors for N2O, this corresponds to a significant improvement both for the achieved normalized noise equivalent absorption coefficient (NNEA) equal to 1.41 × 10−9 cm−1WHz−1/2, and for a Noise-Equivalent-Concentration (NEC) of 1 ppb obtained at 1 s of averaging time.

A novel method is introduced to improve the detection performance of photoacoustic spectroscopy for trace gas detection. For effectively suppressing various types of noise, this method integrates photoacoustic spectroscopy with residual networks model which encompasses a total of 40 weighted layers. Firstly, this approach was employed to accurately retrieve methane concentrations at various levels. Secondly, the analysis of the signal-to-noise ratio (SNR) of multiple sets of photoacoustic spectroscopy signals revealed significant enhancement. The SNR was improved from 21 to 805, 52–962, 98–944, 188–933, 310–941, and 587–936 across the different concentrations, respectively, as a result of the application of the residual networks. Finally, further exploration for the measurement precision and stability of photoacoustic spectroscopy system utilizing residual networks was carried out. The measurement precision of 0.0626 ppm was obtained and the minimum detectable limit was found to be 1.47 ppb. Compared to traditional photoacoustic spectroscopy method, an approximately 46-fold improvement in detection limit and 69-fold enhancement in measurement precision were achieved, respectively. This method not only advances the measurement precision and stability of trace gas detection but also highlights the potential of deep learning algorithms in spectroscopy detection.

Vaccine development requires high-resolution, in situ, and visual adjuvant technology. To address this need, this work proposed a novel adjuvant labeling that involved indocyanine green (ICG) and bovine serum albumin (BSA) with self-assembled aluminium adjuvant (Alum), which was called BSA@ICG@Alum. This compound exhibited excellent photoacoustic properties and has been confirmed its safety, biocompatibility, high antigen binding efficiency, and superior induction of immune response. Photoacoustic tomography (PAT) tracked the distribution of Alum in lymph nodes (LNs) and lymphatic vessels in real time after diverse injection modalities. The non-invasive imaging approach revealed that BSA@ICG@Alum was transported to the draining LNs 60 min after intramuscular injection and to distal LNs within 30 min after lymph node injection. In conclusion, PAT enabled real-time three-dimensional and quantitative visualization, thus offering a powerful tool for advancing vaccine design by providing critical insights into adjuvant transport and immune system activation.

Multispectral photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect to achieve non-invasive and high-contrast imaging of internal tissues but also molecular functional information derived from multi-spectral measurements. However, the hardware cost and computational demand of a multispectral PAT system consisting of up to thousands of detectors are huge. To address this challenge, we propose an ultra-sparse spiral sampling strategy for multispectral PAT, which we named U3S-PAT. Our strategy employs a sparse ring-shaped transducer that, when switching excitation wavelengths, simultaneously rotates and translates. This creates a spiral scanning pattern with multispectral angle-interlaced sampling. To solve the highly ill-conditioned image reconstruction problem, we propose a self-supervised learning method that is able to introduce structural information shared during spiral scanning. We simulate the proposed U3S-PAT method on a commercial PAT system and conduct in vivo animal experiments to verify its performance. The results show that even with a sparse sampling rate as low as 1/30, our U3S-PAT strategy achieves similar reconstruction and spectral unmixing accuracy as non-spiral dense sampling. Given its ability to dramatically reduce the time required for three-dimensional multispectral scanning, our U3S-PAT strategy has the potential to perform volumetric molecular imaging of dynamic biological activities.

Assessing the blood hematocrit (Hct) and oxygenation (SO2) levels are essential for diagnosing numerous blood-related diseases. This study examines the ability of the photoacoustic (PA) technique for quantitative evaluation of these parameters. We conducted the Monte Carlo and k-Wave simulations to compute PA signals at four different optical wavelengths from test blood samples followed by rigorous in vitro experiments. This method can estimate the Hct and SO2 levels faithfully with ≥95% and ≥93% accuracies, respectively in the physiologically relevant hematocrits utilizing PA signals generated at 700 and 1000 nm optical wavelengths. A 2% decrease in the scattering anisotropy factor demotes SO2 estimation by ≈ 27%. This study provides sufficient insight into how the opto-chemical parameters of blood impact PA emission and may help to develop a PA setup for in vitro characterization of human blood.

Photoacoustic microscopy offers functional information regarding tissue vasculature while ultrasound characterizes tissue structure. Combining these two modalities provides novel clinical applications including response assessment among rectal cancer patients undergoing therapy. We have previously demonstrated the capabilities of a co-registered photoacoustic and ultrasound device in vivo, but multiple challenges limited broad adoption. In this paper, we report significant improvements in an acoustic resolution photoacoustic microscopy and ultrasound (ARPAM/US) system characterized by simulation and phantom study, focusing on resolution, optical coupling, and signal characteristics. In turn, higher in-probe optical coupling efficiency, higher signal-to-noise ratio, higher data throughput, and better stability with minimal maintenance requirements were all accomplished. We applied the system to 19 ex vivo resected colorectal cancer samples and found significantly different signals between normal, cancer, and post-treatment tumor tissues. Finally, we report initial results of the first in vivo imaging study.

