We present a systematical study on comparison between water and dry coupling in photoacoustic tomography of the human finger joints. Compared to the direct water immersion of the finger for water coupling, the dry coupling is realized through a transparent PDMS film-based water bag, which ensures water-free contact with the skin. The results obtained suggest that the dry coupling provides image quality comparable to that by water coupling while eliminating the wrinkling of the finger joint caused by the water immersion. In addition, the dry coupling offers more stable hemodynamic images than the water coupling as the water immersion of the finger joint causes reduction in blood vessel size.

We constructed a flexible gold-polydimethylsiloxane (gold-PDMS) nanocomposites film with controllable thickness and light transmittance, to realize optically-excited simultaneous photoacoustic (PA) and ultrasound (US) imaging under a single laser pulse irradiation. Benefiting from the excellent thermoelastic properties, the gold-PDMS film absorbs part of the incident laser energy and produces a high-intensity US, which is used to realize US imaging. Meanwhile, the partly transmitted light is used to excite samples for PA imaging. By controlling the thickness of the gold-PDMS, we can control the center frequency in the US imaging. We experimentally analyzed the frequency of the produced US signal by the gold-PDMS film and compared it with the finite element analysis (FEA) method, where the experiments agree with the FEA results. This method is demonstrated by the experiments on phantoms and a mouse model. Our work provides a cost-effective methodology for simultaneous PA and US imaging.

Prevention is the most effective way to reduce dental caries. In order to provide a simple way to achieve oral healthcare direction in daily life, dual Channel, portable dental Imaging system that combine white light with autofluorescence techniques was established, and then, a group of volunteers were recruited, 7200 tooth pictures of different dental caries stage and dental plaque were taken and collected. In this work, a customized Convolutional Neural Networks (CNNs) have been designed to classify dental image with early stage caries and dental plaque. Eighty percentage (n=6000) of the pictures taken were used to supervised training of the CNNs based on the experienced dentists' advice and the rest 20% (n = 1200) were used to a test dataset to test the trained CNNs. The accuracy, sensitivity and specificity were calculated to evaluate performance of the CNNs. The accuracy for the early stage caries and dental plaque were 95.3% and 95.9%, respectively. These results shown that the designed image system combined the customized CNNs that could automatically and e±ciently find early caries and dental plaque on occlusal, lingual and buccal surfaces. Therefore, this will provide a novel approach to dental caries prevention for everyone in daily life.

Near infrared (NIR) spectroscopy is now widely used in fluidized bed granulation. However, there are still some demerits that should be overcome in practice. Valid spectra selection during modeling process is now a hard nut to crack. In this study, a novel NIR sensor and a cosine distance method were introduced to solve this problem in order to make the fluidized process into "visualization". A NIR sensor was fixed on the side of the expansion chamber to acquire the NIR spectra. Then valid spectra were selected based on a cosine distance method to reduce the influence of dynamic disturbances. Finally, spectral pretreatment and wavelength selection methods were investigated to establish partial least squares (PLS) models to monitor the moisture content. The results showed that the root mean square error of prediction (RMSEP) was 0.124% for moisture content model, which was much lower than that without valid spectra selection treatment. All results demonstrated that with the help of valid spectra selection treatment, NIR sensor could be used for real-time determination of critical quality attributes (CQAs) more accurately. It makes the manufacturing easier to understand than the process parameter control.

Near infrared (NIR) spectrum analysis technology has outstanding advantages such as rapid, nondestructive, pollution-free, and is widely used in food, pharmaceutical, petrochemical, agricultural products production and testing industries. Convolutional neural network (CNN) is one of the most successful methods in big data analysis because of its powerful feature extraction and abstraction ability, and it is especially suitable for solving multi-classification problems. CNN-based transfer learning is a machine learning technique, which migrates parameters of trained model to the new one to improve the performance. The transfer learning strategy can speed up the learning e±ciency of the model instead of learning from scratch. In view of the di±culty in acquisition of drug NIR spectral data and high labeling cost, this paper proposes three simple but very effective transfer learning methods for multi-manufacturer identification of drugs based on one-dimensional CNN. Compared with the original CNN, the transfer learning method can achieve better classification performance with fewer NIR spectral data, which greatly reduces the dependence on labeled NIR spectral data. At the same time, this paper also compares and discusses three different transfer learning methods, and selects the most suitable transfer learning model for drug NIR spectral data analysis. Compared with the current popular methods, such as SVM, BP, AE and ELM, the proposed method achieves higher classification accuracy and scalability in multi-variety and multi-manufacturer NIR spectrum classification experiments.

