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.

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.

Extensive-dynamic-range and high-resolution surface profiling with phase-sensitive spectral-domain white-light interferometry.

We demonstrate a Fourier-transform-based method for extensive-dynamic-range and high-resolution surface profiling using phase-sensitive spectral-domain white-light interferometry. By combining the frequency and phase of interference fringes, this method is capable of displacement measurement with nanometer-scale resolution and a dynamic range up to several millimeters. The performance of the method is demonstrated by surface profiling of a coin, gauge blocks, and a cell-phone circuit board.

Common-path-based device for magnetomotive OCT noise reduction.

Magnetomotive optical coherence tomography (MMOCT) is a promising imaging method for noninvasive three-dimensional tracking of magnetic nanoparticle (MNP) motions in target tissues or organs. The external B-field is the driving force that provides MMOCT contrast. However, B-field modulation also introduces modulation noise, thereby decreasing the quality of the MMOCT image. In this paper, a common-path-based device is designed for modulation noise reduction. The device is capable of adjusting interference distance, reference light intensity, and imaging position (X-Y translation). The sensitivity of the MMOCT is increased by ∼20 times with the new device. Using the proposed device, the distribution of MNPs injected in zebrafish was imaged.

Quantitative phase imaging using spectral domain phase microscopy without phase wrapping ambiguity.

We demonstrate a method for quantitative phase (QP) imaging without 2π ambiguity using a spectral domain phase microscopy system. The method is capable of QP measurement of a large dynamic range with a high sensitivity. We determine an integer multiple of 2π to correct wrapped phases by calculating the phase shift difference between the detected interference fringe and simulated fringes. The presented method is quantitatively verified by measuring the vibration generated by a piezo linear stage and mapping the surface topography of a slanted mirror. QP imaging of red blood cells is also performed to demonstrate the method's capacity and application in biological imaging.

Dynamic Plasmon Resonance Tuning for Surface Enhanced Sensing.

We report on fabricating plasmonic nanorod crystals using focused ion beam lithography. We first demonstrate manipulating the profiles of nanorods perpendicularly aligned with the substrate. Then we show accurate control of nanorod outlines can be achieved. We also show that it is feasible to manufacture nanorods obliquely aligned with the substrate. Tunable plasmon resonance can be realized with different tilting angles and geometries. Our approach may find important applications in plasmon-assisted sensing and surface enhanced spectroscopy.

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.

Mapping transverse velocity of particles in capillary vessels by time-varying laser speckle through perturbation analyses.

We propose a cross-correlation method to map the transverse velocities of particles moving in capillary vessels using full-field time-varying laser speckle technique. The mapping is achieved by a semi-random perturbation model that describes the intensity fluctuation of time-varying laser speckle signals. When passing through probing volume, moving particles encode a random perturbation into the observed laser speckle pattern. We calculate the transverse flow velocity by cross-correlating the temporal envelopes of the perturbation signals. The proposed method is experimentally verified by the use of polymer microsphere suspension in a glass capillary.

In vivo assessment of wall strain in embryonic chick heart by spectral domain optical coherence tomography.

The ability to measure in vivo wall strain in embryonic hearts is important for fully understanding the mechanisms of cardiac development. Optical coherence tomography (OCT) is a powerful tool for the three-dimensional imaging of complex myocardial activities in early-stage embryonic hearts with high spatial and temporal resolutions. We describe a method to analyze periodic deformations of myocardial walls and evaluate in vivo myocardial wall strains with a high-speed spectral domain OCT system. We perform four-dimensional scanning on the outflow tract (OFT) of chick embryonic hearts and determine a special cross-section in which the OFT can be approximated as an annulus by analyzing Doppler blood-flow velocities. For each image acquired at the special cross-section, the annular myocardial wall is segmented with a semiautomatic boundary-detection algorithm, and the fluctuation myocardial wall thickness is calculated from the area and mean circumference of the myocardial wall. The experimental results shown with the embryonic chick hearts demonstrate that the proposed method is a useful tool for studying the biomechanical characteristics of embryonic hearts.

Probe fusion all-optic OCT-PAM dual-mode imaging system for biomedical imaging.