Hydrocarbon gas sensing is a challenging task using laser absorption spectroscopy due to the complex and broad structure of absorption lines. This application requires quick, accurate and highly sensitive detection of hydrocarbon gases concentrations. In this paper, a compact photoacoustic spectrophone was developed to simultaneously measure methane, propane and isobutane. This spectrophone uses wavelength modulation spectroscopy (WMS) with a single acoustic resonator and a single DFB laser emitting at 3368 nm, which greatly reduces the system complexity without using time-division multiplexing technology for multi-gas sensing. Due to the complex and broadband absorption of hydrocarbon gases, a novel signal processing method based on multilinear regression with Ridge regression (MLR-RG) is proposed to reduce the measurement error caused by the nonlinearity of spectra signal. For single gas measurement, the detection limits of methane, propane, and isobutane are determined to be 828 ppb, 419 ppb, and 619 ppb (SNR = 1, integration time = 20 s), respectively. For simultaneous multi-gas sensing in a gaseous mixture, the detection limits of propane and isobutane are determined to be 7 ppb, 68 ppb with an integration time of 860 s, 460 s, respectively. The measurement accuracy of propane and isobutane using MLR-RG is higher than that of ordinary least squares regression and partial least squares regression by 75% and 60%, respectively. The proposed algorithm based on MLR-RG provides a promising approach to process the broad overlapping absorption spectra for accurately retrieving hydrocarbon gases concentrations.

A proof-of-concept on-beam tuning-fork-enhanced photoacoustic sensor based on an open-closed single-tube acoustic-microresonator (AmR) was proposed and investigated for the first time, to the best of our knowledge. Due to the high acoustic amplification effect, the open-closed AmR improved the detection sensitivity by 54 times with respect to the bare tuning fork (TF). Compared to traditional dual-tube/single-tube on-beam spectrophone configuration, the developed approach significantly facilitates the laser beam alignment and reduces the sensor size and gas consumption. A 6.6 kHz low-frequency custom aluminum alloy TF was employed as the acoustic transducer to detect the photoacoustic signal. The vibration of TF was measured by a fiber-optic Fabry-Pérot (FP) interferometer (FPI). The modulation depth, tube length and laser power were experimentally optimized and evaluated in detail. An acetylene (C2H2) 1σ minimum detection limit (MDL) of 6.3 ppb was obtained with a high laser power of ∼ 500 mW, corresponding to a normalized noise equivalent absorption (NNEA) coefficient of 6.5 × 10−9 cm−1 W/Hz1/2. The compact spectrophone size and all-fiber measurement method can make the PAS-based sensor have great application prospects in dissolved gases detection in transformer oil, remote gas detection, space-limited gas detection, and etc.

Metallurgical defects in metal laser additive manufacturing (LAM) are inevitable due to complex non-equilibrium thermodynamics. A laser ultrasonic system was designed for detecting surface/near-surface defects in the layer-by-layer LAM process. An approach was proposed for ultrasonic imaging of defects based on variable time window intensity mapping with adaptive 2σ threshold denoising. The Gaussian mixture model hypothesis and expectation-maximization algorithm can automatically differentiate between components dominated by defects and background noises, thereby providing an adaptive threshold that accommodates detection environments and surface roughness levels. Results show that the ultrasonic wave reflection at defect boundaries diminishes far-field ultrasonic intensity upon pulsed laser irradiation on surface defects, enabling defect size and location characterization. This method is applicable to LAM samples with a significant surface roughness of up to 37.5 μm. It can detect superficial and near-surface defects down to 0.5 mm in diameter and depth, making it significant for online defect detection in additive manufacturing.

