This PDF file contains the front matter associated with SPIE Proceedings Volume 12461, including the Title Page, Copyright information, Table of Contents, and Committee list.

In this work, to study the effects of multiple factors on the blood glucose photoacoustic detection, five different factors including the laser energy, concentration, temperature, flow velocity and detection distance were considered, and a set of blood glucose photoacoustic detection system combined multiple influence factors was established. The time-resolved photoacoustic signals and peak-to-peak spectra of 625 groups of blood samples were obtained. To predict the blood glucose concentration with high accuracy under the influence of multiple factors, back propagation (BP) neural network was used to train five different factors and photoacoustic peak-to-peak values of 500 groups of blood samples, and 125 groups of blood samples were used as the test samples. Meanwhile, the effects of neurons number in the hidden layer, learning rate and training times on the root-mean-square error(RMSE) of predicting blood glucose concentration were investigated. Under the optimal parameters, the RMSE of blood glucose concentration for 125 groups of test blood samples is about 0.807679mmol/L. Compared with the results of partial least square (PLS) algorithm with RMSE of 1.78mmol/L, it is demonstrated that the BP algorithm has good performance in the prediction blood glucose concentration under multiple influence factors based on photoacoustic detection technology.

Multi-spectral optoacoustic tomography (MSOT) combines rich contrast of optical imaging and high resolution of ultrasound, and becomes an attractive biomedical research tool in the last decade. Aligning MSOT images with anatomical map provided by magnetic resonance imaging (MRI) can potentially enhance the interpretation of optoacoustic signal which mainly reflects molecular and functional information. Therefore, developing an automated algorithm of image registration between MSOT and MRI is crucial. Existing MSOT-MRI registration algorithms mostly relied on manual segmentation, which requires user-dependent experience. Herein, we developed a fully automated algorithm for MSOT-MRI registration based on deep learning (DL). This workflow consists of DL-based segmentation and image transformation. We have experimentally demonstrated the accuracy and computational efficiency of the method, paving the way towards high-throughput MSOT data analysis in close future.

Prostate cancer (PCa) is an epithelial malignant tumor occurring in the prostate gland and is the most common malignancy of the male genitourinary system. Prostate cancer is often asymptomatic in its early stage, and the best treatment time is usually missed when it is found. Therefore, early diagnosis and treatment is the key to reduce the mortality of prostate cancer patients. In this work, we developed a method to identify patients with prostate cancer and healthy volunteers. We collected serum samples from patients with prostate cancer and healthy volunteers, detected the SERS spectra of these serum samples, and analyzed the SERS spectrum of the obtained serum samples with a preliminary spectral peak assignment. Then principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose the SERS spectra of serum from patients with prostate cancer and healthy volunteers. Difference spectrum analysis showed that there are obvious differences in several characteristic peaks between the serum of patients with prostate cancer and the serum of healthy volunteers, which may be related to the special changes of nucleic acids, proteins, lipids and other biological molecules in the process of carcinogenesis. Using principal component analysis (PCA) combined with linear discriminant analysis (LDA) multivariate statistical method, the diagnostic sensitivity and specificity were 85% and 95%, respectively. The receiver operating characteristic (ROC) curve further proves the effectiveness of diagnosis algorithm based on PCA-LDA technology, and the area under the curve (AUC) is 0.998. These results show that the combination of SERS technology and PCA-LDA algorithm has great potential in screening prostate cancer patients.

The Raman enhancement effect of Ag-based nanomaterial hydroxyapatite was studied using Rhodamine 6G and adenine. The composite is a nano-rod hydroxyapatite wrapped with Ag particles, which acts as a substrate for surface-enhanced Raman scattering of the material to be tested. The results show that the substrate has a significant enhancement effect for the Raman spectra of Rhodamine 6G and adenine. Compared with the traditional silver colloids substrate, this is an interesting substrate. Nano-level hydroxyapatite is expected to carry drugs into the living body, and then on the basis of silver coating, in vivo SERS detection becomes possible.

The angle between the LED light therapy instrument and the skin will have an impact on the light penetration effect, which interferes with the development of the light treatment dose, resulting in the final light treatment effect does not achieve the desired goal. To address this problem, this paper proposes a new design scheme using a COB packaged LED light source with a 10mm diameter light emitting surface, using a free-form lens with a Fresnel lens for light leveling and collimation. The initial design of the free-form lens is based on the edge light theory, Snell's law and energy mapping method. The distance between the light source and the target surface is set to 200 mm, the diameter of the target surface is 400 mm, and finally the Fresnel lens is chosen to collimate the treatment light emitted from the LED light source. Simulation results show that a treatment spot with a diameter of 220 mm and a uniformity of more than 85% can be produced within the range of 50 to 150 mm after this optical path system, and the divergence angle of the spot is controlled within 10°. This design is able to produce a large size therapeutic spot in the near field with both uniformity and collimation, which makes up for the shortcomings of the traditional LED phototherapy instrument design and provides an important reference for the design of LED phototherapy instruments.

Photoacoustic imaging is an emerging biomedical imaging method based on photoacoustic effect. Firstly, a short pulse laser is used to illuminate the object which absorbs part of the light pulse energy and expands adiabatically. Then the initial sound pressure is generated and radiates outward in the form of pressure waves, which is the ultrasonic wave, also called photoacoustic signal. By placing an ultrasonic transducer in advance, the photoacoustic signal can be detected, which enables the optical absorption distribution to be reconstructed with a reconstruction algorithm. However, photoacoustic signal can be easily disturbed by noise in the process of propagation, leading to a defective reconstruction effect of photoacoustic image. Therefore, it is necessary to digitize the photoacoustic signal and explore its spectrum characteristics. This article constructs a visual photoacoustic signal digital processing platform based on GUI. The platform includes three modules: the module of photoacoustic signal generation, the signal analysis module, and the digital filter module. The module of photoacoustic signal generation obtains photoacoustic signals of time series based on k-Wave. The signal analysis module can achieve the analysis of photoacoustic signal both in time domain and frequency domain to determine its characteristics. The digital filter module includes FIR and IIR digital filters with variable boundary frequency and attenuation constant, upon which the filtered loss function diagram and the waveform diagram of signal in time domain depend. The visual photoacoustic signal digital processing platform has the advantages of simple operation and complete functions, which will contribute to the research of photoacoustic imaging.