We proposed a non-contact photoacoustic (PA) detection method using spectral domain optical coherence tomography (SDOCT). Two interference spectrums (A-lines) were acquired before and after the PA excitation with SDOCT. PA signal propagated within the sample causing the vibration. The vibration inner the sample introduced phase change between the acquired two A-lines. Thus, the PA signal can be detected by evaluating the difference in phase between the two A-lines. Based on the method, an OCT-PAM dual-mode imaging system was constructed. In the system, SDOCT served as the detection unit for PAM. Thus, the combination of the two imaging modalities was simplified. Another advantage of the system is that it realizes non-contact all-optic detection, which is attractive for biomedical imaging. Using the system, we imaged phantoms of carbon fibers, asparagus leaves and human hairs. Furthermore, the cortical vasculature of rat was imaged in vivo and the flow status was evaluated quantitatively.

Raman spectral pattern recognition of breast cancer: A machine learning strategy based on feature fusion and adaptive hyperparameter optimization.

Raman spectroscopy, as a kind of molecular vibration spectroscopy, provides abundant information for measuring components and molecular structure in the early detection and diagnosis of breast cancer. Currently, portable Raman spectrometers have simplified and made equipment application more affordable, albeit at the cost of sacrificing the signal-to-noise ratio (SNR). Consequently, this necessitates a higher recognition rate from pattern recognition algorithms. Our study employs a feature fusion strategy to reduce the dimensionality of high-dimensional Raman spectra and enhance the discriminative information between normal tissues and tumors. In the conducted random experiment, the classifier achieved a performance of over 96% for all three average metrics: accuracy, sensitivity, and specificity. Additionally, we propose a multi-parameter serial encoding evolutionary algorithm (MSEA) and integrate it into the Adaptive Local Hyperplane K-nearest Neighbor classification algorithm (ALHK) for adaptive hyperparameter optimization. The implementation of serial encoding tackles the predicament of parallel optimization in multi-hyperparameter vector problems. To bolster the convergence of the optimization algorithm towards a global optimal solution, an exponential viability function is devised for nonlinear processing. Moreover, an improved elitist strategy is employed for individual selection, effectively eliminating the influence of probability factors on the robustness of the optimization algorithm. This study further optimizes the hyperparameter space through sensitivity analysis of hyperparameters and cross-validation experiments, leading to superior performance compared to the ALHK algorithm with manual hyperparameter configuration.

High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm.

Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human blood using Raman spectroscopy. This paper researches a novel integrated machine learning algorithm called Bagging-ABC-ELM. The optimal input weights and biases of extreme learning machine (ELM) model are obtained by artificial bee colony (ABC) algorithm. The bagging algorithm is used to obtain a better the stability of the model and higher performance than ELM algorithm. The results show that the mean value of coefficient of determination is 0.9928, and root mean square error is 0.1928. Compared to other regression models, the Bagging-ABC-ELM model exhibited superior prediction accuracy, robustness, and generalization capability. The Bagging-ABC-ELM model presents a promising alternative for analyzing blood glucose levels in clinical and research settings.

Study of Foveal Avascular Zone Growth in Individuals With Mild Diabetic Retinopathy by Optical Coherence Tomography.

Purpose: The purpose of this study was to investigate the association between foveal vessels and retinal thickness in individuals with diabetic retinopathy (DR) and control subjects, and to reveal foveal avascular zone (FAZ) growth in early individuals with DR. Methods: The regions with a thickness less than 60 µm were marked from the intima thickness maps and named FAZThic. The avascular zones extracted from the deep vascular plexus were designated as FAZAngi. The boundary of the two FAZ forms a ring region, which we called FAZRing. The FAZ growth rate was defined as the ratio of the FAZRing area to the FAZThic area. Thirty healthy controls and 30 individuals with mild nonproliferative DR were recruited for this study. Results: The FAZThic area in individuals with mild DR and control subjects showed similar distribution. The FAZAngi area in individuals with mild DR are higher than that in control subjects on the whole, but there was no significant difference (P > 0.05). The FAZRing area in individuals with mild DR was significantly higher than that in control subjects (P < 0.001). However, there is still a small amount of overlap data between the two groups. For the FAZ growth rate, the individuals with mild DR were also significantly larger than the control subjects (P < 0.001). But there were no overlapping data between the two groups. Conclusions: The growth of FAZ in individuals with mild DR can be inferred by comparing FAZAngi with FAZThic. This method minimizes the impact of individual variations and helps researchers to understand the progression mechanism of DR more deeply.

In vivo characterization of cerebrovascular impairment induced by amyloid ß peptide overload in glymphatic clearance system using swept-source optical coherence tomography.

