Photoacoustics最新文献

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.

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 N${}_2$O, this corresponds to a significant improvement both for the achieved normalized noise equivalent absorption coefficient (NNEA) equal to 1.41 × 10${}^{-9}$ cm${}^{-1}$WHz${}^{-1/2}$, and for a Noise-Equivalent-Concentration (NEC) of 1 ppb obtained at 1 s of averaging time.

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.

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 (SO${}_2$) 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 SO${}_2$ levels faithfully with $\ge$95% and $\ge$93% accuracies, respectively in the physiologically relevant hematocrits utilizing PA signals generated at 700 and 1000 nm optical wavelengths. A 2% decrease in the scattering anisotropy factor demotes SO${}_2$ estimation by $\approx$ 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.

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.