Time-resolved Photoacoustic Signals Research Articles

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.

Versatile multimodal modality based on Brillouin light scattering and the photoacoustic effect.

Multimodal optical techniques are useful for the comprehensive characterization of material properties. In this work, we developed a new, to the best of our knowledge, multimodal technology that can simultaneously measure a subset of mechanical, optical, and acoustical properties of the sample and is based on the integration of Brillouin (Br) and photoacoustic (PA) microscopy. The proposed technique can acquire co-registered Br and PA signals from the sample. Importantly, using synergistic measurements of the speed of sound and Brillouin shift, the modality offers a new approach to quantifying the optical refractive index, which is a fundamental property of a material and is not accessible by either technique individually. As a proof of concept, we demonstrated the feasibility of integrating the two modalities and acquired the colocalized Br and time-resolved PA signals in a synthetic phantom made out of kerosene and CuSO4 aqueous solution. In addition, we measured the refractive index values of saline solutions and validated the result. Comparison with previously reported data showed a relative error of 0.3%. This further allowed us to directly quantify the longitudinal modulus of the sample with the colocalized Brillouin shift. While the scope of the current work is limited to introducing the combined Br-PA setup for the first time, we envision that this multimodal modality could open a new path for the multi-parametric analysis of material properties.

Photoacoustic identification of blood authenticity based on quantum-behaved particle swarm optimized wavelet neural network.

To accurately identify the blood authenticity, a set of photoacoustic detection system was established. In experiments, five kinds of blood in total of 125 groups were used, the time-resolved photoacoustic signals and peak-to-peak spectra were obtained in 700 to 1064 nm. Experimental results showed the accurate identification of blood authenticity was limited due to overlap of signals and spectra. To solve the problem, wavelet neural network (WNN) was employed to supervised train peak-to-peak spectra of 100 samples. The correct rate was 72% for 25 test samples. To improve correct rate, the parameters of WNN were optimized by quantum-behaved particle swarm optimization (QPSO) algorithm. Meanwhile, the effects of neurons number, learning rate factors, iteration times and training times on correct rate were studied and compared with WNN and WNN-PSO algorithms. Results showed the correct rate of WNN-QPSO was increased to 96%. Then, three kinds of dynamic contraction-expansion coefficients were used. Under the optimal dynamic coefficient, the correct rate reached 100%. Moreover, the truncated mean stabilization strategy (TMSS) was coupled to improve the convergent speed. Finally, 10 algorithms were compared. Results demonstrated that photoacoustic spectroscopy combined with WNN-QPSO coupled with TMSS and dynamic contraction-expansion coefficient had an excellent performance in the identification of blood authenticity.

Quantitative photoacoustic tomography by stochastic search: direct recovery of the optical absorption field.

We present, perhaps for the first time, a stochastic search algorithm in quantitative photoacoustic tomography (QPAT) for a one-step recovery of the optical absorption map from time-resolved photoacoustic signals. Such a direct recovery is free of the numerical inaccuracies inherent in conventional two-step approaches that depend on an accurate estimation of the absorbed energy distribution. The absorption profile parameterized as a vector stochastic process is additively updated over time recursions so as to drive the measurement-prediction misfit to a zero-mean white noise. The derivative-free additive update is a welcome departure from the conventional gradient-based methods requiring evaluation of Jacobians at every recursion. The quantitative accuracy of the recovered absorption map from both numerical and experimental data is good with an overall error of less than 10%.

Photoacoustic Analysis of Matrix-Assisted Laser Desorption/Ionization Processes with Pulsed Infrared Lasers

A fast piezoelectric detection system was utilized to record time-resolved photoacoustic signals reflecting the thermal expansion and the rate of material ablation in infrared matrix-assisted laser desorption/ionization (IR-MALDI). Glycerol was employed as a liquid and 2,5-dihydroxybenzoic acid (2,5-DHB) as a crystalline matrix. An Er:YAG laser (λ = 2.94 μm; τL ∼ 100 ns) and a wavelength-tunable optical parametric oscillator (OPO) laser system (λ = 1.4−4.0 μm; τL = 6 ns) were used for desorption/ionization. Material ejection and MALDI ion signals were recorded as a function of laser fluence, pulse duration, and wavelength. For glycerol as matrix, material ejection was found to be temporally confined to about the laser pulse duration when the OPO was employed. For excitation with the Er:YAG laser, a prolonged material ejection with an approximately exponential decay was observed with a characteristic time constant of ∼1 μs. For both lasers, material ejection was observed already at fluences substantially below the MALDI ion threshold. For excitation with the OPO, a correlation between a change in the desorption/ablation mechanism and the onset of ion generation was found. Crystalline 2,5-DHB preparations were investigated with the OPO only. Material ejection from this matrix was found to exhibit a significantly different dependence of the overall ejected material on laser fluence. The article also provides an introduction to the underlying photoacoustic theory adapted to the MALDI process.