The Stress Phase Angle Measurement Using Spectral Domain Optical Coherence Tomography.

The stress phase angle (SPA), defined as the temporal phase angle between circumferential stress (CS) in the arterial wall and wall shear stress (WSS), is utilized to investigate the interactions between CS and WSS. SPA serves as an important parameter for the early diagnosis of cardiovascular disease. In this study, we proposed a novel method for measuring SPA using spectral domain optical coherence tomography (SD-OCT). The multi-M-mode scan strategy is adopted for interference spectrum acquisition. The phases of CS and WSS are extracted from the corresponding structural and flow velocity images of SD-OCT. The method is validated by measuring SPA in the outflow tract (OFT) of chick embryonic hearts and the common carotid artery of mice. To the best of our knowledge, this is the first time that OCT has been used for SPA measurement.

Chitosan-Grafted Carbon Oxynitride Nanoparticles: Investigation of Photocatalytic Degradation and Antibacterial Activity.

In this work, a series of chitosan (CS)-grafted carbon oxynitride (OCN) nanoparticles (denoted as CS-OCN) were successfully synthesized for the first time by thermal polycondensation and subsequent esterification. The structure and photocatalytic performance of CS-OCN nanoparticles were investigated. The XPS spectra of CS-OCN-3 showed the presence of amino bonds. The optimal photocatalytic degradation efficiency of the synthesized CS-OCN-3 could reach 94.3% within 390 min, while the photocurrent response intensity was about 150% more than that of pure OCN. The improved photocatalytic performance may be mainly attributed to the enhanced photogenerated carrier's separation and transportation and stronger visible light response after CS grafting. In addition, the inhibition diameter of CS-OCN-3 reached 23 mm against E. coli within 24 h under visible light irradiation, exhibiting excellent photocatalytic bactericidal ability. The results of bacterial inhibition were supported by absorbance measurements (OD600) studies of E. coli. In a word, this work provided a rational design of an efficient novel metal-free photocatalyst to remove bacterial contamination and accelerate the degradation of organic dyes.

Spectral interference contrast based non-contact photoacoustic microscopy realized by SDOCT.

We introduce a method to extract the photoacoustic (PA) signal from a contrast reduction of the interference spectrum acquired by spectral domain optical coherence tomography (SDOCT). This all-optical detection is achieved in a noncontact manner directly on the water surface covered on the sample by using its specular reflection. During SDOCT exposure, the phase of the interference spectrum keeps shaking according to the water surface vibration induced by PA excitation. This results in an interference contrast reduction which is quantified by a fast Fourier transform (FFT) for PA imaging. A tungsten filament, asparagus fern leaf, and mouse auricle are imaged to demonstrate the method.

Facial Paralysis Detection in Infrared Thermal Images Using Asymmetry Analysis of Temperature and Texture Features.

Facial temperature distribution in healthy people shows contralateral symmetry, which is generally disrupted by facial paralysis. This study aims to develop a quantitative thermal asymmetry analysis method for early diagnosis of facial paralysis in infrared thermal images. First, to improve the reliability of thermal image analysis, the facial regions of interest (ROIs) were segmented using corner and edge detection. A new temperature feature was then defined using the maximum and minimum temperature, and it was combined with the texture feature to represent temperature distribution of facial ROIs. Finally, Minkowski distance was used to measure feature symmetry of bilateral ROIs. The feature symmetry vectors were input into support vector machine to evaluate the degree of facial thermal symmetry. The results showed that there were significant differences in thermal symmetry between patients with facial paralysis and healthy people. The accuracy of the proposed method for early diagnosis of facial paralysis was 0.933, and the area under the ROC curve was 0.947. In conclusion, temperature and texture features can effectively quantify thermal asymmetry caused by facial paralysis, and the application of machine learning in early detection of facial paralysis in thermal images is feasible.

Quantitative assessment of Bell's palsy-related facial thermal asymmetry using infrared thermography: A preliminary study.

The temperature distribution of normal human skin is symmetrical. Facial paralysis generally changes this thermal symmetry. The aim of this study is to analyze facial thermal asymmetry during the early onset of Bell's palsy, and to assess the feasibility of the diagnosis of early-onset Bell's palsy using infrared thermography (IRT). Fifteen subjects with Bell's palsy and 15 healthy volunteers were considered in this study. The infrared thermal images of the front, left, and right sides of all the subjects were collected and analyzed. Each group of facial thermograms was divided into 16 symmetrical regions of interest (ROIs) with respect to the left and right sides. Three different temperature difference calculation methods were used to express the degree of thermal symmetry between the left- and right-side ROIs, namely, the mean temperature difference (ΔTroi), maximum temperature difference (ΔTmax), and minimum temperature difference (ΔTmin). Among the facial ROIs, there were significant differences in the thermal symmetries of the frontal region, medial canthus region, and infraorbital region between subjects with and without Bell's palsy (p < 0.05). Based on the results, ΔTroi was more effective than the other two methods for the diagnosis of early-onset Bell's palsy. The area under the ROC curve (AUC) of ΔTroi in the infraorbital region was 0.818; and the sensitivity and specificity were 0.867 and 0.800, respectively. Subjects with early-onset Bell's palsy exhibited thermal asymmetry on the left and right sides of their faces. The diagnosis of early-onset Bell's palsy using IRT is therefore necessary. Nevertheless, more effective thermal symmetry analysis methods will be investigated further in future research.

