Limited by the dynamic range of the detector, saturation artifacts usually occur in optical coherence tomography (OCT) imaging for high scattering media. The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images. We proposed a deep learning-based inpainting method of saturation artifacts in this paper. The generation mechanism of saturation artifacts was analyzed, and experimental and simulated datasets were built based on the mechanism. Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs. The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility, strong generalization, and robustness.

Optical molecular tomography (OMT) is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas, which can provide non-invasive quantitative three-dimensional (3D) information regarding tumor distribution in living animals. The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results, resulting in problems such as low accuracy, poor robustness, and long-time consumption. Here, a gates joint locally connected network (GLCN) method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly, thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy. Moreover, gates module was composed of the concatenation and multiplication operators of three different gates. It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates. To evaluate the performance of the proposed method, numerical simulations were conducted, whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.

Photoacoustic imaging (PAI) is a noninvasive emerging imaging method based on the photoacoustic effect, which provides necessary assistance for medical diagnosis. It has the characteristics of large imaging depth and high contrast. However, limited by the equipment cost and reconstruction time requirements, the existing PAI systems distributed with annular array transducers are difficult to take into account both the image quality and the imaging speed. In this paper, a triple-path feature transform network (TFT-Net) for ring-array photoacoustic tomography is proposed to enhance the imaging quality from limited-view and sparse measurement data. Specifically, the network combines the raw photoacoustic pressure signals and conventional linear reconstruction images as input data, and takes the photoacoustic physical model as a prior information to guide the reconstruction process. In addition, to enhance the ability of extracting signal features, the residual block and squeeze and excitation block are introduced into the TFT-Net. For further efficient reconstruction, the final output of photoacoustic signals uses ‘filter-then-upsample’ operation with a pixel-shuffle multiplexer and a max out module. Experiment results on simulated and in-vivo data demonstrate that the constructed TFT-Net can restore the target boundary clearly, reduce background noise, and realize fast and high-quality photoacoustic image reconstruction of limited view with sparse sampling.

The electroencephalogram (EEG) rhythm and functional near-infrared spectroscopy (fNIRS) activation levels have not been compared between a healthy control group (HCG) and methamphetamine user group (MUG) with different addiction histories. This study used 64-electrode EEG and fNIRS to conduct an experiment that analyzed the resting and craving states. The EEG and fNIRS data of 56 participants were collected, including 14 healthy participants, 14 methamphetamine users with an addiction history of 0.5–5 years, 14 users with an addiction history of 5–10 years, and 14 users with an addiction history of 10–15 years. Isolated effective coherence (iCoh) within the brain network was used to process the EEG data. Statistical analysis was performed to compare differences in iCoh among the delta, theta, alpha, beta, and gamma bands and explore oxyhemoglobin activation levels in the ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, orbitofrontal cortex, and frontopolar prefrontal cortex (FPC) of the control group. Finally, the Kmeans, Gaussian mixed model (GMM), linear discriminant analysis (LDA), support vector machine (SVM), Bayes, and convolutional neural networks (CNN) algorithms were used to classify methamphetamine users based on drug and neutral images. A 3-class accuracy was achieved. Changes in EEG and fNIRS activation levels of HCG and MUG with varied addiction histories were demonstrated.

Elastography can be used as a diagnostic method for quantitative characterization of tissue hardness information and thus, differential changes in pathophysiological states of tissues. In this study, we propose a new method for shear wave elastography (SWE) based on laser-excited shear wave, called photoacoustic shear wave elastography (PASWE), which combines photoacoustic (PA) technology with ultrafast ultrasound imaging. By using a focused laser to excite shear waves and ultrafast ultrasonic imaging for detection, high-frequency excitation of shear waves and noncontact elastic imaging can be realized. The laser can stimulate the tissue with the light absorption characteristic to produce the thermal expansion, thus producing the shear wave. The frequency of shear wave induced by laser is higher and the frequency band is wider. By tracking the propagation of shear wave, Young’s modulus of tissue is reconstructed in the whole shear wave propagation region to further evaluate the elastic information of tissue. The feasibility of the method is verified by experiments. Compared with the experimental results of supersonic shear imaging (SSI), it is proved that the method can be used for quantitative elastic imaging of the phantoms. In addition, compared with the SSI method, this method can realize the noncontact excitation of the shear wave, and the frequency of the shear wave excited by the laser is higher than that of the acoustic radiation force (ARF), so the spatial resolution is higher. Compared to the traditional PA elastic imaging method, this method can obtain a larger imaging depth under the premise of ensuring the imaging resolution, and it has potential application value in the clinical diagnosis of diseases requiring noncontact quantitative elasticity.

Visual near-infrared imaging equipment has broad applications in various fields such as venipuncture, facial injections, and safety verification due to its noncontact, compact, and portable design. Currently, most studies utilize near-infrared single-wavelength for image acquisition of veins. However, many substances in the skin, including water, protein, and melanin can create significant background noise, which hinders accurate detection. In this paper, we developed a dual-wavelength imaging system with phase-locked denoising technology to acquire vein image. The signals in the effective region are compared by using the absorption valley and peak of hemoglobin at 700nm and 940nm, respectively. The phase-locked denoising algorithm is applied to decrease the noise and interference of complex surroundings from the images. The imaging results of the vein are successfully extracted in complex noise environment. It is demonstrated that the denoising effect on hand veins imaging can be improved with 57.3% by using our dual-wavelength phase-locked denoising technology. Consequently, this work proposes a novel approach for venous imaging with dual-wavelengths and phase-locked denoising algorithm to extract venous imaging results in complex noisy environment better.

In this paper, we present a distal-scanning common path probe for optical coherence tomography (OCT) equipped with a hollow ultrasonic motor and a simple and specially designed beam-splitter. This novel probe proves to be able to effectively circumvent polarization and dispersion mismatch caused by fiber motion and is more robust to a variety of interfering factors during the imaging process, experimentally compared to a conventional noncommon path probe. Furthermore, our design counteracts the attenuation of backscattering with depth and the fall-off of the signal, resulting in a more balanced signal range and greater imaging depth. Spectral-domain OCT imaging of phantom and biological tissue is also demonstrated with a sensitivity of ～100dB and a lateral resolution of ～3μm. This low-cost probe offers simplified system configuration and excellent robustness, and is therefore particularly suitable for clinical diagnosis as one-off medical apparatus.

Photodynamic therapy (PDT) has been increasingly used in the clinical treatment of neoplastic, inflammatory and infectious skin diseases. However, the generation of reactive oxygen species (ROS) may induce undesired side effects in normal tissue surrounding the treatment lesion, which is a big challenge for the clinical application of PDT. To date, (–)-Epigallocatechin gallate (EGCG) has been widely proposed as an antiangiogenic and antitumor agent for the protection of normal tissue from ROS-mediated oxidative damage. This study evaluates the regulation ability of EGCG for photodynamic damage of blood vessels during hematoporphyrin monomethyl ether (Hemoporfin)-mediated PDT. The quenching rate constants of EGCG for the triplet-state Hemoporfin and photosensitized 1O2 generation are determined to be 6.8×108 M?1S?1 and 1.5×108 M?1S?1, respectively. The vasoconstriction of blood vessels in the protected region treated with EGCG hydrogel after PDT is lower than that of the control region treated with pure hydrogel, suggesting an efficiently reduced photodamage of Hemoporfin for blood vessels treated with EGCG. This study indicates that EGCG is an efficient quencher for triplet-state Hemoporfin and 1O2, and EGCG could be potentially used to reduce the undesired photodamage of normal tissue in clinical PDT.

The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error.