The unique advantage of optical-resolution photoacoustic microscopy (OR-PAM) is its ability to achieve high-resolution microvascular imaging without exogenous agents. This ability has excellent potential in the study of tissue microcirculation. However, tracing and monitoring microvascular morphology and hemodynamics in tissues is challenging because the segmentation of microvascular in OR-PAM images is complex due to the high density, structure complexity, and low contrast of vascular structures. Various microvasculature extraction techniques have been developed over the years but have many limitations: they cannot consider both thick and thin blood vessel segmentation simultaneously, they cannot address incompleteness and discontinuity in microvasculature, there is a lack of open-access datasets for DL-based algorithms. We have developed a novel segmentation approach to extract vascularity in OR-PAM images using a deep learning network incorporating a weak signal attention mechanism and multi-scale perception (WSA-MP-Net) model. The proposed WSA network focuses on weak and tiny vessels, while the MP module extracts features from different vessel sizes. In addition, Hessian-matrix enhancement is incorporated into the pre-and post-processing of the input and output data of the network to enhance vessel continuity. We constructed normal vessel (NV-ORPAM, 660 data pairs) and tumor vessel (TV-ORPAM, 1168 data pairs) datasets to verify the performance of the proposed method. We developed a semi-automatic annotation algorithm to obtain the ground truth for our network optimization. We applied our optimized model successfully to monitor glioma angiogenesis in mouse brains, thus demonstrating the feasibility and excellent generalization ability of our model. Compared to previous works, our proposed WSA-MP-Net extracts a significant number of microvascular while maintaining vessel continuity and signal fidelity. In quantitative analysis, the indicator values of our method improved by about 1.3% to 25.9%. We believe our proposed approach provides a promising way to extract a complete and continuous microvascular network of OR-PAM and enables its use in many microvascular-related biological studies and medical diagnoses.

Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue. However, the accuracy of PAT imaging is compromised by light fluence (LF), which hinders the quantification of light absorbers. Currently, model-based iterative methods are used for LF correction, but they require extensive computational resources due to repeated LF estimation based on differential light transport models. To improve LF correction efficiency, we propose to use Fourier neural operator (FNO), a neural network specially designed for estimating partial differential equations, to learn the forward projection of light transport in PAT. Trained using paired finite-element-based LF simulation data, our FNO model replaces the traditional computational heavy LF estimator during iterative correction, such that the correction procedure is considerably accelerated. Simulation and experimental results demonstrate that our method achieves comparable LF correction quality to traditional iterative methods while reducing the correction time by over 30 times.

Introduction: In this study, we utilized the pulsed photoacoustic (PA) technique to analyze globular sedimentation in whole human blood, with a focus on distinguishing between healthy individuals and those with hemolytic anemia. Methods: Blood samples were collected from both healthy individuals (women and men) and those with hemolytic anemia, and temporal and spectral parameters of PA signals were employed for analysis. Results: Significant differences (p < 0.05) were observed in PA metrics between the two groups. The proposed spectral analysis allowed significant differentiation within a 25-minute measurement window. Anemic blood samples exhibited higher erythrocyte sedimentation rate (ESR) values, indicating increased erythrocyte aggregation. Discussion: This study underscores the potential of PA signal analysis in ESR assessment as an efficient method for distinguishing between healthy and anemic blood, surpassing traditional approaches. It represents a promising contribution to the development of precise and sensitive techniques for analyzing human blood samples in clinical settings.

Photoacoustic imaging creates light-induced ultrasonic signals to provide valuable information on internal body structures and tissue morphology non-invasively. A multi-aperture photoacoustic imaging (MP-PAI) system is an improvement over conventional photoacoustic imaging (PAI) systems in terms of resolution, contrast, and field of view. Previously, a prototype MP-PAI system was introduced based on multiple capacitive micromachined ultrasound transducers (CMUTs) with shared channels, such that each element in a CMUT shares its channel with its counterpart in other CMUTs. The system uses the biasing voltages of the CMUTs to switch between them and multiplex the received signals in time. Notwithstanding all the enhancements, the signal-to-noise ratio (SNR) remains limited in PAI. To address this issue, we are proposing a multi-aperture encoding scheme (MAES) to further increase the SNR in a multi-aperture PAI system. The proposed method involves receiving signals with multiple CMUTs simultaneously based on an encoding matrix, instead of switching between individual CMUTs. As a result, shared channels contain a superposition of signals, which are later recovered by applying a decoding matrix. Here, an analytical model for computing SNR with an arbitrary encoding sequence is presented, and the method is validated through numerical simulations and in an experimental study. Bipolar and unipolar encoding sequences were considered for the experiments. The numerical results show, in comparison to conventional MP-PAI, that MAES will obtain an SNR gain of 5.8 and 8.8 dB for S-sequence and truncated Hadamard encodings, respectively, when using 15 transducers. In experiments, three transducers are encoded by S-sequences and show 1.5 dB improvement in SNR over conventional MP-PAI method, which aligns well with the analytical model.

Real-time applications in three-dimensional photoacoustic tomography from planar sensors rely on fast reconstruction algorithms that assume the speed of sound (SoS) in the tissue is homogeneous. Moreover, the reconstruction quality depends on the correct choice for the constant SoS. In this study, we discuss the possibility of ameliorating the problem of unknown or heterogeneous SoS distributions by using learned reconstruction methods. This can be done by modelling the uncertainties in the training data. In addition, a correction term can be included in the learned reconstruction method. We investigate the influence of both and while a learned correction component can improve reconstruction quality further, we show that a careful choice of uncertainties in the training data is the primary factor to overcome unknown SoS. We support our findings with simulated and in vivo measurements in 3D.