Intra- and Interrater Reliability of Infrared Image Analysis of Facial Acupoints in Individuals with Facial Paralysis.

Infrared thermography (IRT), as a noncontact tool for temperature measurement, is widely applied in the study of acupuncture modernization. The aim of this study was to assess the intra- and interrater reliability of infrared image analysis of facial acupoints of subjects with facial paralysis and determine the factors influencing the variability of the measured values. A total of 26 patients with facial paralysis on one side, aged 26 to 53 years, participated voluntarily in the study. Facial infrared thermal images of all participants were analyzed by two trained raters at two different time points at a one-week interval. The intraclass correlation coefficient (ICC) was used to determine the intra- and interrater reliability of IRT measurements. The ICC values varied depending on the analyzed acupoints. The reliability of temperature measurement ranged from moderate to excellent (intrarater, ICC ranged from 0.669 to 0.990; interrater, ICC ranged from 0.661 to 0.987). The reliability of temperature difference measurement ranged from low to excellent (intrarater, ICC ranged from 0.412 to 0.882; interrater, ICC ranged from 0.334 to 0.828). The main influencing factor of reliability is the incomplete consistency in selecting acupoint positions when repeatedly positioning the same acupoint manually. Despite low reliability of temperature difference measurement at some acupoints, some auxiliary measures can be used to reduce the error of manual positioning. Thus, infrared thermal imaging still has the potential to assist in objective and quantitative research on acupuncture.

Optimized depth-resolved estimation to measure optical attenuation coefficients from optical coherence tomography and its application in cerebral damage determination.

The optical attenuation coefficient (OAC) reflects the optical properties of various tissues or tissues of the same type under different physiological conditions. Quantitative measurement of OAC from optical coherence tomography (OCT) signals can provide additional information and can increase the potential for OCT applications. We present an optimized depth-resolved estimation (ODRE) method that derives a precise mapping between the measured OCT signal and the OAC. In contrast to previous depth-resolved estimation (DRE) methods, the optimized method can estimate the OAC in any depth range and ignore whether the light is completely attenuated. Numerical simulations and phantom experiments are used to verify its validity, and this method is applied to detect cerebral damage. In combination with OCT angiography, real-time observation of the change of blood perfusion and the degree of cerebral damage in mice with focal cerebral ischemia provides important information to help us understand the temporal relationship between brain damage and ischemia.

An active learning representative subset selection method using net analyte signal.

To guarantee accurate predictions, representative samples are needed when building a calibration model for spectroscopic measurements. However, in general, it is not known whether a sample is representative prior to measuring its concentration, which is both time-consuming and expensive. In this paper, a method to determine whether a sample should be selected into a calibration set is presented. The selection is based on the difference of Euclidean norm of net analyte signal (NAS) vector between the candidate and existing samples. First, the concentrations and spectra of a group of samples are used to compute the projection matrix, NAS vector, and scalar values. Next, the NAS vectors of candidate samples are computed by multiplying projection matrix with spectra of samples. Scalar value of NAS is obtained by norm computation. The distance between the candidate set and the selected set is computed, and samples with the largest distance are added to selected set sequentially. Last, the concentration of the analyte is measured such that the sample can be used as a calibration sample. Using a validation test, it is shown that the presented method is more efficient than random selection. As a result, the amount of time and money spent on reference measurements is greatly reduced.

Quantification of cerebral vascular perfusion density via optical coherence tomography based on locally adaptive regional growth.

Optical coherence tomography (OCT) angiography is a noninvasive imaging modality that produces volumetric views of blood flow perfusion in vivo with resolution at capillary level, which has been widely adopted to monitor cerebral perfusion status after stroke in experimental settings. Accurate quantification of cerebral perfusion from OCT angiograms is important for understanding the cerebral vascular pathophysiology and assessing the treatment of ischemic stroke. Quantification of blood vessels from OCT angiography faces some problems; one is uneven backscatter (which causes some blood vessels to be very bright, some very dark), and the other is that the brightness in the same blood vessel also changes due to the difference in diameter or depth. In this paper, we proposed a locally adaptive region growing algorithm to solve this problem. The algorithm, which confines the region growing process to a local region, is used to segment blood vessels in different images to cope well with the intensity changes in blood vessels. During segmentation, the initial seed pixels were selected with the aid of the Otsu algorithm, the growth criterion considered both global and local information, and the thresholds were also adjusted adaptively as local regions varied. After these processes are completed, we can calculate the percentage of segmented blood vessels across field of view of the images, named cerebral vascular perfusion density, and use it as an indicator to evaluate the cerebral blood perfusion of middle cerebral artery occlusion in mice. This paper demonstrates that the algorithm can produce satisfactory vascular segmentation results, and CVPD can be used as an effective indicator for evaluating post-ischemic injury.

[Research progress in using functional near-infrared spectroscopy for monitoring depth of anesthesia].

Intra-operation monitoring depth of anesthesia is an important method to insure the quality and safety of clinical anesthesia. As a noninvasive brain function monitoring technology, functional near-infrared spectroscopy can provide objective and reliable brain activity monitoring and imaging in real time. The characteristic of this technique is highly suitable for interrelated research on depth of anesthesia monitoring. The present paper briefly introduced the fundamental and instruments of functional near-infrared spectroscopy, reviewed the current situation about the application of functional near-infrared spectroscopy in research on depth of anesthesia monitoring, pointed out the possible way of using functional near-infrared spectroscopy in depth of anesthesia monitoring research, and expounded the unsolved problems and future prospects.