Significance: Antiamyloid ß ( A ß ) immunotherapy is a promising therapeutic strategy for Alzheimer's disease (AD) but generates large amounts of soluble A ß peptides that could overwhelm the clearance pathway, leading to serious side effects. Direct implications of A ß in glymphatic drainage transport for cerebral vasculature and tissue are not well known. Studies are needed to resolve this issue and pave the way to better monitoring abnormal vascular events that may occur in A ß -modifying therapies for AD. Aim: The objective is to characterize the modification of cerebral vasculature and tissue induced by soluble A ß abundantly present in the glymphatic clearance system. Approach: A ß 1 - 42 peptide was injected intracerebroventricularly and swept-source optical coherence tomography (SS-OCT) was used to monitor the progression of changes in the brain microvascular network and tissue in vivo over 14 days. Parameters reflecting vascular morphology and structure as well as tissue status were quantified and compared before treatment. Results: Vascular perfusion density, vessel length, and branch density decreased sharply and persistently following peptide administration. In comparison, vascular average diameter and vascular tortuosity were moderately increased at the late stage of monitoring. Endpoint density gradually increased, and the global optical attenuation coefficient value decreased significantly over time. Conclusions: A ß burden in the glymphatic system directly contributes to cerebrovascular structural and morphological abnormalities and global brain tissue damage, suggesting severe deleterious properties of soluble cerebrospinal fluid- A ß . We also show that OCT can be used as an effective tool to monitor cerebrovascular dynamics and tissue property changes in response to therapeutic treatments in drug discovery research.

A shallow convolutional neural network with elastic nets for blood glucose quantitative analysis using Raman spectroscopy.

In this paper, a one-dimensional shallow convolutional neural network structure combined with elastic nets (1D-SCNN-EN) was firstly proposed to predict the glucose concentration of blood by Raman spectroscopy. A total of 106 different blood glucose spectra were obtained by Fourier transform (FT) Raman spectroscopy. The one-dimensional shallow convolutional neural network, with elastic nets added to the full connected layer, was presented to capture multiple deep features and reduce the complexity of the model. The 1D-SCNN-EN model has a better performance than conventional approaches (partial least squares and support vector machine). The root mean squared error of calibration (RMSEC), the root mean squared error of prediction (RMSEP), the determination coefficient of prediction (RP2), and the residual predictive deviation of prediction (RPD) were 0.10262, 0.11210, 0.99403, and 12.94601, respectively. The experiment results showed that the 1D-SCNN-EN model has a higher prediction accuracy and stronger robustness than the other regression models. The overall studies indicated that the 1D-SCNN-EN model looked promising for predict the glucose concentration of blood by Raman spectroscopy when the sample size is small.

Quantitative analysis of Raman spectra for glucose concentration in human blood using Gramian angular field and convolutional neural network.

In this study, convolutional neural network based on Gramian angular field (GAF-CNN) was firstly proposed. The 1-D Raman spectral data was converted into images and used for predicting the biochemical value of blood glucose. 106 sets of blood spectrums were acquired by Fourier transform (FT) Raman spectroscopy. Spectral data ranging from 800 cm-1 to 1800 cm-1 were selected for quantitative analysis of the blood glucose. Data augmentation was used to train neural networks and normalize the Raman spectra. And, we applied principal component analysis (PCA) for dimension reduction and information extraction. The root mean squared error of prediction (RMSEP) are 0.06570 (GADF) and 0.06774 (GASF), the determination coefficient of prediction (R2) are 0.99929 (GADF) and 0.99925 (GASF), and the residual predictive deviation of prediction (RPD) are 37.56324 (GADF) and 36.43362 (GASF). GAF-CNN model performed better for predicting of glucose concentration. The GAF-CNN model can be used to establish a calibration model to predict blood glucose concentration.

MoS2 nanoflower-mediated enhanced intratumoral penetration and piezoelectric catalytic therapy.