Graphene derivatives, possessing strong Raman scattering and near-infrared absorption intrinsically, have boosted many exciting biosensing applications. The tunability of the absorption characteristics, however, remains largely unexplored to date. Here, we proposed a multilayer configuration constructed by a graphene monolayer sandwiched between a buffer layer and onedimensional photonic crystal (1DPC) to achieve tunable graphene absorption under total internal reflection (TIR). It is interesting that the unique optical properties of the buffer-graphene-1DPC multilayer structure, the electromagnetically induced transparency (EIT)-like and Fanolike absorptions, can be achieved with pre-determined resonance wavelengths, and furtherly be tuned by adjusting either the structure parameters or the incident angle of light. Theoretical analyses demonstrate that such EIT- and Fano-like absorptions are due to the interference of light in the multilayer structure and the complete transmission produced by the evanescent wave resonance in the configuration. The enhanced absorptions and the huge electrical field enhancement effect exhibit potentials for broad applications, such as photoacoustic imaging and Raman imaging.

The majority of existing high-power laser therapeutic instruments employ a single wavelength for a single target; thus, they do not meet the requirements for clinical treatment. Therefore, this study designs an optical system for a dual-wavelength high-power laser therapeutic device with a variable spot size. The waist of the short arm of the optical cavity and the G1G2 parameter (G-parameter equivalent cavity method) is calculated using MATLAB software, the spot size and divergence angle on the lens are calculated using an ABCD matrix, and the distance between the treatment spot at different spot sizes and the transformation lens is calculated in order to design the treatment handpiece. Experiments are conducted to analyze the stability at an output power of 532 nm before beam combination and the power loss after beam combination. The results show that the output power stability of the 532-nm beam varies by less than 2% over 150 min, and the loss of both wavelengths is less than 20%, which meets the clinical requirements of the system. The safety performance can meet the requirements of national general standards for medical electrical safety. The proposed dual-wavelength laser therapy instrument has both visible wave and near-infrared wave characteristics; thus, it can accurately target both superficial vessels and vessels with a larger diameter and deeper position. This therapeutic device has the advantages of simple operation, stable and reliable laser output, high security and strong anti-interference ability, and meets the comprehensive clinical treatment demands of vascular diseases.

Optical-resolution photoacoustic microscopy (OR-PAM) has been shown to be an excellent tool for high-resolution imaging of microvasculature, and quantitative analysis of the microvasculature can provide valuable information for the early diagnosis and treatment of various vascularrelated diseases. In order to address the characteristics of weak signals, discontinuity and small diameters in photoacoustic microvascular images, we propose a method adaptive to the microvascular segmentation in photoacoustic images, including Hessian matrix enhancement and the morphological connection operators. The accuracy of our vascular segmentation method is quantitatively evaluated by the multiple criteria. To obtain more precise and continuous microvascular skeletons, an improved skeleton extraction framework based on the multistencil fast marching (MSFM) method is developed. We carried out in vivo OR-PAM microvascular imaging in mouse ears and subcutaneous hepatoma tumor model to verify the correctness and superiority of our proposed method. Compared with the previous methods, our proposed method can extract the microvascular network more completely, continuously and accurately, and provide an effective solution for the quantitative analysis of photoacoustic microvascular images with many small branches.

Intravenous cannulation is the most important phase in medical practices. Currently, limited literature is available about visibility of veins and the characteristics of patients associated with di±cult intravenous access. In modern medical treatment, a major challenge is locating veins for patients who have di±cult venous access. Presently, some products of vein locators are available in the market to improve vein access, but they need auxiliary equipment such as near infrared (NIR) illumination and camera, which add weight and cost to the devices, and cause inconveniences to daily medical care. In this paper, a vein visualization algorithm based on the deep learning method was proposed. Based on a group of synchronous RGB/NIR arm images, a convolutional neural network (CNN) model was designed to implement the mapping from RGB to NIR images, where veins can be detected fromskin. The model has a simple structure and less optimization parameters. A color transfer scheme was also proposed to make the network adaptive to the images taken by smartphone in daily medical treatments. Comprehensive experiments were conducted on three datasets to evaluate the proposed method. Subjective and objective evaluations showed the effectiveness of the proposed method. These results indicated that the deep learning-based method can be used for visualizing veins in medical care applications.