The absence of lymphatic vessels in tumors leads to the retention of interstitial fluid, and the formation of an inverse pressure difference between the tumor and blood vessels hinders drug delivery deep into the tumor, which leads to tumor recurrence and metastasis. Therefore, we designed a novel strategy to downregulate tumor interstitial fluid pressure (TIFP) by water splitting in the tumor interstitium based on piezoelectric catalysis nanomedicine. First, the chemotherapeutic drug doxorubicin (DOX) was loaded on the piezoelectric catalytic material MoS2 and then encapsulated with tumor cell membrane (CM) to obtain MD@C. MD@C could not only target the tumor through homologous targeting but, more importantly, also triggered piezoelectric catalytic water splitting under ultrasound (US) stimulation; as a result, the TIFPs of U14 and PAN02 tumor-bearing mice were reduced to 57.14% and 45.5%, respectively, and the tumor inhibition rates of MD@C were 96.75% and 99.21%, which increased the perfusion of blood-derived drugs in the tumors. Moreover, the hydroxyl radicals generated by piezoelectric catalysis could effectively inhibit the growth of tumors in combination with DOX. Consequently, the piezoelectric catalytic water splitting strategy of MD@C can enhance drug delivery, providing a new universal platform for the treatment of solid malignant tumors.

Automated retinal boundary segmentation of optical coherence tomography images using an improved Canny operator.

Retinal segmentation is a prerequisite for quantifying retinal structural features and diagnosing related ophthalmic diseases. Canny operator is recognized as the best boundary detection operator so far, and is often used to obtain the initial boundary of the retina in retinal segmentation. However, the traditional Canny operator is susceptible to vascular shadows, vitreous artifacts, or noise interference in retinal segmentation, causing serious misdetection or missed detection. This paper proposed an improved Canny operator for automatic segmentation of retinal boundaries. The improved algorithm solves the problems of the traditional Canny operator by adding a multi-point boundary search step on the basis of the original method, and adjusts the convolution kernel. The algorithm was used to segment the retinal images of healthy subjects and age-related macular degeneration (AMD) patients; eleven retinal boundaries were identified and compared with the results of manual segmentation by the ophthalmologists. The average difference between the automatic and manual methods is: 2-6 microns (1-2 pixels) for healthy subjects and 3-10 microns (1-3 pixels) for AMD patients. Qualitative method is also used to verify the accuracy and stability of the algorithm. The percentage of "perfect segmentation" and "good segmentation" is 98% in healthy subjects and 94% in AMD patients. This algorithm can be used alone or in combination with other methods as an initial boundary detection algorithm. It is easy to understand and improve, and may become a useful tool for analyzing and diagnosing eye diseases.

OCT based four-dimensional cardiac imaging of a living chick embryo using an impedance signal as a gating for post-acquisition synchronization.

Optical coherence tomography (OCT) is a non-invasive imaging modality with high spatial resolution suitable for early embryonic heart imaging. However, the most commonly used OCT systems cannot provide direct 4-D imaging due to acquisition speed limitations. We proposed a retrospective gating 4-D reconstruction method based on spectral domain OCT. A special circuit was designed to measure the impedance change of chick embryos in response to the heart beating. The impedance signal was acquired simultaneously with the OCT B-scan image sequence at several different locations along the heart. The impedance signal was used as a gating for 4-D reconstruction. The reconstruction algorithm includes cardiac period calculation, interpolation from multi-cardiac cycle image sequence into one cardiac cycle, and cardiac phase synchronization among the different locations of the heart. The synchronism of the impedance signal change with the heartbeat was verified. Using the proposed method, we reconstructed the cardiac outflow tract (OFT) of chick embryos at an early stage of development (Hamburger-Hamilton stage 18). We showed that the reconstructed 4-D images correctly captured the dynamics of the OFT wall motion.

Simultaneous detection of glucose, triglycerides, and total cholesterol in whole blood by Fourier-Transform Raman spectroscopy.

In this paper, a reagent-free simultaneous and direct detection method of three analytes in human blood based on Fourier-transform Raman (FT-Raman) spectroscopy with 1064 nm laser radiation was proposed for the first time. A total of 161 human blood samples were characterized by FT-Raman spectroscopy under the excitation laser source of 1064 nm. In order to achieve a robust regression model, the Nonlinear Iterative Partial Least Squares (NIPALS) with orthogonal signal correction (OSC) algorithm and sample set partition based on a joint x-y distance (SPXY) is used to establish multivariate calibration models. The root means square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), correlation coefficients (R2) and ratio of performance to deviation (RPD) were 0.34255 mg/dL, 0.3662 mg/dL, 0.99982 and 56.3524 for glucose, 0.33656 mg/dL, 0.75736 mg/dL, 0.99967 and 34.9169 for total cholesterol (TC), and 0.29956 mg/dL, 0.27469 mg/dL, 0.99998 and 173.5098 for triglycerides (TG), respectively. The analysis results showed that the proposed method could be able to accurately predict the concentration of glucose, TC and TG in blood. This method can instantaneous multi-component detection on whole blood.